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The Washington State Convention and Trade Center, built in 2001, has established itself as the premiere convention and trade center facility in the Pacific Northwest. With the Emerald City ComiCon being the premiere comic book and pop culture convention in the Pacific Northwest, it was a perfect match.
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- Research
- Open Access
Selective control of gait subtasks in robotic gait training: foot clearance support in stroke survivors with a powered exoskeleton
- Bram Koopman†1Email author,
- Edwin HF van Asseldonk†1 and
- Herman van der Kooij1, 2
© Koopman et al.; licensee BioMed Central Ltd. 2013
- Received: 15 February 2012
- Accepted: 7 January 2013
- Published: 21 January 2013
Robot-aided gait training is an emerging clinical tool for gait rehabilitation of neurological patients. This paper deals with a novel method of offering gait assistance, using an impedance controlled exoskeleton (LOPES). The provided assistance is based on a recent finding that, in the control of walking, different modules can be discerned that are associated with different subtasks. In this study, a Virtual Model Controller (VMC) for supporting one of these subtasks, namely the foot clearance, is presented and evaluated.
The developed VMC provides virtual support at the ankle, to increase foot clearance. Therefore, we first developed a new method to derive reference trajectories of the ankle position. These trajectories consist of splines between key events, which are dependent on walking speed and body height. Subsequently, the VMC was evaluated in twelve healthy subjects and six chronic stroke survivors. The impedance levels, of the support, were altered between trials to investigate whether the controller allowed gradual and selective support. Additionally, an adaptive algorithm was tested, that automatically shaped the amount of support to the subjects' needs. Catch trials were introduced to determine whether the subjects tended to rely on the support. We also assessed the additional value of providing visual feedback.
With the VMC, the step height could be selectively and gradually influenced. The adaptive algorithm clearly shaped the support level to the specific needs of every stroke survivor. The provided support did not result in reliance on the support for both groups. All healthy subjects and most patients were able to utilize the visual feedback to increase their active participation.
The presented approach can provide selective control on one of the essential subtasks of walking. This module is the first in a set of modules to control all subtasks. This enables the therapist to focus the support on the subtasks that are impaired, and leave the other subtasks up to the patient, encouraging him to participate more actively in the training. Additionally, the speed-dependent reference patterns provide the therapist with the tools to easily adapt the treadmill speed to the capabilities and progress of the patient.
- Robotic gait rehabilitation
- Stroke
- Reference trajectories
- Virtual model control
- Support of subtasks
- Adaptive control
- Impedance control
- Reliance
- Compensatory strategies
- Visual feedback
Many patients with neurological injuries, like stroke or spinal cord injury (SCI), suffer from muscle weakness, loss of independent joint control, and spasticity, often resulting in gait disorders. To regain functional mobility, these patients require task-oriented, high-intensity, and repetitive training[1–3]. Robotic gait-training devices are increasingly being used to provide this kind of training. They can provide highly repetitive, more frequent, and intensive training sessions, while reducing the workload of the therapist, compared to more conventional forms of manual-assisted (and body-weight-supported) gait training. Additionally, the assessment of the progress of the patient becomes more objective with the integration of different sensory systems, which can record interaction forces and gait kinematics[4].
Despite the reduction in labor intensity, the therapeutic effect of the different types of gait trainers is inconsistent. Pohl, et al., and Mayr, et al., reported a significant improvement in gait ability in subacute stroke patients, compared to conventional physiotherapy[5, 6]. Other studies found no significant difference between robotic support and manual treadmill training[7, 8], or conventional physiotherapy[9], although robotic gait training did show improvements in gait symmetry[7, 9]. Some results even indicate that manual treadmill training is superior to robotic assistance[10]. Recently, a large multicenter randomized clinical trial suggested that the diversity of conventional gait training elicits greater improvements in functional recovery than robotic-assisted gait training[11]. These contradicting results emphasize that robot-aided training needs to be further optimized to increase therapeutic outcome.
One of the most important factors that promotes therapeutic outcome is active participation. Active patient participation has been proven to be beneficial for motor learning in general[12–14] and is suggested to be important for rehabilitation of gait disorders[15]. The "first-generation" devices, like the Lokomat (Hocoma AG, Switzerland) or AutoAmbulator (HealthSouth, USA), were initially developed based on the approach of enforcing gait upon a patient by moving the legs through a prescribed gait pattern. This diminishes the need for the patients to actively contribute to the required motion. Moving the legs in a rigid fashion is known to reduce[16] and affect[17] voluntary muscle activity compared to manual assistance, possibly making the patient reliant on the support. Rigid trajectory control also limits the natural gait variability and the possibility to make small movement errors. These small errors have been suggested to promote motor learning in mice[18] as well as humans[19, 20].
To encourage active participation, and allow natural gait variability, more and more robotic devices control the interaction forces by using impedance or admittance control algorithms[21–29]. They guide the leg by applying a force rather than imposing a trajectory. Impedance (or admittance) control can also make the robot's behavior more flexible and adaptive to the patient's capabilities, progress, and current participation. Depending on the impedance levels, small errors are still possible, promoting motor recovery. Patients might also increase their motivation, since additional effort by the patient is reflected in their gait pattern. Controllers based on this principle are referred to as "assist-as-needed" (AAN), "cooperative," "adaptive," or "interactive" controllers. In mice, these AAN algorithms have been shown to be more effective than position-controlled training[30].
Using impedance control instead of position control, however, introduces new challenges. First, low impedance levels increase the risk that the subject and robot start to walk out of phase. Consequently, the robot will resist, rather than support, the subject. Different algorithms have been proposed to avoid synchronization problems. To account for alterations in cadence, the reference pattern of the robotic controller can be accelerated or decelerated, based on the difference between the current gait phase of the subject and the state of the robot. This can be done continuously[21] or on a step-by-step basis[27].
Second, the impedance level needs to match the patient's capabilities and progress, which can vary widely due to different levels of increased muscle tone, muscle weakness, or loss of coordinated control. This makes choosing the appropriate setting a priori a difficult process for the operator. In most applications, the amount of support is set by the operator on a trial-and-error basis. Setting the support levels too low can result in a dangerous situation, whereas too much assistance might reduce active participation of the patient. Roughly two strategies can be distinguished to automate the process of setting the support levels. The support levels can be adjusted based on increased patient effort (detected with force sensors)[24], or based on kinematic errors[27]. Emken, et al.[27], developed an error-based controller with a forgetting factor. The algorithm systematically reduces the impedance levels when kinematic errors are small, whereas it increases the impedance when the errors are large. When the subject (unconsciously) reduces his effort, he will experience no support. Only when the subject fails to commit to the reference pattern for a longer duration of time, the support will be increased. This should prevent the patient from becoming reliant on the support. In parallel, it allows normal gait variability by lowering the impedance levels, when possible. Others use a deadband or a non-linear stiffness to allow normal variability, without causing the robot to increase its assistive forces[25, 29].
Third, even when the impedance levels are adaptive, the whole movement is still potentially supported. This implies that the patient receives support during gait phases where his performance decreases, making no distinction between the patient's incapability, reliance, or fatigue. This also limits the possibility to focus the therapy on specific aspects of the walking pattern that require special attention.
Fourth, despite that impedance control does not rigidly impose a fixed reference pattern, it still requires some sort of reference pattern to determine the supportive force. These patterns are mostly based on pre-recorded trajectories from unimpaired volunteers. The major limitation of these patterns is that they are not publicly available. Additionally, most patterns are recorded at a limited number of speeds, while the progress of the patients' preferred walking speed can be as small as 0.1 km/h.
In this paper, we extend the support strategy that we currently use in out gait trainer LOPES[31]. Within this strategy, patients are supported based on the execution of their gait subtasks, rather than their complete leg movement. Recent simulation and experimental studies[32, 33] showed that the muscle activity during walking can be decomposed in different "modules." Each of these modules can be associated with a specific subtask of walking (e.g., body weight support, forward propulsion or foot clearance). In stroke survivors, each of these subtasks can be impaired to some degree without automatically affecting others. Selectively supporting these subtasks, based on the capabilities and progress of the patient, can be seen as an extension of the "assist-as-needed" principle. Also, the subtasks of both legs can be regarded separately, since in most stroke survivors the paretic leg will be more affected than the non-paretic leg. Controlling gait subtasks, rather than joint angles, also implies that compensatory strategies, like hip circumduction to create more foot clearance, can still be used. Imposing a symmetrical joint-angular reference pattern also limits the possibility of the non-paretic leg to compensate for the deficiencies of the paretic leg.
For the foot-clearance subtask, we developed a controller based on the Virtual Model Control framework[34]. This kind of control provides an elegant way to prevent synchronization problems by only controlling a specific subtask during the corresponding phase of the gait cycle. Using Virtual Models for different subtasks also allows straight-forward adaptation of the support to the subject's specific needs by only turning on the controllers for impaired subtasks. A pilot study on a small number of healthy subjects already showed that this method allows selective control of foot clearance, while leaving the remaining walking pattern largely unaffected[31]. However, also within a specific subtask, the amount of support needs to match the specific needs of the patient. The support should be such that 1) large errors are prevented, 2) safe walking is guaranteed, 3) small errors and variations over steps are allowed and 4) reliance is minimized. In another pilot study we incorporated the adaptive algorithm, that shaped the impedance as a function of tracking performance, and that was introduced by Emken, et al.[27]. With that pilot study we showed that the stiffness profile converged to a subject-specific pattern, that varied over the gait cycle and matched the subject's needs[35]. During the various pilot experiments, we also experienced that visual feedback, based on basic gait parameters like foot clearance, is easier to interpret for patients and therapists than feedback in terms of joint angles or interaction torques.
The main contribution of this paper is to show the effectiveness of selective-subtask-support, in conjunction with adaptive support levels, in stroke survivors. Young healthy subjects will be used as a control group. Secondly, a new method to quantify reliance will be tested. Since reliance, or "slacking," is known to be present in upper-limb robotic support[36], and is considered to be an undesired effect, we try to investigate this phenomena using catch trials. Catch trials are often used in motor learning experiments to evaluate human behavior during prolonged exposure to external stimuli. To our knowledge, this type of methodology, to quantify reliance in lower-limb robotic gait training for stroke survivors, has not been used before. Because reliance is closely related to the feedback that the patient receives, we also developed a system to provide the patient with visual feedback about his performance. Thirdly, we will investigate the use of compensatory strategies in the robotic gait trainer. Since LOPES[28] allows hip abduction, patients are allowed to employ their compensatory strategies in the device. This puts us in the unique position to evaluate whether patients reduce their compensatory strategies when they receive robotic support. Before testing the VMC framework, we will also present a new method, and results, of constructing reference trajectories for the ankle movement at different speeds. First, the pattern is parameterized by defining different key events (minima, maxima etc.), which are extracted from the individual patterns. Next, the walking speed and body-height dependency of the parameters are determined by regression models. These regression models can be used to reconstruct patient-specific ankle movement patterns at any speed.
