Classification of Upper Limb Motions from Around-Shoulder Muscle Activities: Hand Biofeedback

Jose González*, Yuse Horiuchi, Wenwei Yu
Department of Medical System Engineering, Chiba University, Chiba, Japan

Article Metrics

CrossRef Citations:
Total Statistics:

Full-Text HTML Views: 3345
Abstract HTML Views: 1984
PDF Downloads: 236
Total Views/Downloads: 5565
Unique Statistics:

Full-Text HTML Views: 1393
Abstract HTML Views: 1007
PDF Downloads: 143
Total Views/Downloads: 2543

© González et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Medical System Engineering, Chiba University, Chiba, Japan; Tel: +81-43-290-3231; Fax: +81-43-290-3231; E-mail:


Mining information from EMG signals to detect complex motion intention has attracted growing research attention, especially for upper-limb prosthetic hand applications. In most of the studies, recordings of forearm muscle activities were used as the signal sources, from which the intention of wrist and hand motions were detected using pattern recognition technology. However, most daily-life upper limb activities need coordination of the shoulder-arm-hand complex, therefore, relying only on the local information to recognize the body coordinated motion has many disadvantages because natural continuous arm-hand motions can’t be realized. Also, achieving a dynamical coupling between the user and the prosthesis will not be possible. This study objective was to investigate whether it is possible to associate the around-shoulder muscles’ Electromyogram (EMG) activities with the different hand grips and arm directions movements. Experiments were conducted to record the EMG of different arm and hand motions and the data were analyzed to decide the contribution of each sensor, in order to distinguish the arm-hand motions as a function of the reaching time. Results showed that it is possible to differentiate hand grips and arm position while doing a reaching and grasping task. Also, these results are of great importance as one step to achieve a close loop dynamical coupling between the user and the prosthesis.

Keywords: Data mining, classification, upper limb motions, EMG, around shoulder, neural networks, body coordination, dynamical coupling, continuous motion, reaching and grasping, prosthetics.