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目的:研发一种"Lab-in-Shoe"的可穿戴智能鞋步态时空参数分析系统（"Lab-in-Shoe"智能鞋系统），实现足踝损伤患者步态功能障碍的定量评估。方法:通过将惯性传感器和鞋垫式足底压力分布传感器集成于鞋具，构成"Lab-in-Shoe"智能鞋系统的硬件主体。利用惯性传感器的加速度数据积分获得步态空间参数；利用鞋垫式足底压力分布传感器获得足底压力分布数据以及支撑相、摆动相、零速度时刻等步态时间参数和力学参数。招募8名年轻的健康受试者，年龄（25.6±1.3）岁，身高（175.4±2.2）cm。在光学动作捕捉实验室进行步态数据采集，通过比较"Lab-in-Shoe"智能鞋系统与"金标准"Vicon光学动作捕捉系统的步态数据结果，验证智能鞋系统的有效性与可靠性。并对足底压力分布传感器进行传感单元的标定实验，以证明其压力数据的准确性。结果:"Lab-in-Shoe"智能鞋系统可准确获取受试者的步长、步宽、步频、步速、步态相位划分、足底压力分布以及压力中心曲线等核心步态时空参数，并且能够实现步态中的双足位姿复现。通过Bland-Altman图与"金标准"Vicon光学动作捕捉系统进行比较，"Lab-in-Shoe"智能鞋系统在慢速（0.68±0.05）m/s、最适速度（1.10±0.07）m/s和快速（1.40±0.13）m/s 3种行走速度下的步长平均误差为3.12%（范围值为2.76%~4.24%），90%的结果在95%置信区间内，一致性良好。步长参数在慢速、最适速度、快速的组内相关系数（ICC）值分别为0.93、0.917、0.893，可靠性良好。足底压力传感器的多个传感单元标定数据均落在95%的线性回归范围内，相关系数 r=0.949（ P<0.05）。"Lab-in-Shoe"智能鞋系统所采集的足底压力数据整体曲线呈现明显的双峰特性。 结论:自主研发的"Lab-in-Shoe"智能鞋系统能够实现对步态参数的便捷采集和计算，在不同行走速度下各结果参数均具有较好的可靠性与有效性，有助于在临床环境下的大规模步态数据采集。

Objective:To develop a wearable gait analysis system based on "Lab-in-Shoe" intelligent footwear for quantitative assessment of gait dysfunction in patients with ankle injuries.Methods:In this study, integration of inertial sensors and insole-type plantar pressure distribution sensors into footwear formed the hardware core of the "Lab-in-Shoe" intelligent footwear system. In terms of algorithms, acceleration data from the inertial sensors were integrated to obtain spatial parameters of gait. The insole-type plantar pressure sensors were employed to acquire the data concerning foot pressure distribution, as well as temporal parameters and mechanical parameters of gait, including support phase, swing phase, and zero velocity moments. To validate the accuracy of this system, 8 young and healthy participants [age: (25.6±1.3) years; height: (175.4±2.2) cm] were recruited for gait data collection in an optical motion capture laboratory. By comparing the gait data between the "Lab-in-Shoe" intelligent footwear system and the gold standard Vicon optical motion capture system, the effectiveness and reliability of the intelligent footwear system were respectively tested. Additionally, a calibration experiment was conducted for the sensing units of the plantar pressure sensors to examinate the accuracy of the pressure data.Results:The tested system accurately captured the following gait parameters of the participants: step length, step width, step frequency, walking speed, gait phase division, foot pressure distribution, and center of pressure curve, among other core spatiotemporal gait parameters. Moreover, the system demonstrated its ability to replicate the dual-foot posture during gait. Compared with the gold standard Vicon optical motion capture system through Bland-Altman, the Lab-in-Shoe smart shoe system showed stride length mean error within 3.12% (range: 2.76% to 4.24%) across 3 different walking speeds [slow speed (0.68±0.05) m/s, preferred speed (1.10±0.07) m/s, and fast speed (1.40±0.13) m/s]. 90% of the results fell within the 95% limits of agreement, indicating good consistency. The intraclass correlation coefficients (ICC) for stride parameters within the slow, preferred, and fast walking speed groups were 0.93, 0.917, and 0.893, respectively, indicating good reliability. The calibration data of multiple sensor units from the plantar pressure sensors all fell within the 95% linear regression range, with a correlation coefficient of r=0.949 ( P<0.05). The plantar pressure data collected by the intelligent footwear system presented a distinct bimodal characteristic. Conclusions:The "Lab-in-Shoe" smart shoe system developed by our institute is capable of collecting and calculating gait parameters conveniently and quickly, and demondtrates good reliability and validity across different walking speeds. Therefore, it is valueable for large-scale gait data collection in a clinical setting.