Reference patterns
Eleven healthy elderly subjects (five male, six female, age 57.3 ± 5.9, weight 74.9 kg ± 11.9, length 1.70 m ± 0.11) volunteered to participate in an experiment that was setup to collect the reference patterns that are required for VMC of the step height. All subjects had no symptoms of orthopedic or neurological disorders and gave informed consent before participating in the experiments.
Experimental protocol
Gait kinematics were recorded using an optical tracking system (Vicon Oxford Metrics, Oxford, UK) at a frequency of 120 Hz. To track the motion of the subject, twenty-one passive reflective markers were attached to bony landmarks on the legs and trunk. The subjects were asked to walk on a treadmill at seven different speeds: 0.5, 1, 1.5, 2, 3, 4 and 5 km/h. After a general familiarization period of three minutes, the subjects walked for three more minutes at each selected speed. During each trial, the subjects did not receive any specific instructions about how to walk on the treadmill. After each trial, the subject had a one-minute break.
Data analysis
Different steps were taken to derive the regression models.
Only the last minute of each trial was used for data analysis. The recorded marker positions were processed using custom-written MATLAB software[37]. Since the proposed VMC approach is end-point based, we do not require the hip and knee angular reference patterns, but only the ankle pattern in Cartesian space.
Key events and predictor variables
Predictor variables
Where v represents the walking speed and l the body height. y represents the index, position or velocity of a particular key event. Stepwise regression[39] was used to test the statistical significance of the predictor variables, using entrance/exit tolerances of 0.05 on the p-values.
Regression coefficients
Spline fitting
Now that the regression models for the key events are known, a reference pattern can be reconstructed for each walking speed (in the range of 0.5-5.0 km/h). First, the index, position, and velocity of the key events, for a certain speed and body height, are calculated. Next, a cubic spline is fitted between every pair of consecutive key events, resulting in 6 (3rd order) polynomials describing the ankle-height pattern. By definition the position and velocity of the first key event (at 0 percent of the gait cycle) and the end of the last spline (at 100 percent) are equal. The resulting set of splines are merged to construct the reference pattern over the complete gait cycle.
To determine the accurateness of the spline-fitting procedure, we compared the constructed splines (based on data from the right ankle of all subjects) with the left ankle pattern of each subject. First, we reconstructed the reference patterns for the set of walking speeds (0.5-5 km/h) for each subject, taking into account the subject's individual body height. Next, their average left ankle pattern, during the last minute of walking at the different speeds, was calculated and the Root Mean Square Error (RMSE) between both signals was calculated. The resulting RMSE was averaged across subjects, for each speed.
Additionally, the correlation coefficient was used to quantify the similarity between the left ankle pattern and the reconstructed pattern. Both comparisons were performed for the ankle-height profile and the ankle-height velocity profile.
Selective support of subtasks
Clinical description of patient group
Age (years)
Weight (kg)
Length (m)
Time since stroke (month)
Type of stroke
Paretic side
Proprioception (P/NP)
MI leg
Experimental apparatus and recordings
Linear and rotary potentiometers measured translations and angular rotations of all degrees of freedom. Kinematics were used to detect heel contact (HC) and toe-off (TO) events. HC and TO were used as triggers to switch the robotic support on and off, and used to segment the data into individual strides.
xPC Target was used for real-time control at 1000 Hz. From the measured exoskeleton joint angles, and the human segment lengths, the ankle position is calculated at each instant of time. Data is collected on the target computer in real-time and then transferred to a host machine, where it was sampled at 100 Hz and stored for off-line analysis, using custom software (MATLAB, Mathworks Inc., Natick, MA, USA). For all subjects we measured joint kinematics (angles and Cartesian positions), the torques applied by the LOPES, and the gait phases.
Virtual model control
Initial testing showed that no damping was needed, since the human limbs provide a kind of natural damping to the system. The reconstruction of the reference ankle-height pattern is explained in the next section.
For foot-clearance support, only support in vertical direction is required, therefore F x is zero. The symbols are defined in Figure 4.
Reference pattern reconstruction
For the patient group, the reference pattern is reconstructed in the way described before, using the obtained regression models (see spline fitting). In order to investigate the effectiveness of the VMC approach for healthy subjects, we chose to increase the reference pattern, since the subjects were expected to already walk according to the pattern. The shape of the reference pattern is calculated similarly as in the patient group, but the obtained pattern was multiplied such that the maximal ankle height of the reference pattern reached a 15-percent increase with respect to their nominal maximum ankle height. The nominal ankle height of each subject was obtained from a walking trial in LOPES where no support was provided.
To prevent synchronization problems a specific subtask is only supported during the phases in which the subtask should be performed. For the step-height support, this indicates that the controller is only active during the double stance (with the contralateral leg in front) and the swing phase. Heel contact and toe-off events were detected in real time based on a phase-detection method developed by Zeni, et al.[38]. To account for alterations in cadence the speed at which the reference trajectory is replayed is scaled to the previous cycle time, and the timer is reset at the contralateral heel contact.
Impedance shaping
Where the superscript i denotes the ith step cycle, f is a forgetting factor set to 0.9, g is an error-based gain set to 1800, K z is the resulting stiffness profile for the ankle height, and t indicates the percentage of the gait cycle, which is estimated based on the previous cycle time. The stiffness was constrained to positive values, since the support is intended to lift the ankle, and not push the ankle downwards, when the ankle is above the reference.
Experimental protocol
Before positioning a subject in the LOPES, different anthropometric measurements were taken to adjust the exoskeleton segments lengths. Next, the subject was positioned into the LOPES and the trunk, thigh, and upper- and lower shank were strapped to the exoskeleton.
After a general familiarization period, the preferred walking speed was determined for each stroke survivor individually. During this familiarization period, LOPES was operated in the zero-impedance mode. In this mode the impedance of every joint is set to zero, so the robot provides minimal resistance/assistance to the stroke survivor[42]. All patient trials were performed at the same predefined preferred walking speed. All healthy control subjects walked at 3 km/h.
Next, the stroke survivors, and healthy control subjects, were exposed to selective control of the step height with a compliant (600 N/m), stiff (1200 N/m) and adaptive virtual spring (see impedance shaping). The stiffest of these springs was chosen as having the maximum stiffness that was comfortable for subjects during pilot experiments. For the healthy control subjects all conditions were tested on the right leg only, while the left leg was operated in zero impedance. The stroke survivors were supported on their impaired side.
For the patient group, we intended to use visual feedback to maximally motivate the subjects in taking higher steps. To investigate if subjects are capable of translating the information from a simple visual feedback system into the appropriate action, the visual feedback system was first tested on the healthy control group. The visual feedback system consisted of a screen, showing bars that represented the maximum ankle height of their most recent step. Also, the target height was displayed. Preliminary results showed that healthy subjects were able to use this visual feedback to reach the target height very accurately. Therefore, it was decided to provide this kind of visual feedback to the patient group in almost all conditions.
List of the tested conditions
Stiffness (N/m)
Supported leg
Visual feedback
Healthy controls
Stroke survivors
Data analysis
In general, the effectiveness of the step-height controller was assessed by determining how well the set reference values were attained, and how the support affected other aspects of walking. First, the data was segmented into separate steps based on the heel contact events[38]. Next, different spatiotemporal gait parameters were extracted from the ankle trajectories: the maximal ankle height (step height), the step length, and the cycle time. The maximum ankle height is the maximum vertical displacement of the ankle during swing. The step length is the relative horizontal displacement of one ankle with respect to the opposite ankle at the moment of heel contact. All gait parameters were normalized with respect to their nominal values. This allowed for comparison across subjects and conditions. For the healthy subjects, as well as the stroke survivors, the nominal values were recorded during a trial in which they walked in the zero impedance mode. Additionally, the relative duration of the different gait phases was calculated. All parameters were obtained for the exposure, as well as the catch blocks. Group averages were calculated for the stroke survivor and healthy control group.
To investigate the reduction of compensatory strategies, we also determined the maximum knee flexion, maximum hip abduction, and maximum pelvic height for the stroke survivors during the different conditions. Stroke survivors, with stiff-knee gait, for example, often fail to reach enough toe clearance and use different compensatory strategies to overcome their reduced knee flexion. Common strategies are a circumduction strategy, pelvic hiking, and vaulting. Vaulting is caused by an increase of the plantar flexion of the non-paretic leg, pushing the pelvis upward and creating more foot clearance on the paretic size. We hypothesize that, when stroke survivors experience step-height support, they reduce their compensatory strategies. Thus, assisting one subtask might automatically correct gait kinematics elsewhere.
Statistical analysis
To investigate the selectivity of the VMC support, we first used a one sample t-test to determine whether the percentage change in the spatiotemporal parameters differed from 0 percent. If the step-height support significantly influenced one of the defined spatiotemporal parameters, we used a paired t-test to assess whether there was a statistically significant difference between the conditions with the compliant and stiff virtual spring. All statistical tests were performed with SPSS Statistics (IBM Corporation, Armonk, NY, USA). The level of significance was defined at 5 percent.
Regression models for the reference patterns
Regression models for the index, position and velocity of the different key events
β3(body height)
1st local max vel.
max height
2nd local max vel.
2nd local min vel.
min height
β 0 (Intercept)
β 1 (speed)
β 2 (speed 2 )
β 3 (body height)
−0.387 x10-2
0.768 x10-2
0. 572 x10-2
1st local max vel.
7.41 x10-2
1.73 x10-2
−0.165 x10-2
1.36 x10-2
max height
−2.63 x10-2
3.57 x10-2
−0.362 x10-2
6.64 x10-2
1.44 x10-2
2nd local max vel.
1.49 x10-2
0.0221 x10-2
0. 345 x10-2
2nd local min vel.
1.05 x10-2
0. 231 x10-2
min height
β 0 (Intercept)
β 1 (speed)
β 2 (speed 2 )
β 3 (body height)
2.79 x10-3
0. 732 x10-3
−1.75 x10-3
0. 466 x10-3
1st local max vel.
−6.22 x10-3
1.54 x10-3
−0. 207 x10-3
7.95 x10-3
1.08 x10-3
max height
2nd local max vel.
−3.31 x10-3
0.0307 x10-3
2.35 x10-3
0. 970 x10-3
2nd local min vel.
−2.57 x10-3
0. 628 x10-3
min height
As a reference, we also calculated the RMSE and correlation coefficient between the measured right and left ankle patterns. These values provide an indication of the achievable fitting quality (see Figure 6). The correlations between the reconstructed spline and left leg data were very close to the correlations between the left and right leg data, whereas the RMSE values were approximately twice as large.
Healthy control group
Selective and gradual support
Non-adaptive support does not induce reliance
Visual feedback enhances performance and active participation
Stroke survivors
Selective and gradual support
Providing step-height support to the stroke survivors resulted in a selective increase in step height, without significantly affecting the other basic gait parameters, including the relative duration of the different gait phases (see Figure 7). The support was also gradual, since the use of the stiff controller resulted in a significant increase in step height compared to the compliant controller. The stroke survivors show a larger standard error of the mean of the paretic step height, compared to the right step height of the control group. For the compliant controller the increase in nominal step height ranged between 0 and 26 percent, whereas the increase in step height due to the stiff controller ranged between 0 and 44 Percent. For the stiff controller the average maximum joint torques were 21.0 Nm hip extension and 17.4 Nm knee flexion. The stroke survivors also show an asymmetry in stance phase (see Figure 7), which is often observed in stroke survivors[43].
