Literature DB >> 28320673

IMU-Based Wrist Rotation Control of a Transradial Myoelectric Prosthesis.

Daniel A Bennett, Michael Goldfarb.   

Abstract

This paper describes a control method intended to facilitate improved control of a myoelectric prosthesis containing a wrist rotator. Rather than exclusively utilizing electromyogram (EMG) for the control of all myoelectric components (e.g., a hand and a wrist), the proposed controller utilizes inertial measurement (from six-axis inertial measurement unit (IMU)) to sense upper arm abduction/adduction, and uses this input to command a wrist rotation velocity. As such, the controller essentially substitutes shoulder abduction/adduction in place of agonist/antagonist EMG to control wrist angular velocity, which preserves EMG for control of the hand (or other arm components). As a preliminary assessment of efficacy, the control method was implemented on a transradial prosthesis prototype with a powered wrist rotator and hand, and experimentally assessed on five able-bodied subjects who wore the prototype using an able-bodied adaptor and one transradial amputee subject while performing assessments representative of activities of daily living. The assessments compared the (timed) performance of the combined EMG/ IMU-based control method with a (conventional) sequential EMG control approach. Results of the assessment indicate that the able-bodied subjects were able to perform the tasks 33% faster on average with the EMG/IMU-based method, relative to a conventional sequential EMG method. The same assessment was subsequently conducted using a single transradial amputee subject, which resulted in similar performance trends, although with a somewhat lessened effect size-specifically, the amputee subject was on average 22% faster in performing tasks with the IMU-based controller.

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Year:  2017        PMID: 28320673     DOI: 10.1109/TNSRE.2017.2682642

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


  6 in total

1.  Effective recognition of human lower limb jump locomotion phases based on multi-sensor information fusion and machine learning.

Authors:  Yanzheng Lu; Hong Wang; Fo Hu; Bin Zhou; Hailong Xi
Journal:  Med Biol Eng Comput       Date:  2021-03-21       Impact factor: 2.602

2.  Design and Assessment of Control Maps for Multi-Channel sEMG-Driven Prostheses and Supernumerary Limbs.

Authors:  Michele Maimeri; Cosimo Della Santina; Cristina Piazza; Matteo Rossi; Manuel G Catalano; Giorgio Grioli
Journal:  Front Neurorobot       Date:  2019-05-29       Impact factor: 2.650

3.  Improving bimanual interaction with a prosthesis using semi-autonomous control.

Authors:  Robin Volkmar; Strahinja Dosen; Jose Gonzalez-Vargas; Marcus Baum; Marko Markovic
Journal:  J Neuroeng Rehabil       Date:  2019-11-14       Impact factor: 4.262

4.  fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees.

Authors:  Neelum Yousaf Sattar; Zareena Kausar; Syed Ali Usama; Umer Farooq; Muhammad Faizan Shah; Shaheer Muhammad; Razaullah Khan; Mohamed Badran
Journal:  Sensors (Basel)       Date:  2022-01-18       Impact factor: 3.576

5.  Towards Dynamic Multi-Modal Intent Sensing Using Probabilistic Sensor Networks.

Authors:  Joseph Russell; Jeroen H M Bergmann; Vikranth H Nagaraja
Journal:  Sensors (Basel)       Date:  2022-03-29       Impact factor: 3.576

6.  A novel framework for designing a multi-DoF prosthetic wrist control using machine learning.

Authors:  Chinmay P Swami; Nicholas Lenhard; Jiyeon Kang
Journal:  Sci Rep       Date:  2021-07-22       Impact factor: 4.379

  6 in total

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