Electromyogram (EMG) signal-based gait phase recognition for walking-assist devices warrants much attention in human-centered system design as it well exemplifies human-in-the-loop control where the system's prediction directly affects subsequent walking motion. Since walking motion poses considerable variations in electrode placement, performance reliability of such systems is contingent on a combination of electrode montage and a feature extraction method that takes into account underlying physiological factors of peripheral muscles where electrodes are placed. In many practical applications, however, proper consideration of effects of the electrode location variation on performance reliability of the system has received scant empirical attention. Here, based on a user-centered design principle, we establish a gait phase recognition system that is capable of rigidly controlling ill effects due to this covariate by carrying out a large-scale analysis that combines statistical, model-based, and empirical approaches. In doing so, we have developed a special sensing suit for the control of electrode placement and a reliable data acquisition. We then have conducted a nonparametric statistical analysis on class separability values of thirty types of EMG feature sets, followed by a model-based analysis to address the tradeoff between class separability and dimensionality. To further address the issue of how these results generalize to independent systems and data sets, we have carried out an empirical performance assessment over six classification methods. First, the two feature types, Integral of Absolute Value and Histogram, and a combination of the two are shown to be robust against electrode location variations while providing a firm performance guarantee. Second, system organization scenarios are presented on a case-by-case basis, allowing us to trade off system complexity for on-line adaptation capability. Collectively, our integrated analysis lends itself to formulating a guideline for design of highly reliable EMG signal-based walking assistant systems in a variety of smart home scenarios.
Note to Practitioners-This paper addresses the problem of location variation of EMG electrodes in walking assist sysctems. Existing systems lacking consideration of this issue do not guarantee reliable performance. This paper establishes a user-centered design principle of a gait phase recognition system that controls for this covariate. For this, we develop a special sensing suit allowing for the control of electrode placement and a reliable data acquisition. We them combine a statistical, model-based, and empirical analysis to quantify the effect of electrode location variation on the prediction power of thirty types of EMG feature sets. The experimental results suggest that this approach is capable of identifying reliable EMG feature types that are robust against electrode location variation, as well as providing case-by-case system organization scenarios intended for the application of EMG signal-based walking assist systems to various types of smart homes. In future work, it would be beneficial to apply this principle to practical problems for building EMG signal-based devices, such as clinical assistive devices and walking assist devices for the elderly.