Impedance shaping
Non-adaptive support does not induce reliance
Similar to the healthy control group, we did not find any evidence that indicated that the stroke survivors started to rely on the support. We did not find a significant difference between the initial exposure and the end of the exposure blocks (see Figure 10). This is also confirmed by the catch blocks, which show that during support (with the compliant or stiff VMC) the stroke survivors drop back to their baseline, without any significant undershot.
Visual feedback enhances performance and active participation
No reduction of compensatory strategies
The purpose of this study was to assess the effectiveness of selectively supporting the step height during the swing phase. First, we derived regression models for the key events of the reference ankle-height pattern. These models can be used to reconstruct patient-specific reference patterns at any speed. The proposed step-height VMC was tested on healthy subjects and chronic stroke survivors, and proved effective in selectively influencing the step height. Additionally, the step height could be manipulated easily by changing the impedance levels. Incorporation of an impedance-shaping algorithm resulted in an adaptation of the impedance to the specific needs of every individual stroke survivor. Catch trials were used to investigate whether healthy subjects, or stroke survivors, would start to rely on the robotic support, but revealed no signs of reliance. The step height parameter was used to provide intuitive visual feedback. Both groups were able to utilize this feedback. We did not find evidence that the stroke survivors reduced their compensatory strategies when support was provided.
Reference pattern reconstruction
A large part of this paper concerns the reconstruction of the reference patterns. Throughout the literature, different strategies exist to determine these reference patterns. Most reference patterns are based on pre-recorded trajectories from unimpaired volunteers walking on a treadmill[24, 29, 44, 45], or based on walking in the device while it is operated in a transparent mode[21, 27], or with the motors removed[46]. Patient-specific patterns can be obtained by recording the gait trajectories while the patient walks with manual assistance[21, 27], or by defining joint patterns based on movements of the unimpaired limb[47]. Most methodologies, however, have certain considerations that limit the use of the recorded trajectories to a specific application.
A major limitation of most of these reference patterns is that it is unknown how to correct for changes in speed. Most pre-recorded trajectories are recorded at a limited number of speeds, while the progress of the patients' preferred walking speed can be as small as 0.1 km/h. The coupling between the right and left leg[47] will change at different speeds and the recorded pattern, obtained during manual assistance[21, 27], will only be valid for that specific speed. Scaling algorithms can be used to compensate for changes in speed or cadence[48]. Most scaling algorithms, however, apply scaling in time, amplitude and offset, whereas also the (relative) timing of the maximum joint angles changes at different speeds.
For the patterns recorded in the gait trainer itself, another limitation should be noted. Due to the mass and inertia of the device, and/or imperfections of the transparent mode, these patterns might not match with the ones recorded during free walking. Emken, et al., found that the added inertia resulted in a slightly higher stepping pattern compared to free walking[22], while others found a significant and relevant decrease in knee angular range due to the device[42].
Most pre-recorded trajectories are obtained by rescaling the gait pattern to a percentage of the gait cycle, and taking the mean across subjects. This introduces another issue. Averaging normalized data can result in an underestimation of the extremes in the gait pattern, when the subjects have a different distribution of the gait events throughout the gait cycle[49].
Therefore, we developed a method where the gait pattern is parameterized by defining different key events (minima, maxima etc.), which all have a timing, position, and velocity. Next, the walking speed and body-height dependency of the parameters are determined by regression models. This way, the extreme value in the reconstructed pattern is actually based on the extreme values of the individual patterns, even when the extremes occur at another percentage in the gait cycle.
Another advantage of the proposed method, compared to other available methods, is that it can be used to construct a reference ankle-height pattern at each particular walking speed between 0.5 and 5.0 km/h, for persons with different body heights. This allows the physical therapist to easily increase the training speed, even within a single walking session. Speed-dependent reference pattern adjustments are also essential when the patient is in control of the walking speed, either manually or with intuitive speed-adaptation algorithms[50].
The proposed method is also generally applicable and can be applied to reconstruct speed-dependent reference patterns for joint angles. Different studies have already shown that peak joint kinematics are dependent in a linear and/or quadratic way on walking speed[51], and that its occurrence (timing) is also speed dependent[52].
There were some limitations in deriving the regression equations, which are related to the relatively low number of subjects (11) in this study. Due to this small number, we did not derive separate equations for male and female subjects, whereas systematic effects of gender of kinematics have been reported[53]. The range of body heights in this group of subjects was limited (1.52 m to 1.86). However, this range is expected to be sufficient for the majority of the elderly population.
Selective and gradual support
The results from the stroke survivors and healthy control group showed that the step-height VMC could selectively influence the step height. Supporting the step height did not significantly affect other spatiotemporal gait parameters, like non-supported step height, step length, or relative gait phase duration. Although the subjects were free to adapt their cadence, no change in cycle time was observed. As expected, the support was also gradual, a higher stiffness resulted in a closer approximation of the target values.
On average, the stroke survivors received more supportive hip and knee torque. At baseline, the stroke survivors walked more below the reference than the healthy subjects, resulting in more supportive torque.
The stroke survivors also showed a larger standard error of the mean of the paretic step height, compared to the right step height of the control group. For the healthy subjects, the reference ankle-height pattern was scaled such that it reached a 15-percent increase with respect to their nominal maximum ankle height. For the stroke survivors, the reference pattern was purely based on the regression formulas. The stroke survivors who were less affected, and almost reached their target value without support, showed a smaller (relative) increase in step height, compared to the patients that performed less without the support.
Impedance shaping
Selective-subtask-support already allowed us to focus the robotic support on the subtasks that are impaired. However, also within a subtask, the amount of support needs to be minimized to the personal needs of the patient. Aoyagi, et al., already suggested that by scheduling the impedance as a function of the gait cycle, the assistance can be further personalized[21]. This, however, is impossible for the operator to manually adjust. Therefore, we chose to adopt an adaptive algorithm, that shaped the impedance based on the tracking performance, that was suggested by Emken, et al.[27].
Emken, et al., reported that the impedance converged repeatedly over separate trials[27]. Although we only performed one trial with the adaptive algorithm per patient, the impedance profile shaped according to the initial error between ankle and the reference trajectory for all patients. This indicates that the shaped impedance was directly related to the patient's incapabilities.
All stroke survivors converged to a stiffness profile where the stiffness was highest at swing initiation. This is in agreement with the trials where a constant stiffness was used. There, most of the assistive torques were exerted during swing initiation, indicating that that phase requires most of the support. Because of the provided torques during initial swing, the leg was propelled upward with a higher velocity, and required less support during the remainder of the swing phase. Anderson, et al., already demonstrated the importance of knee angular velocity at swing initiation in normal gait. They showed that the knee angular velocity at heel off was the main determinant for the maximal knee angle, and foot clearance, during swing[54]. Reduced angular velocity, and foot clearance, in stiff-knee gait is suggested to be caused by an abnormal knee flexion during swing initiation. Kerrigan, et al., and Riley, et al., found an inappropriate activity in at least one of the quadriceps muscles during the pre-swing or initial swing phase[55, 56], which inhibit a normal knee flexion. Kerrigan, et al.[55], also reported that patients with delayed heel rise achieved less peak knee flexion. The patients included in this study also showed a delayed heel rise. So, providing support during this phase seems like a natural, and the most effective, way to increase the maximum knee angle, and subsequently foot clearance.
Apart from shaping the impedance to the patient's individual needs, minimizing the impedance also allows more variability within the stepping pattern, which has been shown to promote motor learning in mice[18]. Emken, et al., reported an increase in variability in maximum step height and step length, but could not verify whether increasing the variability during gait training had a positive effect on EMG activity levels[27]. In our study, we did not investigate the variability within the gait pattern. We did see a clear reduction in the impedance levels where the stroke survivors required less support, which allows them to vary their steps in a more natural way compared to walking with a stiff controller. The possibility to make small gait variation was also promoted by using a unidirectional spring that only provided support in taking a higher step, thus not constraining the ankle when it reached above the reference.
Based on previous pilot experiments[35], and computational models of movement training[57], we hypothesized that the stroke survivors and healthy controls would start to rely on the support, such that when assistance is no longer provided, their performance becomes worse. Previous studies, that let subjects adapt to external force fields, already showed that the human motor system can be modeled as a process that greedily minimizes a cost function, consisting of a weighted sum of kinematic error and effort[58, 59]. In these studies, a forgetting factor is introduced in the human effort, which models that the human continuously tries to accomplish the prescribed movement with reduced effort.
In this study, however, we did not find patients, or healthy subjects, who started to rely on the support. A likely explanation for the patient group could be the visual feedback, which we did not use in the pilot experiment[35]. The visual feedback provided them with information about their performance on a step-by-step basis, increasing their motivation and reducing the changes of reliance.
Also in the healthy control group, who did not receive visual feedback in most trials, we did not observe reliance. To evaluate the effects of prolonged exposure, the trials were concluded with 50 steps of continuous exposure. This block might have been too short for the subjects to explore the benefits of the support and start to rely on it. The relatively low impedance levels might contribute to this effect.
Also the task instruction and type of support might explain our findings. In most motor learning experiments, a disturbing force field is applied and the subjects are asked to reduce the error. To reduce the error, the subjects have to produce additional effort to overcome the disturbance. During this process, they continuously try to minimize the trade-off between reducing their effort and increasing the error[58, 59]. In our experiments, the subjects experience a force field that decreases, rather than increases, the performance error. Here, the subjects are not challenged to provide additional effort, which might not elicit them to reduce their effort.
The fact that a relative small movement error can cause the subjects to trip might also have contributed to the fact that these subjects did not start to rely on the support. This would indicate that the weight of the error in the cost function increases compared to the reduction in effort. Bays, et al., already suggested that humans can change the weighting of different costs, according to the task and type of the movement[60].
