Prediction of wrist angle from sonomyography signals with artificial neural networks technique

Jun Shi, Yongping Zheng, Zhuangzhi Yan

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

8 Citations (Scopus)

Abstract

Surface electromyography (SEMG) is widely used for the functional assessment of skeletal muscles, while sonography has been commonly used to detect its morphological information. We defined the signal about the continuous change of the morphological parameters of muscles detected by ultrasound as sonomyography (SMG). In this study, we continuously sampled the ultrasound image, SEMG signals on the extensor carpi radialis muscle together with the wrist angle simultaneously during the whole process of wrist extension and flexion from 7 normal subjects. A three-layer feed-forward artificial neural network with BP learning algorithm was used to predict the wrist angle with the muscle deformation SMG and root mean square of SEMG signals as inputs. The overall mean R" value was 0.96 ± 0.02, the mean standard root mean square error was 7.26 ± 1.98, and the mean relative root mean square errors was 0.160 ± 0.037. The results demonstrated that the wrist angle could be well predicted by combining the SMG and SEMG signals with ANN. Our result suggested that the combination of the information of SMG and SEMG could provide more comprehensive assessment of the skeletal muscle.
Original languageEnglish
Title of host publication28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages3549-3552
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2006
Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
Duration: 30 Aug 20063 Sep 2006

Conference

Conference28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
CountryUnited States
CityNew York, NY
Period30/08/063/09/06

ASJC Scopus subject areas

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

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