Gait event and user intention detection for FES-control: selecting sensors

The authors follow a design method for event detectors using the ID3 rule induction algorithm. Rule induction was chosen mainly for two reasons: it ranks the relative importance of sensor signal attribute in detecting an event and, secondly, the reasoning of the algorithm may be understood by humans since the rules are organized in the familiar form of decision tree consisting of IF(...) THEN(...) ELSE(...) statements. This method allows the control system designer the freedom to position a set of available sensors in unobtrusive locations, such as braces, walking aids or the waistband, and operate them in less demanding environments. Furthermore, the method does not require a high level of intuition as to the contribution that each sensor makes to the detection of an event. Indeed, it has been shown that human experts perform poorly relative to the algorithm in ranking the importance of the sensors (C.A. Kirkwood, and B.J. Andrews, Proc. 11th IEEE EMBS Conf., Seattle, USA, p. 1020-1, 1989). Here, the authors describe a procedure in which a reliable event detector/predictor can be developed with a minimum of sensors. It will mimic a paraplegic's skill in using hand switches to control a simple FES walking system, i.e. it will signal the users implicit intention. This example of skill cloning follows that previously described (Kirkwood and Andrews, 1989).