Although the chances that reliance will occur are reduced by minimizing and localizing the support with the impedance shaping algorithm, two issues remain. First, the algorithm cannot distinguish between a decrease in effort due to reliance or due to fatigue. In both cases, the algorithm will increase its support. Second, subjects might still, consciously or unconsciously, reduce their effort over time and consequently receive more support. Emken, et al., showed that, to effectively assist-as-needed, the robot must reduce its assistance at a rate that is faster than that of the learning human[59]. They stated that reliance can be prevented by setting the forgetting factor to a lower value than the learning rate of the subjects. They also state that determining the learning rate for neurological patients can be difficult because of their impaired motor control due to spasticity, muscle weakness, and synergies. Therefore, we chose to set the forgetting factor based on a stable convergence of the stiffness pattern within approximately 30 steps.
Finally, one might argue that to eliminate reliance one should apply resistive forces rather than supportive forces. In fact, error-enhancing therapy is suggested to be more effective than assistive therapy[20, 61]. For some training exercises, where movement errors do not impose serious safety issues, this might be true. For robotic treadmill training, where small movement errors can have large consequences, this strategy may be inappropriate.
Visual feedback
In this study, very simple visual feedback was provided in the form of the step-height parameter. We showed that both the stroke survivors and the healthy controls were capable of utilizing this information effectively. Providing visual feedback to the healthy controls led to a very close approximation of the reference values. Adding visual feedback to the trials, in which the stroke survivors received adaptive support, led to lower impedance levels in three of the four patients, indicating that these patients are additionally motivated by the visual feedback.
The key element of any form of feedback is that it displays the subject's effort in an intuitive manner. Different forms of feedback are available. A review performed by Teasell, et al.[62], concluded that there is a positive effect of EMG feedback in patients after stroke. Others use the subject's kinematics to display their performance[25, 29], or the interaction force between user and robot, like in the Lokomat[63].
A disadvantage of the latter approach is that it is only applicable to position-controlled gait trainers. In these type of gait trainers, the additional effort of the subject is reflected on the screen, but is not reflected in their gait pattern. This might decrease the motivation of the subject. Thus, to optimize visual feedback, the gait trainer needs to be compliant. In more recent versions of the Lokomat, Duschau-Wicke, et al., introduced a more patient-cooperative strategy, effectively making the robot more compliant[25]. In their study, they used body kinematics as visual feedback.
To optimize the feedback, factors like the amount of information and its frequency need to be investigated. Also the complexity of the feedback is important - do we need detailed information from every joint, or combined information from several joints, like the ankle position? Banala, et al.[29], only displayed the ankle position in the sagittal plane. Our results suggest that only showing the maximum ankle height of the last step is already sufficient to control the hip and knee joint such that the subject takes a higher step. Also, for the therapist himself, we expect that feedback in the form of basic gait parameters will be easier to interpret, compared to joint angles or ankle trajectories.
The primary goal of visual feedback is, of course, to contribute to the long-term changes in relearned gait kinematics. Kim, et al.[26], used the ALEX to induce gait modification in healthy adults. They reported that a combination of visual and force guidance resulted in larger modifications in step height that maintained longer, persisting up to two hours, whereas only visual guidance or only force guidance evoked changes that did not last beyond the 10-min retention test. Although we did not investigate retention, our experience with visual feedback is encouraging, and can serve as a starting point in the investigation about how to optimize gait training in such a way that short-term gait adaptation can become long-lasting gait modification.
Compensatory strategies
The VMC approach, used in this study, is an end-point-based-control strategy. This implies that within a certain subtask there is more freedom to walk, or choose a certain strategy. For example, different patients might choose different strategies to accomplish appropriate foot clearance. With the step-height VMC, the patients are left free in the strategy they use to clear their foot and will only receive support when this task is not executed successfully. This means that compensatory strategies[64, 65], like pelvic hiking, hip circumduction, or vaulting[66, 67], which are seen in most stroke survivors, can still be employed. Joint control limits the use of these strategies. Additionally, imposing a symmetrical joint-angle pattern limits the possibility of the non-paretic leg to compensate for the deficiencies of the paretic leg. Although these compensatory strategies do not contribute to a more symmetric walking pattern, they do increase basic gait function[68, 69]. Some even advocate teaching compensatory strategies because of time and financial limitations[70]. Thus, because it is still largely debated whether the focus of robotic gait training should be on restitution of a normal walking pattern or on these compensatory strategies, they should not be overruled.
The use of these compensatory strategies might even become redundant when support is provided on the impaired subtask that evokes these compensatory strategies. We hypothesized that providing support on one subtask, i.e. foot clearance, would reduce the need for the patient to employ his compensatory strategies. Although all our stroke survivors showed compensatory strategies without support, none of them reduced their compensatory strategies with support. During the experiments, the stroke survivors received no specific instructions about how to walk on the treadmill. Therefore, they might not have been triggered to reduce their compensatory strategies. Also, the limited time that the stroke survivors walked in the LOPES during the experiments, in combination with the amount of time it would take to un-teach their adapted strategies, might be a reason for the unchanged kinematics. In the future, we might even develop special VMC modules that suppress compensatory strategies to promote restitution of a symmetrical walking pattern.
Related work
Different support methods have been suggested to correct the gait pattern of neurological patients. However, none of the compliant, or interactive, support methods has been evaluated in large-scale clinical trials. To guide potential clinical trials, the differences between our and other approaches will be explained. The method presented in this paper can be best compared to the "virtual tunnel" approach. Banala, et al., implemented this virtual tunnel approach, which was previously described by Chai, et al.,[30], and trained two chronic stroke survivors with the ALEX[29]. Their tunnel consisted of a healthy-control template and the assistance was composed of a normal force, that simulated the virtual walls, and a tangential force that helped the ankle move along the trajectory. A similar virtual tunnel strategy is implemented in the Lokomat to train iSCI patients. Duschau-Wicke, et al., also implemented a "moving window" that limits free movement to a region of the tunnel, similar to the tangential force in the ALEX[25]. In contrast to Banala, et al., they defined a torque field in joint space rather than a force field in Cartesian space. There are three main differences between the above-mentioned control strategies and the control strategy presented in this study.
First, both the ALEX and the Lokomat use some sort of support that potentially helps the ankle, or joint, move along the trajectory. The tangential force, used by Banala, et al.[29], decreases when the ankle deviates from the trajectory, thus the ankle is only pushed along the path when the ankle is close to the desired trajectory. Duschau-Wicke, et al.[25] use the moving window, that is synchronized with the user's cadence[71], to assist the user. In our study, no tangential force, or moving window, is used. Within the subtask-support strategy, step timing and foot clearance are two separate subtasks. Here, we only supported foot clearance. This allowed the subjects to freely change their timing, if they wished to do so. Still, subjects did not adapt their timing. For bilateral affected iSCI patients, who experience difficulties during swing initiation, or gait initiation in general, gait-timing assistance might be useful. In that case an additional VMC in the horizontal plane can be added. Our experience with stroke survivors suggests that the non-paretic leg can take care of the gait timing and the paretic leg will follow.
Second, both studies use a virtual tunnel that lifts the ankle[29], or increases joint angles[25], but can also do the opposite when the subject performs above the reference. In this study, a unidirectional spring was used, because the support is intended to support the subject in taking higher steps, and not push the ankle downwards, when the ankle is above the reference.
Third, in contrast to the Lokomat, the support of subtasks is an end-point-based-control strategy, rather than a joint-angle-based-control strategy. As mentioned before, joint-angle-based-control strategies exclude the use of compensatory strategies.
Future applications of selective support
The key goal of future research is to expand the concept of subtask support. Support in taking higher steps is an important part of the rehabilitation process, but other subtasks might also require assistance. A new VMC, that assists patients in taking more symmetric steps, is currently under development, and its interaction with other subtask controllers is being investigated.
For severely affected patients, body weight support systems (BWSS) are often used. Alternatively, VMC can also be used to partially support the body weight by attaching a vertical virtual spring to the hips. In that case, the forces, required to bear your own body weight, are provided in terms of hip and knee torques, rather than lifting the body externally. This allows normal sensory input from the foot soles, which is essential in order to generate natural gait kinematics[72, 73]. VMC for body weight support also allows easy modulation of the amount of support between the different legs, since stroke patients primarily need support during the stance phase of the affected leg. It also enables separate control of body weight support and balance support, which can be considered as two separate subtasks, either of which can be impaired to a certain degree. BWSS, with an overhead harness, not only provide a force in the pure vertical direction, but also in the horizontal plane that stabilizes the body. Pilot experiments have shown the feasibility of body weight support with VMC[74]. The possibilities of VMC for balance support are now being investigated.
Finally, we started preliminary tests with an intuitive speed-adaptation algorithm, in which the patient can move freely over the treadmill and the speed is automatically adapted when the patient deviates from the center of the treadmill. In conjunction with the obtained speed-dependent reference patterns, this will provide the therapist and patient with tools to easily adapt the treadmill speed to the capabilities and progress of the patient, without the need to manually change the control settings.
In this study we implemented, and evaluated, a VMC strategy for selective and gradual support of gait subtasks. Here we focused on one specific subtask, i.e. increasing foot clearance. Initially, we derived and provided regression models that can be used to reconstruct patient-specific ankle movement patterns based on body height and walking speed. The RMSE between the predicted and actual trajectory was around 1 cm for all walking speeds. The proposed method can also be applied to reconstruct speed-dependent reference patterns for joint angles. Experiments with healthy subjects, and chronic stroke survivors, showed that with the proposed VMC approach, the step height could be selectively and gradually influenced, without affecting other spatiotemporal gait variables. In conjunction, we tested an impedance-shaping algorithm, which shaped the impedance to the patient's individual needs. The provided support did not result in reliance on the support for both the stroke survivors as well as the healthy control groups. Providing visual feedback to the user resulted in an increased active contribution in all healthy subjects and three of the four stroke survivors. The presented VMC approach, and impedance shaping, can be crucial for the development of new rehabilitation strategies and robotic gait trainers. It allows automatic localization and minimization of the support, which increases active patient contribution and promotes functional recovery.
This study was supported by a grant from Dutch Ministry of Economic affairs and Province of Overijssel, the Netherlands (grant: PID082004). We would like to thank Jaap Buurke and Martijn Postma, from the Roessingh Research and Development, for their assistance during the patient experiments.
Authors' Affiliations
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- Bays PM, Wolpert DM: Computational principles of sensorimotor control that minimize uncertainty and variability. J Physiol 2007, 578: 387-396.PubMed CentralView ArticlePubMedGoogle Scholar
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- Banz R, Bolliger M, Colombo G, Dietz V, Lunenburger L: Computerized visual feedback: an adjunct to robotic-assisted gait training. Phys Ther 2008, 88: 1135-1145. 10.2522/ptj.20070203View ArticlePubMedGoogle Scholar
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


