Nikolas Wilhelm, Sami Haddadin, Carina M. Micheler, Jan J. Lang, Florian Hinterwimmer, Victor Schaack, Ricardo Smits, Rainer Burgkart
{"title":"一种具有成本效益的步态分析IMU系统的开发和评估:与Vicon和VideoPose3D算法的比较","authors":"Nikolas Wilhelm, Sami Haddadin, Carina M. Micheler, Jan J. Lang, Florian Hinterwimmer, Victor Schaack, Ricardo Smits, Rainer Burgkart","doi":"10.1515/cdbme-2023-1064","DOIUrl":null,"url":null,"abstract":"Abstract This study aimed to develop and evaluate a costeffective Inertial Measurement Unit (IMU) system for gait analysis, comparing its performance with the Vicon system and the VideoPose3D algorithm. The system comprises five calibrated sensors and a mobile app to measure lower body orientation during gait and stair climbing. Eight healthy participants were involved in the experiment, each performing ten repetitions to analyze hip and knee flexion angles. The IMU system demonstrated significantly lower mean square error than deep learning-based approaches and comparable results to the Vicon system, indicating its potential for clinical and research applications.","PeriodicalId":10739,"journal":{"name":"Current Directions in Biomedical Engineering","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and Evaluation of a Cost-effective IMU System for Gait Analysis: Comparison with Vicon and VideoPose3D Algorithms\",\"authors\":\"Nikolas Wilhelm, Sami Haddadin, Carina M. Micheler, Jan J. Lang, Florian Hinterwimmer, Victor Schaack, Ricardo Smits, Rainer Burgkart\",\"doi\":\"10.1515/cdbme-2023-1064\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This study aimed to develop and evaluate a costeffective Inertial Measurement Unit (IMU) system for gait analysis, comparing its performance with the Vicon system and the VideoPose3D algorithm. The system comprises five calibrated sensors and a mobile app to measure lower body orientation during gait and stair climbing. Eight healthy participants were involved in the experiment, each performing ten repetitions to analyze hip and knee flexion angles. The IMU system demonstrated significantly lower mean square error than deep learning-based approaches and comparable results to the Vicon system, indicating its potential for clinical and research applications.\",\"PeriodicalId\":10739,\"journal\":{\"name\":\"Current Directions in Biomedical Engineering\",\"volume\":\"60 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Directions in Biomedical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1515/cdbme-2023-1064\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Directions in Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/cdbme-2023-1064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
Development and Evaluation of a Cost-effective IMU System for Gait Analysis: Comparison with Vicon and VideoPose3D Algorithms
Abstract This study aimed to develop and evaluate a costeffective Inertial Measurement Unit (IMU) system for gait analysis, comparing its performance with the Vicon system and the VideoPose3D algorithm. The system comprises five calibrated sensors and a mobile app to measure lower body orientation during gait and stair climbing. Eight healthy participants were involved in the experiment, each performing ten repetitions to analyze hip and knee flexion angles. The IMU system demonstrated significantly lower mean square error than deep learning-based approaches and comparable results to the Vicon system, indicating its potential for clinical and research applications.