Article

Several multiple degree-of-freedom upper-limb prostheses that have the promise of
highly dexterous control have recently been developed. Inadequate
controllability, however, has limited adoption of these devices. Introducing more
robust control methods will likely result in higher acceptance rates. This work
investigates the suitability of using high-density force myography (HD-FMG) for
prosthetic control. HD-FMG uses a high-density array of pressure sensors to
detect changes in the pressure patterns between the residual limb and socket
caused by the contraction of the forearm muscles. In this work, HD-FMG
outperforms the standard electromyography (EMG)-based system in detecting
different wrist and hand gestures. With the arm in a fixed, static position,
eight hand and wrist motions were classified with 0.33% error using the HD-FMG
technique. Comparatively, classification errors in the range of 2.2%-11.3% have
been reported in the literature for multichannel EMG-based approaches. As with
EMG, position variation in HD-FMG can introduce classification error, but
incorporating position variation into the training protocol reduces this effect.
Channel reduction was also applied to the HD-FMG technique to decrease the
dimensionality of the problem as well as the size of the sensorized area. We
found that with informed, symmetric channel reduction, classification error could
be decreased to 0.02%.

Langue : ANGLAIS