Table 2.
Task | Master finger(s) |
n | c | F | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Fn | I | 1 | [ 1 0 0 0 | 0 0 0 0]T | [ 44.8 | 6.63 | 1.40 | 2.34 | −0.01 | −1.75 | −0.64 | −1.01 ]T |
M | 1 | [ 0 1 0 0 | 0 0 0 0] T | [ 11.6 | 38.9 | 13.2 | 1.81 | 4.71 | −3.62 | −3.62 | −1.25 ]T | |
R | 1 | [ 0 0 1 0 | 0 0 0 0] T | [ 3.08 | 15.8 | 27.5 | 9.22 | 0.64 | 3.64 | −4.39 | −6.35 ]T | |
L | 1 | [ 0 0 0 1 | 0 0 0 0] T | [ 2.13 | 5.41 | 15.0 | 24.4 | −0.09 | 1.71 | −0.55 | −5.84 ]T | |
IMRL | 4 | [ 1 1 1 1 | 0 0 0 0] T | [ 22.9 | 28.6 | 22.1 | 16.9 | 4.53 | −1.45 | −4.30 | −6.55 ]T | |
(Ft)r | I | 1 | [ 0 0 0 0 | −1 0 0 0] T | [ 9.37 | 2.71 | 0.77 | 2.25 | −26.7 | −5.82 | −1.54 | −8.01 ]T |
M | 1 | [ 0 0 0 0 | 0 −1 0 0] T | [ 1.91 | 13.4 | 1.78 | 1.47 | −7.04 | −25.4 | −8.01 | −4.67 ]T | |
R | 1 | [ 0 0 0 0 | 0 0 −1 0] T | [ 1.13 | 5.82 | 7.15 | 3.48 | −2.16 | −14.0 | −13.3 | −9.69 ]T | |
L | 1 | [ 0 0 0 0 | 0 0 0 −1] T | [ 1.34 | 3.09 | 4.14 | 7.97 | −1.75 | −7.40 | −9.07 | −19.5 ]T | |
IMRL | 4 | [ 0 0 0 0 | −1 −1 −1 −1] T | [ 9.66 | 6.94 | 4.52 | 5.83 | −15.8 | −20.9 | −11.2 | −16.3 ]T | |
(Ft)u | I | 1 | [ 0 0 0 0 | 1 0 0 0] T | [ 12.8 | 2.69 | 2.16 | 2.71 | 26.9 | 9.29 | 2.74 | 0.92 ]T |
M | 1 | [ 0 0 0 0 | 0 1 0 0] T | [ 5.39 | 9.30 | 4.66 | 2.56 | 11.6 | 19.0 | 6.93 | 2.48 ]T | |
R | 1 | [ 0 0 0 0 | 0 0 1 0] T | [ 2.81 | 5.91 | 4.63 | 4.86 | 4.71 | 13.0 | 10.1 | 5.60 ]T | |
L | 1 | [ 0 0 0 0 | 0 0 0 1] T | [ 2.14 | 0.97 | 1.73 | 10.0 | 2.77 | 5.03 | 4.59 | 17.1 ]T | |
IMRL | 4 | [ 0 0 0 0 | 1 1 1 1] T | [ 12.2 | 8.28 | 3.80 | 10.4 | 19.6 | 16.0 | 8.18 | 9.83 ]T |
Radial and ulnar deviation are denoted by (Ft)r and (Ft)u, respectively. The number of master fingers is indicated by n, and the master finger(s) force performance is highlighted for each task. The goal of the neural network is to map inputs c to outputs F by minimizing the MSE of the predictions of Eq. 6
T Transpose