Literature DB >> 26890910

Noncontact Capacitive Sensing-Based Locomotion Transition Recognition for Amputees With Robotic Transtibial Prostheses.

Enhao Zheng, Qining Wang.   

Abstract

Recent advancement of robotic transtibial prostheses can restore human ankle dynamics in different terrains. Automatic locomotion transitions of the prosthesis guarantee the amputee's safety and smooth motion. In this paper, we present a noncontact capacitive sensing-based approach for recognizing locomotion transitions of amputees with robotic transtibial prostheses. The proposed sensing system is designed with flexible printed circuit boards which solves the walking instability brought by our previous system when using robotic prosthesis and improves the recognition performance. Six transtibial amputees were recruited and performed tasks of ten locomotion transitions with the robotic prosthesis that we recently constructed. The capacitive sensing system was integrated on the prosthesis and worked in combination with on-prosthesis mechanical sensors. With the cascaded classification method, the proposed system achieved 95.8% average recognition accuracy by support vector machine (SVM) classifier and 94.9% accuracy by quadratic discriminant analysis (QDA) classifier. It could accurately recognize the upcoming locomotion modes from the stance phase of the transition steps. In addition, we proved that adding capacitance signals could significantly reduce recognition errors of the robotic prosthesis in locomotion transition tasks. Our study suggests that the fusion of capacitive sensing system and mechanical sensors is a promising alternative for controlling the robotic transtibial prosthesis.

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Year:  2016        PMID: 26890910     DOI: 10.1109/TNSRE.2016.2529581

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


  9 in total

1.  Motion Intent Recognition in Intelligent Lower Limb Prosthesis Using One-Dimensional Dual-Tree Complex Wavelet Transforms.

Authors:  Min Sheng; Wan-Jun Wang; Ting-Ting Tong; Yuan-Yuan Yang; Hui-Lin Chen; Ben-Yue Su
Journal:  Comput Intell Neurosci       Date:  2021-11-24

Review 2.  Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices-A Systematic Review.

Authors:  Floriant Labarrière; Elizabeth Thomas; Laurine Calistri; Virgil Optasanu; Mathieu Gueugnon; Paul Ornetti; Davy Laroche
Journal:  Sensors (Basel)       Date:  2020-11-06       Impact factor: 3.576

3.  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

4.  A Novel Motion Recognition Method Based on Force Myography of Dynamic Muscle Contractions.

Authors:  Xiangxin Li; Yue Zheng; Yan Liu; Lan Tian; Peng Fang; Jianglang Cao; Guanglin Li
Journal:  Front Neurosci       Date:  2022-01-13       Impact factor: 4.677

5.  Intelligent Error Correction of College English Spoken Grammar Based on the GA-MLP-NN Algorithm.

Authors:  Yining Du
Journal:  Comput Intell Neurosci       Date:  2021-12-22

6.  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

7.  Forearm Motion Recognition With Noncontact Capacitive Sensing.

Authors:  Enhao Zheng; Jingeng Mai; Yuxiang Liu; Qining Wang
Journal:  Front Neurorobot       Date:  2018-07-27       Impact factor: 2.650

8.  ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses.

Authors:  Huong Thi Thu Vu; Felipe Gomez; Pierre Cherelle; Dirk Lefeber; Ann Nowé; Bram Vanderborght
Journal:  Sensors (Basel)       Date:  2018-07-23       Impact factor: 3.576

9.  On-board Training Strategy for IMU-Based Real-Time Locomotion Recognition of Transtibial Amputees With Robotic Prostheses.

Authors:  Dongfang Xu; Qining Wang
Journal:  Front Neurorobot       Date:  2020-10-22       Impact factor: 2.650

  9 in total

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