Biomechanics and Movement Science Program, University of Delaware, Newark, DE 19713, USA.
Department of Physical Therapy, University of Delaware, Newark, DE 19713, USA.
Sensors (Basel). 2020 Sep 15;20(18):5272. doi: 10.3390/s20185272.
Video- and sensor-based gait analysis systems are rapidly emerging for use in 'real world' scenarios outside of typical instrumented motion analysis laboratories. Unlike laboratory systems, such systems do not use kinetic data from force plates, rather, gait events such as initial contact (IC) and terminal contact (TC) are estimated from video and sensor signals. There are, however, detection errors inherent in kinematic gait event detection methods (GEDM) and comparative study between classic laboratory and video/sensor-based systems is warranted. For this study, three kinematic methods: coordinate based treadmill algorithm (CBTA), shank angular velocity (SK), and foot velocity algorithm (FVA) were compared to 'gold standard' force plate methods (GS) for determining IC and TC in adults ( = 6), typically developing children ( = 5) and children with cerebral palsy ( = 6). The root mean square error (RMSE) values for CBTA, SK, and FVA were 27.22, 47.33, and 78.41 ms, respectively. On average, GED was detected earlier in CBTA and SK (CBTA: -9.54 ± 0.66 ms, SK: -33.41 ± 0.86 ms) and delayed in FVA (21.00 ± 1.96 ms). The statistical model demonstrated insensitivity to variations in group, side, and individuals. Out of three kinematic GEDMs, SK GEDM can best be used for sensor-based gait event detection.
基于视频和传感器的步态分析系统正在迅速涌现，用于典型仪器化运动分析实验室之外的“真实世界”场景。与实验室系统不同，此类系统不使用力板的动力学数据，而是从视频和传感器信号估算初始接触 (IC) 和终端接触 (TC) 等步态事件。然而，运动学步态事件检测方法 (GEDM) 中存在检测误差，因此有必要对经典实验室和基于视频/传感器的系统进行比较研究。在这项研究中，三种运动学方法：基于坐标的跑步机算法 (CBTA)、小腿角速度 (SK) 和脚速算法 (FVA) 与“黄金标准”力板方法 (GS) 进行了比较，用于确定成人 (= 6)、正常发育儿童 (= 5) 和脑瘫儿童 (= 6) 的 IC 和 TC。CBTA、SK 和 FVA 的均方根误差 (RMSE) 值分别为 27.22、47.33 和 78.41 毫秒。平均而言，CBTA 和 SK 中的 GED 检测提前（CBTA：-9.54 ± 0.66 毫秒，SK：-33.41 ± 0.86 毫秒），而 FVA 中的 GED 检测延迟（21.00 ± 1.96 毫秒）。统计模型对组间、侧间和个体间的差异不敏感。在三种运动学 GEDM 中，SK GEDM 最适合用于基于传感器的步态事件检测。
