{"title":"Muscle Deformation Sensing for Swimming Mode Identification and Continuous Phase Estimation With Two-Stage Network","authors":"Yuchao Liu;Jiajie Guo;Chuxuan Guo;Zijie Liu;Yiran Tong;Xuan Wu;Qining Wang;Caihua Xiong","doi":"10.1109/TIM.2025.3580898","DOIUrl":null,"url":null,"abstract":"Accurate recognition of human motion modes and continuous phases is crucial to exoskeleton control to provide proper assistance. However, harsh underwater environments severely restrict the study on swimming motion monitoring, where existing studies either focus on a single swimming mode or discrete phases, limiting underwater exoskeleton control. To address this limitation, this article develops a two-stage network (TSN) consisting of one mode classifier (first stage) and four phase regressors (second stage), where muscle deformation features are used instead of traditional joint kinematics. Swimming tests are conducted on nine subjects with four modes at three frequencies. The effectiveness of the proposed method is justified by mode identification accuracy of 99.72% and phase estimation error of 3.92%, where the error is 52.89% smaller than that in the traditional time-based estimation (TBE) method. This article is the first to simultaneously recognize the swimming mode and the continuous phase, which is valuable to adapt the smooth exoskeleton assistance to harsh underwater environment and multimodal motion scenarios.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.9000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11040031/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate recognition of human motion modes and continuous phases is crucial to exoskeleton control to provide proper assistance. However, harsh underwater environments severely restrict the study on swimming motion monitoring, where existing studies either focus on a single swimming mode or discrete phases, limiting underwater exoskeleton control. To address this limitation, this article develops a two-stage network (TSN) consisting of one mode classifier (first stage) and four phase regressors (second stage), where muscle deformation features are used instead of traditional joint kinematics. Swimming tests are conducted on nine subjects with four modes at three frequencies. The effectiveness of the proposed method is justified by mode identification accuracy of 99.72% and phase estimation error of 3.92%, where the error is 52.89% smaller than that in the traditional time-based estimation (TBE) method. This article is the first to simultaneously recognize the swimming mode and the continuous phase, which is valuable to adapt the smooth exoskeleton assistance to harsh underwater environment and multimodal motion scenarios.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.