| dc.description.abstract |
A remaining barrier to the clinical accessibility of pattern recognition systems is
the lack of practical methods to acquire the myoelectric signals required to train
the system. Many current methods involve screen-guided training (SGT), where wearers
connected to an external computer perform muscle contractions synchronized with a
sequence of visual cues. The system complexity prevents easy retraining when signal
conditions change. We have developed a method called prosthesis-guided training (PGT),
where the prosthesis itself provides the cues by moving through a sequence of preprogrammed
motions; screen prompting and external connections are eliminated. Five prosthesis
wearers performed a repetitive clothespin placement task using pattern recognition
control. Wearers demonstrated similar baseline functionality between systems trained
with PGT (10 ± 4 clothespins) and SGT (12 ± 7 clothespins) (p = 0.56). To investigate
the efficacy of PGT retraining, real-world issues (e.g. broken wires, external noise)
were simulated to accelerate control degradation. Sessions ended when wearers indicated
loss of functional control. On average, wearers maintained function through two malfunctioning
inputs, placing 48 ± 17 clothespins in 31.6 ± 16.2 minutes when allowed to retrain
using PGT. These results suggest that PGT acquires adequate training data and may
enable longer-lasting functional use, potentially increasing prosthesis wear time
and reducing device rejection.
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