The theoretical development of a multichannel time-series myoprocessor for simultaneous limb function detection and muscle force estimation.

This paper details the theoretical development and simulation of a complete time-series myoprocessor which provides reliable and economical predictions of both the magnitude and direction of limb motion from the spectral content of the surface EMG. Treating multiple channels of surface EMG as a vector-valued autoregressive process incorporates spatially distributed information which extends the operating range of parallel filtering limb function classifiers and reduces their sensitivity to modeling conditions. Active joint moment is estimated simultaneously from the pooled variance of the prewhitened EMG generated during the classification procedure. Estimation from the prewhitened sequence imposes no additional computational requirements and extends optimal myoprocessors to include multiple channels of serially dependent data. Such a system may be applied to the control of actively powered prostheses or orthoses.