Department of Electrical & Information Engineering, Dalian Neusoft University of Information, Dalian 116023, China.
School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2021 Feb 14;21(4):1347. doi: 10.3390/s21041347.
Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients' gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.
步态分析作为一种常见的人体步态检查方法，可以通过仪器测量提供一系列运动学、动力学等参数。近年来，步态分析逐渐应用于疾病诊断、矫形外科手术评估和康复进展的评估中，特别是步态相位异常可作为阿尔茨海默病和帕金森病的临床诊断指标，这两种疾病通常表现出不同程度的步态相位异常。本研究提出了一种基于惯性传感器的步态分析方法。选择平滑和滤波后的角速度信号作为 15 维时间特征的输入数据。采用隐马尔可夫模型和参数自适应模型对步态相位进行分割。实验结果表明，与其他分类模型相比，基于 HMM 和参数自适应的模型在步态相位分割中具有较高的识别率，并且步态相位的识别结果与真实值一致。所提出的用于数据采集的可穿戴设备可以嵌入在鞋子上，不仅可以稳定可靠地采集患者的步态数据，保证步态数据的完整性和客观性，还可以在日常场景和户外环境中采集数据。
