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. Author manuscript; available in PMC: 2023 Oct 1.
Published in final edited form as: IEEE Trans Robot. 2022 Jun 1;38(5):2841–2857. doi: 10.1109/tro.2022.3170720

Fig. 2.

Fig. 2.

Decoding individual finger and wrist postures. Participants controlled the virtual hand in real-time with the HMM-NB to match 10 postures: flexion of all five fingers (T,I,M,R,S), wrist flexion (WF), finger abduction (Ab), finger adduction (Ad), thumb opposition (TO), and rest (Re). (a) Simulated offline performance during rest and hold periods of P1’s training data (5-fold cross validation, 5–6 repetitions per movement). (b) Online accuracy shows transition errors to cued postures while P1 controlled the virtual hand. (c) Trial latency histograms binned in 50 ms increments and overlaid with the median (dashed line) and middle 50% (shading) of trials (n = 30). Trials with latency greater than a second (>1) are aggregated in the orange rectangle. (d) Offline simulation for P2 using training data from one experiment session. (e-f) Same as above for P2 across three single day sessions (n = 181 trials). Unsuccessful trials (F) are aggregated in the red rectangle.