Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction
Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction
复制标题
基于人工神经网络的活动分类、步态阶段估计和预测
DOI：
10.1007/s10439-023-03151-y
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发表时间：
2023
影响因子：
3.8
通讯作者：
Su, Hao
中科院分区：
文献类型：
--
作者：
Yu, Shuangyue;Yang, Jianfu;Huang, Tzu-Hao;Zhu, Junxi;Visco, Christopher J.;Hameed, Farah;Stein, Joel;Zhou, Xianlian;Su, Hao
步态模式对于健康监测、步态损伤评估和可穿戴设备控制至关重要。基于社区的非节律步态检测是该领域的一个新的前沿。提出了一种基于两级人工神经网络的高精度步态相位估计和预测算法。这项工作的目标是开发一种算法，可以估计和预测的步态周期在真实的时间使用便携式控制器只有两个IMU传感器（一个在每个大腿）在社区设置。我们的算法可以检测步态阶段在无节奏的条件下，在步行，楼梯上升，楼梯下降，并分类这些活动与站立。此外，我们的算法能够预测未来的跨内和跨步态阶段，提供了一种潜在的手段，以提高可穿戴设备控制器的性能。所提出的数据驱动算法是基于一个数据集，由5个健全的主体和3个不同的健全的主体进行验证。在非节律性活动情况下，该算法能够准确识别多个活动，准确率为99.55%，并能提前200 ms估计和预测真实的步态相位百分比，分别比基于事件的方法在相同条件下的误差平均小57.7%和54.0%.这项研究展示了一种解决方案，以估计和预测步态状态的多个无节奏的活动，这可能是部署到可穿戴机器人或健康监测设备的控制器。
Gait patterns are critical to health monitoring, gait impairment assessment, and wearable device control. Unrhythmic gait pattern detection under community-based conditions is a new frontier in this area. The present paper describes a high-accuracy gait phase estimation and prediction algorithm built on a two-stage artificial neural network. This work targets to develop an algorithm that can estimate and predict the gait cycle in real time using a portable controller with only two IMU sensors (one on each thigh) in the community setting. Our algorithm can detect the gait phase in unrhythmic conditions during walking, stair ascending, and stair descending, and classify these activities with standing. Moreover, our algorithm is able to predict both future intra- and inter-stride gait phases, offering a potential means to improve wearable device controller performance. The proposed data-driven algorithm is based on a dataset consisting of 5 able-bodied subjects and validated on 3 different able-bodied subjects. Under unrhythmic activity situations, validation shows that the algorithm can accurately identify multiple activities with 99.55% accuracy, and estimate (: 6.3%) and predict 200-ms-ahead (: 8.6%) the gait phase percentage in real time, which are on average 57.7 and 54.0% smaller than the error from the event-based method in the same conditions. This study showcases a solution to estimate and predict gait status for multiple unrhythmic activities, which may be deployed to controllers for wearable robots or health monitoring devices.
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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
发表时间：
2019
期刊：
2019 Design of Medical Devices Conference
影响因子：
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作者：
Yang, Jianfu;Huang, Tzu-Hao;Yu, Shuangyue;Yang, Xiaolong;Su, Hao;Spungen, Ann M.;Tsai, Chung-Ying
通讯作者：
Tsai, Chung-Ying
影响因子：
2.4
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Lewis CL;Ferris DP
通讯作者：
Ferris DP
影响因子：
4.6
作者：
Swami CP;Lenhard N;Kang J
通讯作者：
Kang J
影响因子：
3.7
作者：
Malcolm P;Derave W;Galle S;De Clercq D
通讯作者：
De Clercq D
DOI：
10.1109/biorob49111.2020.9224359
发表时间：
2020-11
期刊：
Proceedings of the ... IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics
影响因子：
--
作者：
Kang, Inseung;Molinaro, Dean D.;Choi, Gayeon;Young, Aaron J.
通讯作者：
Young, Aaron J.