Literature DB >> 32286999

IMU-Based Locomotion Mode Identification for Transtibial Prostheses, Orthoses, and Exoskeletons.

Fei Gao, Gaoyu Liu, Fengyan Liang, Wei-Hsin Liao.   

Abstract

Active transtibial prostheses, orthoses, and exoskeletons hold the promise of improving the mobility of lower-limb impaired or amputated individuals. Locomotion mode identification (LMI) is essential for these devices precisely reproducing the required function in different terrains. In this study, a terrain geometry-based LMI algorithm is proposed. The environment should be built according to the inclination grade of the ground. For example, when the inclination angle is between 7 degrees and 15 degrees, the environment should be a ramp. If the inclination angle is around 30 degrees, the environment is preferred to be equipped with stairs. Given that, the locomotion mode/terrain can be classified by the inclination grade. Besides, human feet always move along the surface of terrain to minimize the energy expenditure for transporting lower limbs and get the required foot clearance. Hence, the foot trajectory estimated by an IMU was used to derive the inclination grade of the terrain that we traverse to identify the locomotion mode. In addition, a novel trigger condition (an elliptical boundary) is proposed to activate the decision-making of the LMI algorithm before the next foot strike thus leaving enough time for performing preparatory work in the swing phase. When the estimated foot trajectory goes across the elliptical boundary, the decision-making will be executed. Experimental results show that the average accuracy for three healthy subjects and three below-knee amputees is 98.5% in five locomotion modes: level-ground walking, up slope, down slope, stair descent, and stair ascent. Besides, all the locomotion modes can be identified before the next foot strike.

Entities:  

Mesh:

Year:  2020        PMID: 32286999     DOI: 10.1109/TNSRE.2020.2987155

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  3 in total

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

Authors:  Yang Han; Chunbao Liu; Lingyun Yan; Lei Ren
Journal:  Sensors (Basel)       Date:  2021-01-13       Impact factor: 3.576

2.  Locomotion Mode Recognition Algorithm Based on Gaussian Mixture Model Using IMU Sensors.

Authors:  Dongbin Shin; Seungchan Lee; Seunghoon Hwang
Journal:  Sensors (Basel)       Date:  2021-04-15       Impact factor: 3.576

3.  Locomotion Mode Recognition with Inertial Signals for Hip Joint Exoskeleton.

Authors:  Gang Du; Jinchen Zeng; Cheng Gong; Enhao Zheng
Journal:  Appl Bionics Biomech       Date:  2021-05-24       Impact factor: 1.781

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.