text:
I was reading through the documentation for "Post ZENworks 11 SP4 UPdate Prerequisites" and I was wondering if this effects the database at all? There is no indication that if you are using multiple primary servers, that you need to stop the services on the other primaries while you do the first one. Do we need to stop the services? Do not have too? Want to be sure before I update my environment.
Also will the ZENworks Diagnostic Center files be released for 11.4.1 soon?
Tim Overdorf


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The latest installment of Microsoft's browser battery challenge shows once again that Edge consumes less energy than Chrome and Firefox. From a report: With the Windows 10 April 2018 Update rolling out across the globe, Microsoft thinks it's once again time to square Edge up against Chrome and Firefox in a new battery-life test. Microsoft's browser experiment shows a time-lapse of "three identical devices, three different browsers, streaming one video." Firefox, Edge, and Chrome play what appears to be a Netflix video on three Surface Books. As usual, the Edge device lasts the longest, depleting the battery after 14 hours and 20 minutes. The Chrome device lasted 12 hours and 32 minutes, while the Firefox laptop ran out of steam after just seven hours and 15 minutes.
Read more of this story at Slashdot.


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Cowgirl Tuff Jeans - Vintage Wild & Wooly
Cowgirl Tuff Jeans - Vintage Wild & Wooly
The Cowgirl Tuff Vintage Wild & Wooly is a medium wash jean accented with our signature Wild & Wooly pocket design. It features tan and chocolate mixed stitching, light distressing and light whiskering. Standard seven belt loop design. Limited Edition. 98% Cotton 2% spandex.
FIT - This jean is the Unbelievable Fit, the most common Cowgirl Tuff fit, it's considered a mid-rise. They will sit on the point of your hip at the front and a little higher at the back.
SIZING - See the size chart in product images or visit the sizing page for a size comparison chart. Measure around the widest part your hips and use the size measurement chart in product images to see which size in the CTC Size column that aligns with. If you aren't sure please make contact.
Not sure of your size? Don't want to order online? Your size sold out? Why not contact us direct? Order now - phone 0432428363 or email email@example.com


