{"title":"能否利用可穿戴鞋垫设备的压力数据来估算外骨骼控制系统的腰背力矩?","authors":"S. Chae, Ahnryul Choi, Jeehae Kang, J. Mun","doi":"10.3390/act13030092","DOIUrl":null,"url":null,"abstract":"This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist.","PeriodicalId":48584,"journal":{"name":"Actuators","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System?\",\"authors\":\"S. Chae, Ahnryul Choi, Jeehae Kang, J. Mun\",\"doi\":\"10.3390/act13030092\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist.\",\"PeriodicalId\":48584,\"journal\":{\"name\":\"Actuators\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-02-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Actuators\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/act13030092\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Actuators","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/act13030092","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System?
This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist.
期刊介绍:
Actuators (ISSN 2076-0825; CODEN: ACTUC3) is an international open access journal on the science and technology of actuators and control systems published quarterly online by MDPI.