A preliminary study of classification of upper limb motions and forces based on mechanomyography. (July 2020)

• Record Type: Journal Article
• Authors: Zhang, Yue; Xia, Chunming
• Is Part Of: Medical engineering & physics. Volume 81(2020)
• Page Start: 97
• Page End: 104
• Publication Date: 2020-07
• Subjects: Mechanomyography -- Upper limb motion -- Muscle force -- Classification; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2020.05.009
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: A new experiment that involves upper limb motions and muscle forces is designed. The classification of upper limb motions and muscle forces based on mechanomyography is proposed. This study provide a basis for further research in which rehabilitation training is evaluated based on MMG. This study has potential advantages in development on the human-computer interaction system that assist rehabilitation training. Abstract: Rehabilitation training is essential for patients who have a history of certain illnesses, such as stroke. As a crucial part of rehabilitation training, upper limb training involves such key factors as upper limb motions and forces. This study investigated three upper limb motions (elbow flexion of 135°, Motion 1; shoulder flexion of 90°, Motion 2; and shoulder abduction of 90°, Motion 3) and various forces (muscle Force 0, no force; holding one 1.4 kg dumbbell, muscle Force 1; holding one 2.4 kg dumbbell, muscle Force 2) in combination to evaluate nine motion patterns. These patterns were completed by twelve healthy volunteers. Mechanomyography (MMG) measurements of the biceps brachii (Channel 1), triceps (Channel 2), and deltoid (Channel 3) muscles were collected. These were subsequently divided into signal segments corresponding to each of the motions using a segmentation method based on average energy. After extracting time-domain features and wavelet packet energy features, support vector machine analysis (SVM) was used for the classification of the upper limb motions and forces based on the MMG measurements. Channel 2 and Channel 3 were shown to play an important role in the classification of upper limb motions, and Channel 1 played a role in the classification of the forces. These results demonstrate that collection of MMG measurements from the three muscles is feasible and suggest a foundation for further studies in which rehabilitation training is evaluated based on MMG measurements.