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In 1792 during his search for the
famed Northwest Passage, Capt.
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Princess Louisa Inlet possesses a
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North America. This fabulous Pacific Northwest charter cruising destination will
provide lifelong memories of an inequalled bareboat charter experience.


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The current job situation has left millions of people without work. We're committed to making it easier to find a job.
Las Vegas, NV (PRWEB) December 10, 2012
Resume.com, a free resume hosting and job service, announced that it recently surpassed a landmark 100,000 online resumes. Since its launch, the website has steadily grown to become an important resource for many job seekers who are looking for new opportunities in a difficult job market.
Resume.com allows users to create and publish their resumes online for free. A job seeker can upload and edit their previous resumes or use the website's Resume Builder to create one from scratch.
The Resume Builder is a simple three-part process. Users start by choosing one of seven professional templates then fill in the necessary information about their work and educational backgrounds. Resume templates offer many of the details employers are looking for, making resume creation quick and easy. Resumes can then be published and shared online.
"The current job situation has left millions of people without work," said Linda Hildebrandt, Associate Director of Internet Marketing at Resume.com. "We are committed to taking some of the pressure out of the job hunt and provide an effective resource for both employers and job seekers."
The website also features social media integration allowing people to share their resume through Facebook, Twitter, or LinkedIn. Users can promote their resumes through social channels, and employers can view and share them while searching for candidates. Resumes can also be downloaded as a Word or PDF documents to be printed or viewed offline.
"A good resume is one of the most important job search tools in your tool box," Hildebrandt added. "Employers need to see exactly what you are capable of accomplishing, and the best way to market yourself is with a strong, clear, and organized resume."
About Resume.com
Resume.com is a free resume hosting and job service that allows job seekers to create and edit resumes online. Free resume hosting is also provided so potential employers instantly can view, download and print resumes.


