SVM for estimation of wrist angle from sonomyography and SEMG signals

Jun Shi, Yongping Zheng, Zhuangzhi Yan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

The skeletal muscle plays a very important role in the human movement. Surface electromyography (SEMG) is a very useful tool for the functional assessment of skeletal muscles, while sonography has been commonly used to detect its morphological information. We named the signal about the muscle morphological changes derived from ultrasound as sonomyography (SMG). In this study, the ultrasound image, SEMG signals were synchronously sampled from the extensor carpi radialis muscle together with the wrist angle during the whole process of wrist extension and flexion. A Support Vector Machine (SVM) algorithm was used to estimate the wrist angle with the muscle deformation SMG and root mean square of SEMG signals as inputs. The overall mean correlation coefficient value was 0.96 ± 0.02, the mean standard root mean square error was 7.26 ± 1.98, and the root mean square difference was 0.16 ± 0.03. The results demonstrated that the wrist angle could be well estimated by combining the SMG and SEMG signals with SVM algorithm. The combination of SMG and SEMG could provide more comprehensive information to study skeletal muscle.
Original languageEnglish
Title of host publication29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
Pages4806-4809
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2007
Event29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07 - Lyon, France
Duration: 23 Aug 200726 Aug 2007

Conference

Conference29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07
CountryFrance
CityLyon
Period23/08/0726/08/07

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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