A Low-Cost End-to-End sEMG-Based Gait Sub-Phase Recognition System.
IEEE Trans Neural Syst Rehabil Eng
; 28(1): 267-276, 2020 01.
Article
in En
| MEDLINE
| ID: mdl-31675333
ABSTRACT
As surface electromyogram (sEMG) signals have the ability to detect human movement intention, they are commonly used to be control inputs. However, gait sub-phase classification typically requires monotonous manual labeling process, and commercial sEMG acquisition devices are quite bulky and expensive, thus current sEMG-based gait sub-phase recognition systems are complex and have poor portability. This study presents a low-cost but effective end-to-end sEMG-based gait sub-phase recognition system, which contains a wireless multi-channel signal acquisition device simultaneously collecting sEMG of thigh muscles and plantar pressure signals, and a novel neural network-based sEMG signal classifier combining long-short term memory (LSTM) with multilayer perceptron (MLP). We evaluated the system with subjects walking under five conditions flat terrain at 5 km/h, flat terrain at 3 km/h, 20 kg backpack at 5 km/h, 20 kg shoulder bag at 5 km/h and 15° slope at 5 km/h. Experimental results show that the proposed method achieved average classification accuracies of 94.10%, 87.25%, 90.71%, 94.02%, and 87.87%, respectively, which were significantly higher than existing recognition methods. Additionally, the proposed system had a good real-time performance with low average inference time in the range of 3.25 ~ 3.31 ms.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Electromyography
/
Gait
Type of study:
Health_economic_evaluation
/
Prognostic_studies
Limits:
Adult
/
Humans
/
Male
Language:
En
Journal:
IEEE Trans Neural Syst Rehabil Eng
Journal subject:
ENGENHARIA BIOMEDICA
/
REABILITACAO
Year:
2020
Document type:
Article