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Real Estate Businesses Insurance
Keep your Assets Protected
Winder Insurance Center provides insurance to real estate agents and businesses in Winder, Jefferson, Bethlehem, Athens, Monroe, Braselton, and surrounding areas.
You will want to ensure that your real estate business is covered properly, primarily based on what type of real estate in which you are involved. At Winder Insurance Center, we will work with you to create a business owner's policy that can lump coverages, such as:
- Property insurance
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?Winder Insurance Center will help you lump together the right protection plan so you can keep your business protected in as an efficient manner as possible. Contact us today to set up a consultation.


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Skating On The Winter Sky – A Selection Of Mobile Photos On Ice
Winter is over, the thaw has come, and being the past, it can now be looked on in retrospect. Being from Los Angeles, that's about the only place I like to view winter from. One of those highlights worth reflecting on was a nice trip out on a lake, not too far a drive from here, that we went ice skating on with an acquaintance. The sun was out, warm for the first time, and the freezing cold weather had turned to a nice 5 degrees. That made for an interesting situation of thick, smooth ice on the lake covered by a thin layer of water perhaps a centimeter deep.
The view was magic, and it lent for some beautiful images! The image above is the wonderful, thick ice we were skating on, but looking up at the horizon, you wouldn't be able to tell by the sheer blue and untouched winter sky beneath our skates. I managed to get a nice shot of my son peering at the ice, though from my vantage point he is walking on water, staring into the deep blue.
All images were edited using Snapseed.
Winter Sky Images
[gmedia id=120]


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Claude Fontheim is the President of the Alliance for Global Health and Competitiveness and the CEO of Fontheim International, LLC. He also serves as Chairman of GlobalWorks Foundation, which is dedicated to fighting global poverty.
Mr. Fontheim advises global companies on international trade and business; government relations; and corporate social responsibility issues. He was among the first CSR practitioners and his work includes environment, energy, labor, human rights, health, and economic development issues.
Mr. Fontheim has published numerous articles, including on international trade, corporate social responsibility, and labor policy. These articles have appeared in Blueprint magazine, PR Week, various scholarly journals, and he authored the chapter on European telecommunications policy and regulation in the American Enterprise Institute series, "The United States Faces a United Europe," and the concluding chapter of Trade Policy: The Lawyer's Perspective of which he was co-editor.
Sam Buchman is an Analyst at the Alliance for Global Health and Competitiveness. He also works for Fontheim International, where he conducts research and analysis on corporate social responsibility and international trade policy.
Before joining the firm, Mr. Buchman worked on a number of different political campaigns in the state of Michigan. During the 2014 election cycle, he worked for candidates seeking seats in the Michigan State House of Representatives, serving as Communications Director for Gretchen Driskell, Campaign Manager for Sharon Wimple, and as Field Director/Co-Manager for Mary Kerwin. In the 2013 cycle, Mr. Buchman served as Assistant Communications Director for Maureen Brosnan's campaign for Livonia City Council.
Before joining Fontheim International as an Analyst, Mr. Buchman interned with the firm as part of the Michigan in Washington program. During college, he served as a research assistant at the Social Science Data Analysis Network, where he researched issues such as health insurance coverage, the foreign born population, voter turnout, and immigration into the United States. Additionally, Mr. Buchman interned with Hon. Wayne Easter, a Member of Parliament in the Canadian House of Commons.
Mr. Buchman holds a B.A. in Political Science from the University of Michigan.
The Alliance for Global Health and Competitiveness, LLC is a multi-stakeholder initiative (MSI) of the Business Council for Global Development, LLC.
© 2019 Alliance for Global Health and Competitiveness (AHC)
Tel: 202.296.8100


text:
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Fine & Clear: Rainbow Complete Barbies blue colored contacts offer great quality and dynamic color for your eyes. They give you a natural look that is fashionable and comfortable to wear. It is a great color contact lens that will be sure to enhance your style and appearance. FreshLook, Acuvue, and other brands are no match for Rainbow Complete!
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Martini Party - $62.00
(No rating yet)
Everything you need to get your martini party started! Whether you want Lemon Drops, Dirty Martinis or Cosmos, everything is here! 1 Liter bottle of Colorado's own Spring 44 Vodka will make over a dozen Martinis. Optional add-ons include your own shaker cup or martini kit.
- For Lemon Drops we include a 375 ml of Caravella Limoncella and 4 fresh lemons.
- For Dirty Martinis, included are 375ml of Santa Barbara Martini Juice and 9 pittled olives.
- For Cosmos, we have Rose's Cosmo Mix
And if you need the martini making fixings, we can include a Boston Shaker cup or a barkeeper set of jigger, shaker, strainer and spoon. No excuse to not have a party!


text:
Stephen R. Smith
Click here to email
Successful Resolution Services
Click for Full Website
321 Billerica Road
Suite 16
Chelmsford, MA 01824
Description of Practice
We provide professional and affordable mediation and arbitration services to clients in New England. As trained mediators and arbitrators we help people in conflict discuss concerns and find resolutions. SRS Mediation can help you resolve any conflict quickly and in a less formal manner than traditional court setting, often times preserving relationships and with better results for both parties. Mediation can help you clarify issues, develop options and negotiate towards a resolution with a neutral party.
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text:
Home >About US > Advantage >
SpaGeo has, in its charter, imbibed the philosophy of 'Client Satisfaction Foremost'. SpaGeo endeavors at amalgamating human intelligence and computer's speed to meet the GIS needs of the client and , at the same time, keeping the operations fairly simple.
SpaGeo is a firm providing full range of AM/FM/GIS/RS/DP services for government, utilities and business. Effective implementation of GIS solutions is complex endeavor that requires an integrated approach.SpaGeo consulting staff offers a wide range of startup services from an initial need assessment to full implementation.
SpaGeo is in technical collaboration with Geotrace Data Integration Services Ltd. , UK for Data Management Solutions , KB Panorama- Wide range of GIS Products & STAR-APIC -Enterprise GIS Solutions.


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Are you keen to improve respiratory services in your area but wondered how to turn your ideas into a reality?
Then don't miss the next two day PCRS-UK clinical leadership workshop on 16th- 17th June, which will help you to develop and practice the range of skills necessary to successfully implement a project.
Run by experienced PCRS-UK respiratory clinicians with guest speaker Catherine Blackaby, an independent coach, facilitator and trainer, this event will teach you:
- Why it is important to be clear about the aim and scope of your project
- Key steps/principles needed to lead to a successful project
- Tools and techniques you can use to turn your plan into a reality
- Key people to engage to make your project happen
This workshop is part of the PCRS-UK Respiratory Clinical Leadership programme. Participating in these workshops, held twice a year over a 2-3 year period, will enable you to build your breadth and depth of skills and knowledge and join a supportive network of experts and fellow participants.
The workshop is exclusive to PCRS-UK members and if you are not already a member you can join here for just £59.
For further information about Project initiation – your case for change being held on 16th-17th June at the Hilton Doubletree City Centre, Bristol, and to book a place, click here.


