Force myography (FMG) is an alternative to electromyography (EMG) for the control of powered upper limb prostheses. FMG signals originate from deformations of muscles and surrounding tissue applying pressure to a force sensor. FMG-based pattern recognition classifiers have been shown to yield high classification rates. High classification accuracy, however, does not ensure great device usability. Instead, these control systems for prostheses should be evaluated based on real-time usability metrics. In the first section of this work a proportional control algorithm, critical to the completion of the second phase of work, was derived and compared to a mean signal amplitude-based approach. In the second, the real-time usability of high-density force myography (HDFMG) was compared to that of EMG in a Fitts’ Law virtual target acquisition task. FMG was found to significantly outperform EMG in throughput for both classification (0.901±0.357 bits/s versus 0.751±0.309 bits/s) and regression (0.871±0.325 bits/s versus 0.689±0.269 bits/s) control types. The evaluated regression-based proportional control algorithm also performed significantly better (ρ<0.001) than a standard mean signal amplitude-based approach. Subsequent data collection from an amputee subject achieved comparable classification accuracy to the able-bodied participants, but an R2 correlation coefficient of only 0.375 for regression based proportional control, significantly (ρ<0.001) lower than the able-bodied results. This work provides a comparison between the real-time usability of HD-FMG and EMG-based control in both a traditional classification-based pattern recognition scheme, with an additional proportional controller dictating device velocity and a regression-based control scheme. HD-FMG was shown to outperform EMG in both control schemes in both throughput and efficiency.
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