文献类型： 会议论文

第一作者： Rafael Escamilla-Nunez

作者： Rafael Escamilla-Nunez ¹ ; Luis Aguilar ² ; Gabriel Ng ¹ ; Aliaa Gouda ¹ ; Jan Andrysek ¹ ;

作者机构： 1.Institute of Biomaterials and Biomedical Engineering at University of Toronto

2.Mechanical and Industrial Engineering Department - Biomedical Simulation Laboratory at University of Toronto

会议名称： Annual International Conference of the IEEE Engineering in Medicine and Biology Society

主办单位：

页码： 4487-4490

摘要： Wearable sensors have been investigated for the purpose of gait analysis, namely gait event detection. Many types of algorithms have been developed specifically using inertial sensor data for detecting gait events. Though much attention has turned toward machine learning algorithms, most of these approaches suffer from large computational requirements and are not yet suitable for real-time applications such as in prostheses or for feedback control. Current rules-based algorithms for real-time use often require fusion of multiple sensor signals to achieve high accuracy, thus increasing complexity and decreasing usability of the instrument. We present our results of a novel, rules-based algorithm using a single accelerometer signal from the foot to reliably detect heel-strike and toe-off events. Using the derivative of the raw accelerometer signal and applying an optimizer and windowing approach, high performance was achieved with a sensitivity and specificity of 94.32% and 94.70% respectively, and a timing error of 6.52 ± 22.37 ms, including trials involving multiple speed transitions. This would enable development of a compact wearable system for robust gait analysis in real-world settings, providing key insights into gait quality with the capability for real-time system control.

分类号： Q81-53