Muscle twitch responses for shaping the multi-pad electrode for functional electrical stimulation

In this paper we present a method for optimization of multi-pad electrode spatial selectivity during transcutaneous Functional Electrical Stimulation (FES) of hand. The presented method is based on measurement of individual muscle twitch responses during low frequency electrical stimulation via pads within multi-pad electrode. Twitch responses are recorded by Micro-Electro-Mechanical Systems (MEMS) accelerometers. The aim of this methodology is to substitute bulky sensors, torque sensors and goniometers, in multi-pad electrode optimization algorithm with smaller and lighter sensors; therefore making multi-pad stimulation suitable for daily use. Additionally we present method for minimizing number of MEMS accelerometers, which relies on characteristic waveforms of joint acceleration during wrist or fingers flexion/extension. These signals can be used to train Artificial Neural Network (ANN) to distinguish between different waveform classes and define correlation of each pad and activated muscle beneath. Results presented in this paper show high agreement of goniometers based classification and accelerometers based classification. As for classification with minimized number of sensors (one accelerometer) our ANN backed algorithm achieved high degree of accurate classification in intra-subject testing, but lower performance in inter-subject testing.