Machine Learning Based Adaptive Gait Phase Estimation Using Inertial Measurement Sensors
Machine Learning Based Adaptive Gait Phase Estimation Using Inertial Measurement Sensors
复制标题
使用惯性测量传感器进行基于机器学习的自适应步态相位估计
DOI：
10.1115/dmd2019-3266
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Tsai, Chung-Ying
中科院分区：
文献类型：
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作者：
Yang, Jianfu;Huang, Tzu-Hao;Yu, Shuangyue;Yang, Xiaolong;Su, Hao;Spungen, Ann M.;Tsai, Chung-Ying
提出了一种基于惯性测量单元（IMU）的便携式运动传感系统，并提出了一种用于非稳态步行和多种活动（步行、跑步、上楼梯、下楼梯、下蹲）监测的自适应步态相位检测方法。该算法的目的是克服现有的步态检测方法的局限性，是基于时域阈值的稳态运动，是不是通用的检测步态在不同的活动或不同的步态模式相同的活动。便携式传感套装由三个IMU传感器（用于步态相位检测的可穿戴传感器）和两个脚踏开关（地面实况测量，所提出算法的步态检测不需要）组成。来自三个伊穆斯的加速度、角速度、欧拉角、合成加速度和合成角速度用作输入训练数据，两个脚踏开关的数据用作训练标签数据（单支撑、双支撑、摆动相位）。三种方法1）逻辑回归（LR），2）随机森林分类器（RF），3）人工神经网络（NN）被用来建立步态相位检测模型。实验结果表明，本文提出的基于随机森林分类器的步态相位检测方法在步行、跑步、上楼梯、下楼梯和下蹲时的准确率分别为98.94%、98.45%、99.15%、99.00%和99.63%。实验结果表明，该传感服不仅可以检测任意瞬时状态下的步态状态，而且可以推广到多种活动。因此，它可以实现对人体步态的实时监测和辅助设备的控制。
This paper presents a portable inertial measurement unit (IMU)-based motion sensing system and proposed an adaptive gait phase detection approach for non-steady state walking and multiple activities (walking, running, stair ascent, stair descent, squat) monitoring. The algorithm aims to overcome the limitation of existing gait detection methods that are time-domain thresholding based for steady-state motion and are not versatile to detect gait during different activities or different gait patterns of the same activity. The portable sensing suit is composed of three IMU sensors (wearable sensors for gait phase detection) and two footswitches (ground truth measurement and not needed for gait detection of the proposed algorithm). The acceleration, angular velocity, Euler angle, resultant acceleration, and resultant angular velocity from three IMUs are used as the input training data and the data of two footswitches used as the training label data (single support, double support, swing phase). Three methods 1) Logistic Regression (LR), 2) Random Forest Classifier (RF), and 3) Artificial Neural Network (NN) are used to build the gait phase detection models. The result shows our proposed gait phase detection with Random Forest Classifier can achieve 98.94% accuracy in walking, 98.45% in running, 99.15% in stair-ascent, 99.00% in stair-descent, and 99.63% in squatting. It demonstrates that our sensing suit can not only detect the gait status in any transient state but also generalize to multiple activities. Therefore, it can be implemented in real-time monitoring of human gait and control of assistive devices.