1 2,3,4,5,6,7 8,9,10 11,12 13,14,15 16 17 18
20,21,22 23 24 25 14 11 9,10 26 27
28,29
HR HC HO W F W E W P W S U F RF F P KP SG CG 32 w t τ t τ ≤ t
Figure 3. Slinding winding for feature extraction, adopted from31
to a lower statistical variance of features, and therefore, achieve greater classification accuracy32. However, it would induce a longer processing delay.
Incremental interval (t). It determines the bounds allowing the control system to extract feature vectors as well as generate a classification decision. Thus, the computing time of any algorithm should satisfy this demand.
2.4.2 Feature extraction
The most prevalent Hudgins’ TD features, including Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Changes (SSC), in combination with 4-th order autoregressive
coefficients (AR4) are adopted as the baseline feature options. The definition of these sEMG features is defined as below, where N is the sliding window size, and xi is an instant sEMG value at the time point i. MAV is an average of absolute value in the segment, which relatively accurately illustrates the
change trend of the absolute value in sEMG signal. MAV could be defined as
• ZC = N−1! i=1 sgn(−xixi+1), sgn(x) = " 1 x > ε 0 x! ε ε • SSC = N!−1 i=2 f (xi−1, xi, xi+1), f (xi−1, xi, xi+1) = ⎧ ⎨ ⎩ 1 (xi+1− xi)(xi−1− xi) > 0 AN D (|xk+1− xk| > ε OR |xk−1− xk| > ε) 0 else • xk−i ei xk= p ! i aixk−i+ ek, p ai w t N = 256 ε
P (x|i) i P (x|i) = 1 (2π)|x|/2|Σi|1/2exp(− 1 2(x− µi) TΣ−1 i (x− µi))) |x| x µi Σi i g(x, i) =−1 2µ T iΣ−1µi+ xTΣ−1µi+ ln p(ωi) ln p(ωi) ωi ln p(ωi) Σ µi Σi i = 1, 2, ...c c x i g(x, i) > g(x, j) j̸= i 1≤ j, i ≤ c Ci i Ci = 1 i Ci = 0 i &16i=1Ci= N N C1= 0 C2= 1 C3 = 0 C4 = 1 C5 = 0 C6 = 1 C7 = 0 C8 = 1 C9 = 1 C10 = 0 C11 = 1 C12= 1 C13= 0 C14= 0 C15= 0 C16= 1 Channel ID: 0 1 0 1 0 1 0 1 1 0 1 1 0 0 0 1 Chromosome: C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16
p 1 1 0 1 0 1 0 1 1 0 1 0 0 0 0 1 Chromosome A: 0 0 0 1 1 0 1 1 0 1 1 1 1 0 0 0 Chromosome B: 1 1 0 1 0 1 1 1 0 1 1 1 1 0 0 0 Chromosome A+B: Crossover point 0 0 0 1 1 0 0 1 1 0 1 0 0 0 0 1 Chromosome B+A: 1 1 0 1 0 0 1 1 0 1 1 1 1 0 0 0 0 0 0 1 1 0 0 1 1 0 1 1 0 0 0 1 p p Chromosome C: Chromosome C: Crossover Mutation &16 i=1Ci= N & 16 i=1Cinot = N
Chr Chracc Samples, N P revious Chr Chr P revious Chr̸= Chr P revious Chr← Chr Chr p = 0.9 Chr Chr Chracc ◃ Chracc • acc acc = T N, N T • acc
1 3 5 7 9 11 13 15 The number of sEMG channels 0.3 0.4 0.5 0.6 0.7 0.8 0.9
The classification accuracy
Optimally selected channel Random selected channel 97% peak accuracy 90% peak accuracy * * ** * * < < < <
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 The number of sEMG channels
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
The classification accuracy
All Gestures, Optimal Selection All Gestures, Random Selection Basic Gestures, Optimal Selection Basic Gestures, Random Selection Wirst Gestures, Optimal Selection Wrist Gestures, Random Selection Grasp Gestures, Optimal Selection Grasp Gestures, Random Selection
p < 0.05
(1) 3 channels 0 5 10 15 No. of Channels 0 0.2 0.4 0.6 0.8 1 Probability (2) 4 channels 0 5 10 15 No. of Channels 0 0.2 0.4 0.6 0.8 1 Probability (3) 5 channels 0 5 10 15 No. of Channels 0 0.2 0.4 0.6 0.8 1 Probability (4) 6 channels 0 5 10 15 No. of Channels 0 0.2 0.4 0.6 0.8 1 Probability (5) 7 channels 0 5 10 15 No. of Channels 0 0.2 0.4 0.6 0.8 1 Probability (6) The Average 0 5 10 15 No. of Channels 0 0.2 0.4 0.6 0.8 1 Probability
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 The number of sEMG channels
0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85
The classification accuracy
Optimally selection approach Random selection approach
* **
< <
<
33