Optimizing wearable motion tracking by assessing sagittal joint angle accuracy with minimal sensor use

Brett C. Hannigan, M. Elgendi, Gholami Mohsen, C. Menon
{"title":"Optimizing wearable motion tracking by assessing sagittal joint angle accuracy with minimal sensor use","authors":"Brett C. Hannigan, M. Elgendi, Gholami Mohsen, C. Menon","doi":"10.36950/2024.2ciss047","DOIUrl":null,"url":null,"abstract":"Introduction \nWearable motion tracking technology often focuses on reducing the number of sensors to simplify design and lower costs. Research has shown that single IMUs can reconstruct leg kinematics (Gholami et al., 2020; Hossain et al., 2022; Lim et al., 2020) and ground reaction forces (Jiang et al., 2020) effectively. Additionally, model-based methods have demonstrated the feasibility of using fewer gyroscopes to estimate stride length and motion range in healthy individuals and patients with coxarthritis (Salarian et al., 2013). In this study, we aim to assess the precision of sagittal joint angle estimations using strain sensors while minimizing sensor count. \nMethods \nWe conducted a study with ten participants based on our previous work that involved collecting single-leg treadmill running data to monitor lower limb joint angles with piezoresistive strain sensors. Subjects ran on an instrumented treadmill at 8-10 km/h, wearing athletic pants embedded with nine strain sensors located on the hip, knee, and ankle. Optical motion capture provided reference kinematics. Our prior research achieved less than 1.5° error in the sagittal plane using a machine-learning approach. The current study explores the extent to which sensor reduction is possible without meaningful loss of accuracy. Three evaluation measures were used for assessment: Pearson correlation, dynamic time warping, and root-mean-squared error. \nResults \nThe results from our correlation analysis will be used to develop a model that optimally balances between accuracy and minimizing the number of sensors. This has practical implications in sports science, where athletes could benefit from less intrusive and more comfortable performance monitoring, and in healthcare, for remote monitoring of patients with mobility issues. \nReferences \nGholami, M., Napier, C., & Menon, C. (2020). Estimating lower extremity running gait kinematics with a single accelerometer: A deep learning approach. Sensors, 20(10), Article 2939. https://doi.org/10.3390/s20102939 \nHossain, M. S., Bin, Dranetz, J., Choi, H., & Guo, Z. (2022). DeepBBWAE-Net: A CNN-RNN based deep superlearner for estimating lower extremity sagittal plane joint kinematics using shoe-mounted IMU sensors in daily living. IEEE Journal of Biomedical and Health Informatics, 26(8), 3906-3917. https://doi.org/10.1109/jbhi.2022.3165383 \nJiang, X., Napier, C., Hannigan, B., Eng, J. J., & Menon, C. (2020). Estimating vertical ground reaction force during walking using a single inertial sensor. Sensors, 20(15), Article 4345. https://doi.org/10.3390/s20154345 \nLim, H., Kim, B., & Park, S. (2020). Prediction of lower limb kinetics and kinematics during walking by a single IMU on the lower back using machine learning. Sensors, 20(1), Article 130. https://doi.org/10.3390/s20010130 \nSalarian, A., Burkhard, P. R., Vingerhoets, F. J. G., Jolles, B. M., & Aminian, K. (2013). A novel approach to reducing number of sensing units for wearable gait analysis systems. IEEE Transactions on Biomedical Engineering, 60(1), 72–77. https://doi.org/10.1109/TBME.2012.2223465","PeriodicalId":415194,"journal":{"name":"Current Issues in Sport Science (CISS)","volume":"19 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Issues in Sport Science (CISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36950/2024.2ciss047","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Introduction Wearable motion tracking technology often focuses on reducing the number of sensors to simplify design and lower costs. Research has shown that single IMUs can reconstruct leg kinematics (Gholami et al., 2020; Hossain et al., 2022; Lim et al., 2020) and ground reaction forces (Jiang et al., 2020) effectively. Additionally, model-based methods have demonstrated the feasibility of using fewer gyroscopes to estimate stride length and motion range in healthy individuals and patients with coxarthritis (Salarian et al., 2013). In this study, we aim to assess the precision of sagittal joint angle estimations using strain sensors while minimizing