A simulation method for the firing sequences of motor units

User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control

OBJECTIVE

Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses.

APPROACH

In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees.

MAIN RESULTS

It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance.

SIGNIFICANCE

These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.

A computational model and simulation study of the efferent activity in the brachial nerves during voluntary motor intent

Inherent limitations of the surface myoelectric signal, such as the lack of recording sites in high-level amputations, and the sensitivity to placement and impedance effects, confound its wider application in powered prostheses. Since a functionally topographic distribution (somatotopic organization) of nerve fascicles exists within the peripheral nerves, it is theoretically possible that complete motor control information can be retrieved from peripheral nerve signals. In this study, we present a computational model that simulates the recording from specific nerve fascicles in the upper limb during voluntary contractions while they innervate relevant muscles. A procedure of classifying the nerve data is presented using a set of time domain features and a spike detection algorithm. Recommendations are made to achieve optimal neural signal recognition, with regard to electrode geometry and signal analysis.