Hamza Al Kouzbary, Mouaz Al Kouzbary, Jingjing Liu, Taha Khamis, Nooranida Arifin, Hamam Mokayed, Noor Azuan Abu Osman
{"title":"基于gru的动力假肢足部位置预测的方差分析和线性回归特征选择。","authors":"Hamza Al Kouzbary, Mouaz Al Kouzbary, Jingjing Liu, Taha Khamis, Nooranida Arifin, Hamam Mokayed, Noor Azuan Abu Osman","doi":"10.1080/10255842.2025.2558026","DOIUrl":null,"url":null,"abstract":"<p><p>This study evaluates feature selection using ANOVA and Linear Regression to optimize GRU-based models for predicting foot position in powered prostheses across varied terrains. Kinematic data from ten healthy participants during walking, stair ascend/descend, and standing were processed in MATLAB. Selected features, compared with Recursive Feature Elimination, trained GRU networks on mixed datasets and were tested on independent subjects. Results showed ANOVA and regression efficiently selected features with reduced computation and comparable performance. The GRU achieved RMSE as low as 0.066 radians, demonstrating robust generalization. While promising, clinical validation on amputee subjects remains necessary.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-18"},"PeriodicalIF":1.6000,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ANOVA and linear regression feature selection for GRU-based foot position prediction in powered prostheses.\",\"authors\":\"Hamza Al Kouzbary, Mouaz Al Kouzbary, Jingjing Liu, Taha Khamis, Nooranida Arifin, Hamam Mokayed, Noor Azuan Abu Osman\",\"doi\":\"10.1080/10255842.2025.2558026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study evaluates feature selection using ANOVA and Linear Regression to optimize GRU-based models for predicting foot position in powered prostheses across varied terrains. Kinematic data from ten healthy participants during walking, stair ascend/descend, and standing were processed in MATLAB. Selected features, compared with Recursive Feature Elimination, trained GRU networks on mixed datasets and were tested on independent subjects. Results showed ANOVA and regression efficiently selected features with reduced computation and comparable performance. The GRU achieved RMSE as low as 0.066 radians, demonstrating robust generalization. While promising, clinical validation on amputee subjects remains necessary.</p>\",\"PeriodicalId\":50640,\"journal\":{\"name\":\"Computer Methods in Biomechanics and Biomedical Engineering\",\"volume\":\" \",\"pages\":\"1-18\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2025-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Methods in Biomechanics and Biomedical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/10255842.2025.2558026\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Methods in Biomechanics and Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10255842.2025.2558026","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
ANOVA and linear regression feature selection for GRU-based foot position prediction in powered prostheses.
This study evaluates feature selection using ANOVA and Linear Regression to optimize GRU-based models for predicting foot position in powered prostheses across varied terrains. Kinematic data from ten healthy participants during walking, stair ascend/descend, and standing were processed in MATLAB. Selected features, compared with Recursive Feature Elimination, trained GRU networks on mixed datasets and were tested on independent subjects. Results showed ANOVA and regression efficiently selected features with reduced computation and comparable performance. The GRU achieved RMSE as low as 0.066 radians, demonstrating robust generalization. While promising, clinical validation on amputee subjects remains necessary.
期刊介绍:
The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.