sensor count. Methods We conducted a study with ten participants based on our previous work that involved collecting single-leg treadmill running data to monitor lower limb joint angles with piezoresistive strain sensors. Subjects ran on an instrumented treadmill at 8-10 km/h, wearing athletic pants embedded with nine strain sensors located on the hip, knee, and ankle. Optical motion capture provided reference kinematics. Our prior research achieved less than 1.5° error in the sagittal plane using a machine-learning approach. The current study explores the extent to which sensor reduction is possible without meaningful loss of accuracy. Three evaluation measures were used for assessment: Pearson correlation, dynamic time warping, and root-mean-squared error. Results The results from our correlation analysis will be used to develop a model that optimally balances between accuracy and minimizing the number of sensors. This has practical implications in sports science, where athletes could benefit from less intrusive and more comfortable performance monitoring, and in healthcare, for remote monitoring of patients with mobility issues. References Gholami, M., Napier, C., & Menon, C. (2020). Estimating lower extremity running gait kinematics with a single accelerometer: A deep learning approach. Sensors, 20(10), Article 2939. https://doi.org/10.3390/s20102939 Hossain, M. S., Bin, Dranetz, J., Choi, H., & Guo, Z. (2022). DeepBBWAE-Net: A CNN-RNN based deep superlearner for estimating lower extremity sagittal plane joint kinematics using shoe-mounted IMU sensors in daily living. IEEE Journal of Biomedical and Health Informatics, 26(8), 3906-3917. https://doi.org/10.1109/jbhi.2022.3165383 Jiang, X., Napier, C., Hannigan, B., Eng, J. J., & Menon, C. (2020). Estimating vertical ground reaction force during walking using a single inertial sensor. Sensors, 20(15), Article 4345. https://doi.org/10.3390/s20154345 Lim, H., Kim, B., & Park, S. (2020). Prediction of lower limb kinetics and kinematics during walking by a single IMU on the lower back using machine learning. Sensors, 20(1), Article 130. https://doi.org/10.3390/s20010130 Salarian, A., Burkhard, P. R., Vingerhoets, F. J. G., Jolles, B. M., & Aminian, K. (2013). A novel approach to reducing number of sensing units for wearable gait analysis systems. IEEE Transactions on Biomedical Engineering, 60(1), 72–77. https://doi.org/10.1109/TBME.2012.2223465
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过评估矢状关节角度的准确性,优化可穿戴运动跟踪,同时尽量少用传感器
引言 可穿戴运动跟踪技术通常侧重于减少传感器数量,以简化设计和降低成本。研究表明,单个 IMU 可有效重建腿部运动学(Gholami 等人,2020 年;Hossain 等人,2022 年;Lim 等人,2020 年)和地面反作用力(Jiang 等人,2020 年)。此外,基于模型的方法已经证明了使用较少的陀螺仪来估计健康人和髋关节炎患者的步长和运动范围的可行性(Salarian 等人,2013 年)。在本研究中,我们旨在评估使用应变传感器估计矢状关节角度的精度,同时尽量减少传感器数量。方法 我们在之前工作的基础上对 10 名参与者进行了一项研究,其中包括收集单腿跑步机跑步数据,使用压阻应变传感器监测下肢关节角度。受试者穿着嵌有九个应变传感器的运动裤,在装有仪器的跑步机上以 8-10 公里/小时的速度跑步,应变传感器分别位于髋关节、膝关节和踝关节。光学运动捕捉提供了参考运动学数据。我们之前的研究采用机器学习方法,矢状面误差小于 1.5°。目前的研究探讨了在不损失准确性的前提下减少传感器的可能性。评估采用了三种评估方法:皮尔逊相关性、动态时间扭曲和均方根误差。结果 我们的相关性分析结果将用于开发一种模型,在准确性和尽量减少传感器数量之间取得最佳平衡。这对运动科学和医疗保健领域都有实际意义,前者可以为运动员提供侵入性更低、更舒适的运动表现监测,后者可以为行动不便的病人提供远程监测。参考文献 Gholami, M., Napier, C., & Menon, C. (2020).用单个加速度计估算下肢跑步步态运动学:深度学习方法。https://doi.org/10.3390/s20102939 Hossain, M. S., Bin, Dranetz, J., Choi, H., & Guo, Z. (2022).DeepBBWAE-Net:基于 CNN-RNN 的深度超级学习器,用于在日常生活中使用鞋载 IMU 传感器估计下肢矢状面关节运动学。https://doi.org/10.1109/jbhi.2022.3165383 Jiang, X., Napier, C., Hannigan, B., Eng, J. J., & Menon, C. (2020)。使用单个惯性传感器估算行走过程中的垂直地面反作用力。https://doi.org/10.3390/s20154345 Lim, H., Kim, B., & Park, S. (2020)。利用机器学习通过下背部的单个 IMU 预测行走过程中的下肢动力学和运动学。https://doi.org/10.3390/s20010130 Salarian, A., Burkhard, P. R., Vingerhoets, F. J. G., Jolles, B. M., & Aminian, K. (2013)。减少可穿戴步态分析系统传感单元数量的新方法。IEEE Transactions on Biomedical Engineering, 60(1), 72-77. https://doi.org/10.1109/TBME.2012.2223465
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
From supercrip to techno supercrip Associations between daily movement behaviors, sleep, and affect in older adults: An ecological momentary assessment study Position statement regarding the current standing of exercise therapy in Austria (Positionspapier zur Situation der Trainingstherapie in Österreich) The Perceived Instrumental Effects of Maltreatment in Sport (PIEMS) scale: Translation, (cross-)validation, and short-form development of the German version Who’s better? Adaptive comparative judgment of dance performances
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1