Classification of Hand Motions Using Spatial Information in HDEMG Signals with HOG Features

D. S.V. Bandara, He Chongzaijiao, Jumpei Arata

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

Wearable assistive robotic systems require insight into users' motion intentions to provide intuitive assistance. Bio-signal modalities such as EEG, fNIRS, or sEMG can intercept signals from the nervous system, accessing information related to intended motions. However, technical challenges persist in interpreting the acquired information from these modalities, especially when dealing with a larger number of motions. In such cases, High-Density Electromyography (HDEMG) can offer measurements from a higher number of channels, providing more comprehensive information essential for motion classification. This study proposes a method to comprehend the information contained in HDEMG by analysing spatial changes in muscle activation during various motions. It utilizes histogram of gradient features derived from heatmaps associated with muscle activation. The proposed method aims to classify 12 different hand motions using a support vector machine-based classifier. Results demonstrate an average classification accuracy of 95% through 5-fold cross-validation involving 8 subjects. The high accuracy showcases the effectiveness of utilizing spatial variations in muscle activity to estimate human motion intention using HDEMG-based methods, particularly in potential robotic applications.

本文言語英語
ホスト出版物のタイトル2024 16th International Conference on Computer and Automation Engineering, ICCAE 2024
出版社Institute of Electrical and Electronics Engineers Inc.
ページ368-372
ページ数5
ISBN(電子版)9798350370058
DOI
出版ステータス出版済み - 2024
イベント16th International Conference on Computer and Automation Engineering, ICCAE 2024 - Hybrid, Melbourne, オーストラリア
継続期間: 3月 14 20243月 16 2024

出版物シリーズ

名前2024 16th International Conference on Computer and Automation Engineering, ICCAE 2024

会議

会議16th International Conference on Computer and Automation Engineering, ICCAE 2024
国/地域オーストラリア
CityHybrid, Melbourne
Period3/14/243/16/24

!!!All Science Journal Classification (ASJC) codes

  • 人工知能
  • コンピュータ サイエンスの応用
  • ハードウェアとアーキテクチャ
  • 情報システムおよび情報管理
  • 制御およびシステム工学
  • 制御と最適化

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