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Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU

by 1,2, 1,2,*, 3 and 2,3,*
1
The School of Mechanical Science and Aerospace Engineering, Jilin University, Changchun 130000, China
2
Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130000, China
3
The School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK
*
Authors to whom correspondence should be addressed.
Sensors 2021, 21(2), 526; https://doi.org/10.3390/s21020526
Received: 7 December 2020 / Revised: 7 January 2021 / Accepted: 11 January 2021 / Published: 13 January 2021
(This article belongs to the Section Wearables)
Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man–machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness. View Full-Text
Keywords: wearable robotic system; locomotion mode recognition; inertial measurement unit (IMU); decision tree structure (DTS) wearable robotic system; locomotion mode recognition; inertial measurement unit (IMU); decision tree structure (DTS)
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MDPI and ACS Style

Han, Y.; Liu, C.; Yan, L.; Ren, L. Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU. Sensors 2021, 21, 526. https://doi.org/10.3390/s21020526

AMA Style

Han Y, Liu C, Yan L, Ren L. Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU. Sensors. 2021; 21(2):526. https://doi.org/10.3390/s21020526

Chicago/Turabian Style

Han, Yang; Liu, Chunbao; Yan, Lingyun; Ren, Lei. 2021. "Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU" Sensors 21, no. 2: 526. https://doi.org/10.3390/s21020526

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