Liang Sam Guoshi, Chung Ho Yin, Chu Ka Wing, Gao Yuk Hong, Lau Fong Ying, Lai Wolfe Ixin, Fong Gabriel Ching-Hang, Kwong Patrick Wai-Hang, Lam Freddy Man Hin
Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, 11 Yuk Choi Road, Hung Hom, Kowloon City, Hong Kong.
Biosensors (Basel). 2025 Jun 20;15(7):397. doi: 10.3390/bios15070397.
Smartphone-embedded inertia sensors are widely available nowadays. We have developed a smartphone application that could assess temporal gait characteristics using the built-in inertia measurement unit with the aim of enabling mass screening for gait abnormality. This study aimed to examine the test-retest reliability and concurrent validity of the smartphone-based gait assessment in assessing temporal gait parameters in level-ground walking. Twenty-six healthy young adults (mean age: 20.8 ± 0.7) were recruited. Participants walked at their comfortable pace on a 10 m pathway repetitively in two walking sessions. Gait data were simultaneously collected by the smartphone application and a VICON system during the walk. Gait events of heel strike and toes off were detected from the sensors signal by a peak detection algorithm. Further gait parameters were calculated and compared between the two systems. Pearson Product-Moment Correlation was used to evaluate the concurrent validity of both systems. Test-retest reliability was examined by the intraclass correlation coefficients (ICCs) between measurements from two sessions scheduled one to four weeks apart. The validity of smartphone-based gait assessment was moderate to excellent for parameters involving only heel strike detection (r = 0.628-0.977), poor to moderate for parameters involving detection of both heel strike and toes off (r = 0.098-0.704), and poor for the proportion of gait phases within a gait cycle. Reliability was good to fair for heel strike-related parameters (ICC = 0.845-0.388), good to moderate for heel strike and toes-off-related parameters (ICC = 0.827-0.582), and moderate to fair for proportional parameters. Validity was adversely affected when toe off was involved in the calculation, when there was an insufficient number of effective steps taken, or when calculating sub-phases with short duration. The use of smartphone-based gait assessment is recommended in calculating step time and stride time, and we suggest collecting no less than 100 steps per leg during clinical application for better validity and reliability.
如今，智能手机内置的惯性传感器已广泛应用。我们开发了一款智能手机应用程序，该程序能够利用内置的惯性测量单元评估步态的时间特征，旨在实现对步态异常的大规模筛查。本研究旨在检验基于智能手机的步态评估在评估平地行走时的时间步态参数方面的重测信度和同时效度。招募了26名健康的年轻成年人（平均年龄：20.8±0.7岁）。参与者在两个步行时段中，以舒适的步伐在10米的路径上重复行走。在行走过程中，智能手机应用程序和VICON系统同时收集步态数据。通过峰值检测算法从传感器信号中检测足跟触地和足趾离地的步态事件。计算并比较两个系统之间的进一步步态参数。使用Pearson积矩相关系数来评估两个系统的同时效度。通过间隔一到四周安排的两个时段测量之间的组内相关系数（ICC）来检验重测信度。对于仅涉及足跟触地检测的参数，基于智能手机的步态评估的效度为中等至优秀（r = 0.628 - 0.977），对于涉及足跟触地和足趾离地检测的参数，效度为差至中等（r = 0.098 - 0.704），对于步态周期内步态阶段的比例，效度较差。对于与足跟触地相关的参数，信度为良好至一般（ICC = 0.845 - 0.388），对于与足跟触地和足趾离地相关的参数，信度为良好至中等（ICC = 0.827 - 0.582），对于比例参数，信度为中等至一般。当计算中涉及足趾离地、有效步数不足或计算持续时间短的子阶段时，效度会受到不利影响。建议在计算步时和步幅时间时使用基于智能手机的步态评估，并且我们建议在临床应用中每条腿收集不少于100步以获得更好的效度和信度。
