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Activity Recognition for Incomplete Spinal Cord Injury Subjects Using Hidden Markov Models

Successful activity recognition in patients with motor disabilities can improve patient care by informing researchers and clinicians about changes in patient mobility both in the clinic and at home. Standard machine learning approaches can improve activity recognition in patient populations by tailoring recognition models to specific populations. However, many approaches use only static machine learning classifiers, which classify each data sample individually, ignoring the temporal relationship of successive samples over time. Static classification can be augmented by integrating the output of static classifiers with a dynamic state estimation model. Here, we use a hidden Markov model (HMM) and apply the static supervised machine learning classifier results as observations. We experimentally validate the effectiveness of our model by recognizing six activities from 13 ambulatory incomplete spinal cord injury subjects who were instructed to perform a standardized set of activities while wearing a waist-worn accelerometer in a clinical setting. Activities included lying, sitting, standing, walking, wheeling, and stair climbing. Using within-subject cross validation, the highest classification accuracy from static classifiers alone was 86.3% (85.5%–87.0% and 95% confidence). By augmenting the classification model with an HMM, we were able to improve the accuracy to 88.9% (88.2%–89.6%). The additional 2.6% demonstrated a significant improvement of the classification accuracy using a hybrid static/dynamic classifier compared to the use of static classifiers alone. Such improved activity recognition can provide better outcome measures, aiding clinicians to select or refine the right physical or drug therapies to improve patient mobility.