by Keyword: Communication


Verschure, P., (2018). Capabilities Living machines: A handbook of research in biomimetics and biohybrid systems (ed. Prescott, T. J., Lepora, Nathan, Verschure, P.), Oxford Scholarship (Oxford, UK) , 211-217

This chapter introduces the “Capabilities” section of the Handbook of Living Machines. Where the previous section considered building blocks, we recognize that components or modules do not automatically make systems. Hence, in the remainder of this handbook, the emphasis is toward the capabilities of living systems and their emulation in artifacts. Capabilities often arise from the integration of multiple components and thus sensitize us to the need to develop a system-level perspective on living machines. Here we summarize and consider the 14 contributions in this section which cover perception, action, cognition, communication, and emotion, and the integration of these through cognitive architectures into systems that can emulate the full gamut of integrated behaviors seen in animals including, potentially, our own capacity for consciousness.

Keywords: Action, Cognition, Cognitive architecture, Communication, Consciousness, Emotion, Perception




Estrada, L., Torres, A., Sarlabous, L., Jané, R., (2016). Evaluating respiratory muscle activity using a wireless sensor platform Engineering in Medicine and Biology Society (EMBC) 38th Annual International Conference of the IEEE , IEEE (Orlando, USA) , 5769-5772

Wireless sensors are an emerging technology that allows to assist physicians in the monitoring of patients health status. This approach can be used for the non-invasive recording of the electrical respiratory muscle activity of the diaphragm (EMGdi). In this work, we acquired the EMGdi signal of a healthy subject performing an inspiratory load test. To this end, the EMGdi activity was captured from a single channel of electromyography using a wireless platform which was compared with the EMGdi and the inspiratory mouth pressure (Pmouth) recorded with a conventional lab equipment. From the EMGdi signal we were able to evaluate the neural respiratory drive, a biomarker used for assessing the respiratory muscle function. In addition, we evaluated the breathing movement and the cardiac activity, estimating two cardio-respiratory parameters: the respiratory rate and the heart rate. The correlation between the two EMGdi signals and the Pmouth improved with increasing the respiratory load (Pearson's correlation coefficient ranges from 0.33 to 0.85). The neural respiratory drive estimated from both EMGdi signals showed a positive trend with an increase of the inspiratory load and being higher in the conventional EMGdi recording. The respiratory rate comparison between measurements revealed similar values of around 16 breaths per minute. The heart rate comparison showed a root mean error of less than 0.2 beats per minute which increased when incrementing the inspiratory load. In summary, this preliminary work explores the use of wireless devices to record the muscle respiratory activity to derive several physiological parameters. Its use can be an alternative to conventional measuring systems with the advantage of being portable, lightweight, flexible and operating at low energy. This technology can be attractive for medical staff and may have a positive impact in the way healthcare is being delivered.

Keywords: Biomedical monitoring, Electrodes, Medical services, Monitoring, Muscles, Wireless communication, Wireless sensor networks