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In stock
Special Price
Regular Price:
The 2ml push fit 'pod' system for the Joyetech Exceed EDGE houses the popular EX atomizer heads and is great as a spare or replacement.Available in a pack of 5.
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text:
I am so thrilled to be attending LANDROVER POLO IN THE CITY Sydney this year, as a VIP Media Guest.
Of course, the first thing that came to mind, is 'What will I wear?'
Polo Fashion, is the ever so glamorous and chic sister to Racing Fashion and the approach of what to wear, should be the same.
Think understated elegance, with delicate fabrics and styles.
Lace in ivory, cream or neutral colours, pops of colour and prints, and a touch of monochrome are some of the go to options.
Styles can include… soft tailored lines, playsuits, jumpsuits, long gorgeous printed maxi dresses, an easy shift dress, a boho inspired, off the shoulder top and maxi skirt and of course, lets not forget the pant of the season.. a gorgeous delicate culotte!
Polo Fashion, is all about comfort but that shouldn't affect the glamour!
Polo is NOT the day, to be wearing your latest stiletto purchase. So please opt for a gorgeous glam sandal, comfortable wedge or chunky heel option.
Sunglasses and a decent size handbag is a MUST…Head wear is not paramount, however a wide brim hat will not only protect you from a day in the sun, but also add some glamour to your outfit! Please NO fascinators!
Jewellery should be complementary to your gorgeous day outfit. Thin delicate pieces, in gold or silver are a fashion must have at the moment. However if you're drawn to more of a statement piece, then opt for it in either gorgeous silver, gold. stones or pops of colour and leave the sparkly diamonte statements pieces at home.
Hair and Makeup…
Hair and makeup should be kept natural, but a pop of lip can never go astray!
Samantha Armytage looking very classic, in delicate monochrome and neutral wedges. Emily Hill in a classic white lace dress and navy shoes.
Naomi Roth in a printed maxi dress and tan heels. Sami Lukis and Michelle Bridges in pops of colour and prints.
Just like the ladies, Polo Fashion for men should be approached in the same way! A smart casual look is essential, there is no need to suit up!
The Polo is an opportunity to show off a little more of your personality. Have fun with pops of colour, print on print and add a point of difference.
Styles can include… A coloured printed shirt, or pop of colour, tailored pant, chino pants or shorts are a great option. A blazer with a stylish pocket square, is always a great way to complete an outfit. But please keep in mind the weather, if it's going to be too hot, a shirt is suffice.
There is no requirements to wear a tie, but if you prefer the look then go ahead. A bow tie could always be fun too!
Smart leather or suede brogues or loafers are a great choice, or a leather boat shoe. Please do not wear thongs!
Once again, sunglasses should be a must. And for the men wanting that added protection or style, a woven or felt fedora, is a great addition!
Alex Guidera in a classic white shirt, chinos and leather loafers. Peter Nyong'o in a classic blue shirt, black chinos and suede loafter with the gorgeous Lupita Nyong'o in monochrome.
Chris Joannou in tailored blue shirt, white chino shorts and casual shoes, with the Laura Csortan in a white & yellow maxi dress. James Tobin in a printed shirt, green chinos, navy blazer and brown suede laceups with Grace Huang by his side in an all white jumpsuit.


text: On 2007-01-10 12:36, dinesh sundar wrote: > Hi All > > > I have a *supervisor*, a *genserver* which receives > messages through udp and a *gen fsm* module which sends the messages > using the same socket. > The application *runs fine* when i start it the supervisor *without > using threads*..ie it is able to receive and send messages.. first i would like to mention that most people use the word ''threads'' to mean execution contexts that share memory. erlang processes do not share memory, so they are processes. > But when i run the supervisor using threads i am unable to receive and > send message through gen server and gen fsm module respectively.. if you could scale down this supervisor to as few lines as possible (that still fail) and then post it, that would make it easier to help. from your description i can only think of asking if you have taken into account the documentation for gen_udp that says: ''open(Port) -> {ok, Socket} | {error, Reason} ...deleted Associates a UDP port number (Port) with the calling process.'' ie, it is only the process that calls open/1 that will get the messages. bengt -- EPO guidelines 1978: "If the contribution to the known art resides solely in a computer program then the subject matter is not patentable in whatever manner it may be presented in the claims."


text:
Date: 24 May 2014 09:22:29 -0000
From: smokinjoe689 <smokinjoe689@xxxxxxxxxxxx>
Diabolical! !! I fuckin love it
-------- Original message --------

From: Chas <reignpdx@xxxxxxxxx>

Date:23/05/2014 10:56 PM (GMT-06:00)

To: tcbcourier@xxxxxxxxxxxxxxxx

Cc: messengers@xxxxxxxxx

Subject: Re: [messengers] [TCB] Re: 1hour record @ d.f. trackday

So in Taking care of the business > On May 23, 2014, at 8:34 PM, Alex Farioletti <alex@xxxxxxxxxxxxxx> wrote: > > Details: > > How many? > Everyone, at the same time, co-ed, co-op. We need everyone to beat the Cannibal. > > Gear ratio? > Go big 50x15 to 52x14 (90" to 98"). If we want to beat Eddy, we gotta go approx 32 mph for an hour, little gears means lotsa pedal strokes. > > Bars? > Drops, this will be massed start so unless you are a team pursuit pro, drops is best. Probably drops only. > > Co-op? Co-ed!? > Yes, we need to cooperate to succeed. We need lotsa bodies to keep a good draft. All ages, all genders, all abilities. I don't expect everyone who starts to finish, this will be tough, 32 mph for an hour at 7000'+ altitude is going to crush all but the Swiss, but even if you can only hang for a few laps you will have contributed to the record attempt and we welcome your efforts. It will be a race of attrition, competition will not be necessary. I expect less than 10 to finish. > > What's in it for me? > If we beat Eddy s record, the finishers will get their names inscribed on a giant silver trophy cup to be bestowed to the winner for safe keeping until the next hour record is set, whereupon the new record holders will take custody of the Hour of Power Cup. > > Even if we dont beat Eddy Merckx, we will still win since its the first one attempted. It will probably also be the last, since nobody is gonna be dumb enough to do it again. > > 1st annual Co-Op Co-Ed Cycle Messenger World Hour Record Attempt. > > Do it or die. > > Would be up for that thing! Are there any details for that. How many > racers? What gear ratio? (Recommended) what kinda bars? (Aerobars?) > Etc.. > Sending love out of the desert of baja! > Kaiser > -- > Diese Nachricht wurde von meinem Android Mobiltelefon mit GMX Mail > gesendet. > _______________________________________________ > Messengers mailing list > Messengers@xxxxxxxxx > http://ifbma.org/cgi-bin/mailman/listinfo/messengers > -- > Be Excellent to Each Other > MAINTAIN THE VEIL > TEXT THE CODE > TCB > --- > You received this message because you are subscribed to the Google Groups "TCBCOURIER" group. > To unsubscribe from this group and stop receiving emails from it, send an email to tcbcourier+unsubscribe@xxxxxxxxxxxxxxxx. > Visit this group at http://groups.google.com/group/tcbcourier. > For more options, visit https://groups.google.com/d/optout. _______________________________________________ Messengers mailing list Messengers@xxxxxxxxx http://ifbma.org/cgi-bin/mailman/listinfo/messengers