Literature DB >> 24187229

Non-contact capacitance sensing for continuous locomotion mode recognition: design specifications and experiments with an amputee.

Enhao Zheng, Long Wang, Yimin Luo, Kunlin Wei, Qining Wang.   

Abstract

Locomotion mode recognition plays an important role in the control of powered lower-limb prostheses. In this paper, we present a non-contact capacitance sensing system (C-Sens) to measure the interfacial signals between the residual limb and the prosthetic socket. The system includes sensing front-ends, a sensing circuit, a control circuit and foot pressure insoles. In the proposed system, the electrodes are fixed on the inner surface of the socket, which couple with the human body forming capacitors. The foot pressure insoles are built for detecting gait phases. The data sequence is controlled by the control circuit. To evaluate the capacitance sensing system, experiments with a transtibial amputee are carried out and seven kinds of locomotion modes are recorded. With the continuous phase dependent classification method and the quadratic discriminant analysis (QDA) classifier, the average recognition accuracies are 93.8% and 95.0% for the stance phase and the swing phase respectively. The results show the potential of the proposed system for the control of powered lower-limb prostheses.

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Year:  2013        PMID: 24187229     DOI: 10.1109/ICORR.2013.6650410

Source DB:  PubMed          Journal:  IEEE Int Conf Rehabil Robot        ISSN: 1945-7898


  3 in total

Review 1.  Active lower limb prosthetics: a systematic review of design issues and solutions.

Authors:  Michael Windrich; Martin Grimmer; Oliver Christ; Stephan Rinderknecht; Philipp Beckerle
Journal:  Biomed Eng Online       Date:  2016-12-19       Impact factor: 2.819

2.  Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction.

Authors:  Pingao Huang; Hui Wang; Yuan Wang; Zhiyuan Liu; Oluwarotimi Williams Samuel; Mei Yu; Xiangxin Li; Shixiong Chen; Guanglin Li
Journal:  Comput Math Methods Med       Date:  2020-04-14       Impact factor: 2.238

Review 3.  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 in total

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