S. M. N. Arosha Senanayake, Chong Shen, J. Chong, R. G. Sirisinghe
{"title":"Instrumented Orthopaedics Analysis System","authors":"S. M. N. Arosha Senanayake, Chong Shen, J. Chong, R. G. Sirisinghe","doi":"10.1109/COASE.2006.326879","DOIUrl":null,"url":null,"abstract":"The system built consists of gait analysis system and gait recognition system. Gait analysis system is built attaching force sensing resistors into insole for foot analysis and wireless sensors are attached on ankle, kneecap (patella) and hip (pelvis) for lower extremity. Gait pattern recognition system is constructed using an embedded system with the capability of learning real time gait based on recurrent neural networks. In this investigation a systematic approach is undertaken to quantify the effects of varied interventions of legs and foot on the movement pattern of the lower extremity. Further, the relationship of these kinematics effects and the muscular activity of leg are analyzed. The prototype built is responsible to recognize features of orthopaedics issues of soccer players","PeriodicalId":116108,"journal":{"name":"2006 IEEE International Conference on Automation Science and Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE International Conference on Automation Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COASE.2006.326879","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
The system built consists of gait analysis system and gait recognition system. Gait analysis system is built attaching force sensing resistors into insole for foot analysis and wireless sensors are attached on ankle, kneecap (patella) and hip (pelvis) for lower extremity. Gait pattern recognition system is constructed using an embedded system with the capability of learning real time gait based on recurrent neural networks. In this investigation a systematic approach is undertaken to quantify the effects of varied interventions of legs and foot on the movement pattern of the lower extremity. Further, the relationship of these kinematics effects and the muscular activity of leg are analyzed. The prototype built is responsible to recognize features of orthopaedics issues of soccer players
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