Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada.
Faculty of Nursing and Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada.
Sensors (Basel). 2019 Mar 27;19(7):1483. doi: 10.3390/s19071483.
The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this study, algorithms for identifying gait events were developed for accelerometers that were placed on the foot and low back and validated against a gold standard force plate gait event detection method. These algorithms were automated to enable the processing of large quantities of data by accommodating variability in running patterns. An evaluation of the accuracy of the algorithms was done by comparing the magnitude and variability of the difference between the back and foot methods in different running conditions, including different speeds, foot strike patterns, and outdoor running surfaces. The results show the magnitude and variability of the back-foot difference was consistent across running conditions, suggesting that the gait event detection algorithms can be used in a variety of settings. As wearable technology allows for running gait analyses to move outside of the laboratory, the use of automated accelerometer-based gait event detection methods may be helpful in the real-time evaluation of running patterns in real world conditions.
初始接触（IC）和离地（TO）事件的识别是跑步步态分析的关键组成部分。为了在实际环境中评估跑步步态，需要基于可穿戴传感器信号的稳健步态事件检测算法。在这项研究中，针对放置在脚部和低背部的加速度计开发了用于识别步态事件的算法，并针对金标准测力板步态事件检测方法进行了验证。这些算法是自动化的，可通过适应跑步模式的可变性来处理大量数据。通过比较不同跑步条件下（包括不同速度、脚部触地模式和户外跑步表面）背部和脚部方法之间差异的大小和可变性，对算法的准确性进行了评估。结果表明，背部-脚部差异的大小和可变性在各种跑步条件下是一致的，这表明步态事件检测算法可以在各种环境中使用。随着可穿戴技术使跑步步态分析能够走出实验室，基于自动化加速度计的步态事件检测方法的使用可能有助于在实际条件下实时评估跑步模式。
