基于imu的实时四类步态分析分类及电路实现

Che-Wei Chang, Jiun-Lin Yan, Chen-Nen Chang, K. Wen
{"title":"基于imu的实时四类步态分析分类及电路实现","authors":"Che-Wei Chang, Jiun-Lin Yan, Chen-Nen Chang, K. Wen","doi":"10.1109/SENSORS52175.2022.9967269","DOIUrl":null,"url":null,"abstract":"Nowadays, foot gait analysis has been widely used in the diagnosis and treatment of neuromusculoskeletal diseases and has received much more attention. The motion of the human hip joint can be simplified into three degrees of freedom: flexionextension, exhibition-adduction, and internal-external rotation motion. We can encompass these types of hip movements by using four types of walking gait: normal walking, tandem walking, toe walking, and heel walking. In this paper, we proposed a real-time and applicable system, which has low computational complexity while maintaining high accuracy for four kinds of gait analysis and recognition with a single Inertial Measurement Unit (IMU) sensor. The orientation is estimated by fast complementary filter (FCF) and Attitude and heading reference system (AHRS). Then the linear acceleration is integrated twice and calculated many spatial-temporal parameters of gait can be obtained, and the zero velocity update (ZUPT) method is used to suppress the integral drift. Experimental results show that the error rate of the four type of walking stride length can reach 1.34%, 1.65%, 2.26%, 2.13% respectively. The system has a recognition accuracy rate of 83.3%. Implemented with TSMC 0.18 µm process. To achieve a low power design, set the clock frequency to 45 kHz. The performance of the chip achieves an area of 2.47 x 2.47 mm2 and a power consumption of 0.1198 mW.","PeriodicalId":120357,"journal":{"name":"2022 IEEE Sensors","volume":"399 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"IMU-Based Real Time Four Type Gait Analysis and Classification and Circuit Implementation\",\"authors\":\"Che-Wei Chang, Jiun-Lin Yan, Chen-Nen Chang, K. Wen\",\"doi\":\"10.1109/SENSORS52175.2022.9967269\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nowadays, foot gait analysis has been widely used in the diagnosis and treatment of neuromusculoskeletal diseases and has received much more attention. The motion of the human hip joint can be simplified into three degrees of freedom: flexionextension, exhibition-adduction, and internal-external rotation motion. We can encompass these types of hip movements by using four types of walking gait: normal walking, tandem walking, toe walking, and heel walking. In this paper, we proposed a real-time and applicable system, which has low computational complexity while maintaining high accuracy for four kinds of gait analysis and recognition with a single Inertial Measurement Unit (IMU) sensor. The orientation is estimated by fast complementary filter (FCF) and Attitude and heading reference system (AHRS). Then the linear acceleration is integrated twice and calculated many spatial-temporal parameters of gait can be obtained, and the zero velocity update (ZUPT) method is used to suppress the integral drift. Experimental results show that the error rate of the four type of walking stride length can reach 1.34%, 1.65%, 2.26%, 2.13% respectively. The system has a recognition accuracy rate of 83.3%. Implemented with TSMC 0.18 µm process. To achieve a low power design, set the clock frequency to 45 kHz. The performance of the chip achieves an area of 2.47 x 2.47 mm2 and a power consumption of 0.1198 mW.\",\"PeriodicalId\":120357,\"journal\":{\"name\":\"2022 IEEE Sensors\",\"volume\":\"399 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Sensors\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SENSORS52175.2022.9967269\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Sensors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SENSORS52175.2022.9967269","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

目前,足部步态分析已广泛应用于神经肌肉骨骼疾病的诊断和治疗,受到越来越多的关注。人类髋关节的运动可以简化为三个自由度:屈伸、外展、内收和内外旋转运动。我们可以通过使用四种走路方式来包含这些类型的髋关节运动:正常走路,串联走路,脚趾走路和脚跟走路。本文提出了一种基于惯性测量单元(IMU)传感器的四种步态分析识别系统,该系统具有较低的计算复杂度和较高的精度。利用快速互补滤波(FCF)和姿态航向参考系统(AHRS)估计目标的方位。然后对线性加速度进行两次积分,计算得到步态的多个时空参数,并采用零速度更新(ZUPT)方法抑制积分漂移。实验结果表明,四种步行步幅的错误率分别达到1.34%、1.65%、2.26%、2.13%。该系统的识别准确率为83.3%。采用台积电0.18µm工艺实现。为了实现低功耗设计,将时钟频率设置为45khz。该芯片的性能达到2.47 x 2.47 mm2,功耗为0.1198 mW。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
IMU-Based Real Time Four Type Gait Analysis and Classification and Circuit Implementation
Nowadays, foot gait analysis has been widely used in the diagnosis and treatment of neuromusculoskeletal diseases and has received much more attention. The motion of the human hip joint can be simplified into three degrees of freedom: flexionextension, exhibition-adduction, and internal-external rotation motion. We can encompass these types of hip movements by using four types of walking gait: normal walking, tandem walking, toe walking, and heel walking. In this paper, we proposed a real-time and applicable system, which has low computational complexity while maintaining high accuracy for four kinds of gait analysis and recognition with a single Inertial Measurement Unit (IMU) sensor. The orientation is estimated by fast complementary filter (FCF) and Attitude and heading reference system (AHRS). Then the linear acceleration is integrated twice and calculated many spatial-temporal parameters of gait can be obtained, and the zero velocity update (ZUPT) method is used to suppress the integral drift. Experimental results show that the error rate of the four type of walking stride length can reach 1.34%, 1.65%, 2.26%, 2.13% respectively. The system has a recognition accuracy rate of 83.3%. Implemented with TSMC 0.18 µm process. To achieve a low power design, set the clock frequency to 45 kHz. The performance of the chip achieves an area of 2.47 x 2.47 mm2 and a power consumption of 0.1198 mW.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Non-intrusive Water Flow Rate Measurement: A TEG-powered Ultrasonic Sensing Approach Design of optical inclinometer composed of a ball lens and viscosity fluid to improve focusing Fall Event Detection using Vision Transformer Porous Silicon-Based Microspectral Unit for Real-Time Moisture Detection in a Battery-less Smart Mask Twisted and Coiled Carbon Nanotube Yarn Muscle Embedding Ferritin
×
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