Tao Shuai, Zhang Hao, Kong Liwen, Sun Yan, Zhao Jie
College of Information Engineering, Dalian University, Dalian, Liaoning, China.
China United Network Communications Co Ltd, Huaian, Jiangsu, China.
Digit Health. 2024 May 28;10:20552076241257054. doi: 10.1177/20552076241257054. eCollection 2024 Jan-Dec.
This study aims to validate the reliability and validity of gait analysis using smartphones in a controlled environment.
Thirty healthy adults attached smartphones to the waist and thigh, while an inertial measurement unit was fixed at the shank as a reference device; each participant was asked to walk six gait cycles at self-selected low, normal, and high speeds. Thirty-five cerebral small vessel disease patients were recruited to attach the smartphone to the thigh, performing single-task (ST), cognitive dual-task (DT), and physical dual-task walking (DT) to obtain gait parameters.
The results from the healthy group indicate that, regardless of whether attached to the thigh or waist, the smartphones calculated gait parameters with good reliability (ICC > 0.75) across three different walking speeds. There were no significant differences in the gait parameters between the smartphone attached to the thigh and the IMU across all three walking speeds (> 0.05). However, significant differences were observed between the smartphone at the waist and the IMU during the stance phase, swing phase, stance time, and stride length at high speeds (< 0.05). At the same time, measurements of other gait parameters were similar > 0.05). Patients demonstrated significant differences in the cadence, stride time, stance phase, swing phase, stance time, stride length, and walking speed between ST and DT (< 0.05). Significant differences were observed in the stance phase, swing phase, stride length, and walking speed between ST and DT (< 0.05).
This study demonstrates the feasibility of using built-in smartphone sensors for gait analysis in a controlled environment.
本研究旨在验证在受控环境中使用智能手机进行步态分析的可靠性和有效性。
30名健康成年人将智能手机系于腰部和大腿，同时将惯性测量单元固定在小腿作为参考设备；要求每位参与者以自行选择的低速、正常速度和高速行走六个步态周期。招募了35名脑小血管疾病患者，将智能手机系于大腿，进行单任务（ST）、认知双任务（DT）和身体双任务行走（DT）以获取步态参数。
健康组的结果表明，无论系于大腿还是腰部，智能手机在三种不同步行速度下计算出的步态参数具有良好的可靠性（组内相关系数>0.75）。在所有三种步行速度下，系于大腿的智能手机与惯性测量单元之间的步态参数无显著差异（>0.05）。然而，在高速行走时，腰部的智能手机与惯性测量单元在站立期、摆动期、站立时间和步长方面存在显著差异（<0.05）。同时，其他步态参数的测量结果相似（>0.05）。患者在单任务和双任务之间的步频、步幅时间、站立期、摆动期、站立时间、步长和步行速度方面存在显著差异（<0.05）。在单任务和双任务之间的站立期、摆动期、步长和步行速度方面观察到显著差异（<0.05）。
本研究证明了在受控环境中使用智能手机内置传感器进行步态分析的可行性。
