Walking Phase Recognition for People with Lower Limb Disability

Lee, Sang Wan; Yi, Taeyoub; Han, Jong-Seob; Jang, HeeJin; Kim, Heon-Hui; Jung, Jin‐Woo; Bien, Zeungnam

doi:10.1109/icorr.2007.4428407

Cited by 6 publications

(2 citation statements)

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“…Many researchers have adopted surface electromyogram (sEMG) signals to recognize gait for the control of lower-limb exoskeletons (or prostheses) [7]. Due to their capability for of revealing the inner workings of the human motor nerve system, sEMG signals can directly reflect the subject's intended activity, and the motor function instruction information of the neuromuscular system [8]. In addition to the convenience and non-invasive nature of the signal acquisition, sEMG signals are also widely used as a neural control source for human-machine interaction, such as the control of lower-limb exoskeletons (or prostheses).…”

Section: Instructionmentioning

confidence: 99%

“…There are several practical limitations for sEMGs for use in gait recognition for the control of exoskeletons and prostheses. Lee et al [8] demonstrated that performance in gait recognition is not guaranteed as a result of the influence of the variable environmental factors. The most common variable factors include different lower-limb positions [11,12], sEMG signal instability [13], displacement of the electrode [14], and so on; moreover, load variation is a problem that cannot be ignored.…”

Section: Instructionmentioning

confidence: 99%

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Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals

Zhang

Sun

et al. 2018

Applied Sciences

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As lower-limb exoskeleton and prostheses are developed to become smarter and to deploy man–machine collaboration, accurate gait recognition is crucial, as it contributes to the realization of real-time control. Many researchers choose surface electromyogram (sEMG) signals to recognize the gait and control the lower-limb exoskeleton (or prostheses). However, several factors still affect its applicability, of which variation in the loads is an essential one. This study aims to (1) investigate the effect of load variation on gait recognition; and to (2) discuss whether a lower-limb exoskeleton control system trained by sEMG from different loads works well in multi-load applications. In our experiment, 10 male college students were selected to walk on a treadmill at three different speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h) with four different loads (L0 = 0, L20 = 20%, L30 = 30%, L40 = 40% of body weight, respectively), and 50 gait cycles were performed. Back propagation neural networks (BPNNs) were used for gait recognition, and a support vector machine (SVM) and k-nearest neighbor (k-NN) were used for comparison. The result showed that (1) load variation has significant effects on the accuracy of gait recognition (p < 0.05) under the three speeds when the loads range in L0, L20, L30, or L40, but no significant impact is found when the loads range in L0, L20, or L30. The least significant difference (LSD) post hoc, which can explore all possible pair-wise comparisons of means that comprise a factor using the equivalent of multiple t-tests, reveals that there is a significant difference between the L40 load and the other three loads (L0, L20, L30), but no significant difference was found among the L0, L20, and L30 loads. The total mean accuracy of gait recognition of the intra-loads and inter-loads was 91.81%, and 69.42%, respectively. (2) When the training data was taken from more types of loads, a higher accuracy in gait recognition was obtained at each speed, and the statistical analysis shows that there was a substantial influence for the kinds of loads in the training set on the gait recognition accuracy (p < 0.001). It can be concluded that an exoskeleton (or prosthesis) control system that is trained in a single load or the parts of loads is insufficient in the face of multi-load applications.

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Section: Instructionmentioning

confidence: 99%