利用惯性传感器与多模态网络解析跑步参数
Analysis of Running Parameters Using IMU and Multi-modal Network
向勉 1易本顺 2周丙涛 1谭建军 1朱黎1
作者信息
- 1. 湖北民族大学智能科学与工程学院,湖北 恩施,445000
- 2. 武汉大学电子信息学院,湖北 武汉,430072
- 折叠
摘要
实时检测跑步时的速度与步幅在避免运动者受伤、提升运动效率上有着重要意义.提出了 一种利用惯性测量单元(inertial measurement unit,IMU)来检测这两个指标的方法.首先,招募了 10名志愿者,并将3个IMU模块安置在足部、小腿、大腿处,采集了 5 137个步态周期的数据;然后,利用主成分分析法分析数据,结合皮尔逊相关系数探讨了速度步幅与传感器位置、物理参数之间的关系;提出了一种多模态架构的长短期记忆特征提取网络(multi-modal-attention-long short-term memory,M-Att-LSTM),利用两个引入注意力机制的长短期记忆网络(att-long short-term memory,Att-LSTM)对加速度和角度变化分别做特征提取,最后进行回归拟合.实验结果表明,M-Att-LSTM在速度上误差为0.058 m/s,标准偏差为0.013 m/s,而在步幅上误差为0.023 m,标准偏差为0.022 m,两项指标都优于单纯的Att-LSTM.
Abstract
Objectives:Real-time measurement of running speed and stride length is of great significance in avoiding injury and improving exercise efficiency.Methods:We propose a method using inertial measure-ment unit(IMU)to detect these two indicators.First,3 IMU are placed on the foot,calf and thigh of the 10 runners which we recruited,and 5 137 data of gait cycles are collected.Second,principal component analysis is used to analyze the data,and Pearson correlation coefficient is used to discuss the relationship be-tween the detection indicators of running and the sensor position and physical parameters.Then a multi-modal attention-long short-term memory(M-Att-LSTM)is proposed for feature extraction,two long short-term memory(LSTM)modules with attention mechanism are used to extract features of acceleration and angle,and regression fitting is carried out.Results:The experiment result shows that M-Att-LSTM has errors of 0.058 m/s in speed and 0.023 m in stride,the standard deviation is 0.013 m/s and 0.022 m,respectively.Both indicators are better than pure Att-LSTM.Conclusions:The studies show that multi-modal network can improve network processing capabilities,compared with relevant researches in recent years,our study has obvious advantages in error control.
关键词
惯性传感器/主成分分析/皮尔逊系数/长短期记忆网络/多模态Key words
inertial sensor/principal component analysis/Pearson coefficient/long short-term memory networks/multi-modal引用本文复制引用
基金项目
国家自然科学基金(61771188)
国家自然科学基金(61961017)
2020年湖北省教育厅科学技术研究计划青年人才(Q20201902)
恩施州科技局技术支撑类项目(D20220004)
出版年
2024
NETL
NSTL
万方数据