Objective: In this research, a marker-less ‘smart hallway’ is proposed where stride parameters are computed as a person walks through an institutional hallway. Stride analysis is a viable tool for identifying mobility changes, classifying abnormal gait, estimating fall risk, monitoring progression of rehabilitation programs, and indicating progression of nervous system related disorders. Methods: Smart hallway was build using multiple Intel RealSense D415 depth cameras. A novel algorithm was developed to track a human foot using combined point cloud data obtained from the smart hallway. A method was implemented to separate the left and right leg point cloud data, then find the average foot dimensions. Foot tracking was achieved by fitting a box with average foot dimensions to the foot, with the box’s base on the foot’s bottom plane. A smart hallway with this novel foot tracking algorithm was tested with 22 able-bodied volunteers by comparing marker-less system stride parameters with Vicon motion analysis output. Results: With smart hallway frame rate at approximately 60fps, temporal stride parameter absolute mean differences were less than 30ms. Random noise around the foot’s point cloud was observed, especially during foot strike phases. This caused errors in medial-lateral axis dependent parameters such as step width and foot angle. Anterior-posterior dependent (stride length, step length) absolute mean differences were less than 25mm. Conclusion: This novel marker-less smart hallway approach delivered promising results for stride analysis with small errors for temporal stride parameters, anterior-posterior stride parameters, and reasonable errors for medial-lateral spatial parameters.

中文翻译：

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开发用于无标记人类足部跟踪和步幅分析的智能走廊

目标：在这项研究中，提出了一种无标记的“智能走廊”，其中在一个人走过机构走廊时计算步幅参数。步幅分析是一种可行的工具，可用于识别活动性变化、对异常步态进行分类、估计跌倒风险、监测康复计划的进展以及指示神经系统相关疾病的进展。方法：智能走廊是使用多个英特尔实感 D415 深度摄像头构建的。开发了一种使用从智能走廊获得的组合点云数据来跟踪人脚的新算法。实现了一种方法来分离左右腿点云数据，然后找到平均脚尺寸。足部追踪是通过将一个具有平均足部尺寸的盒子安装到足部来实现的，盒子的底部位于足部的底部平面上。通过将无标记系统步幅参数与 Vicon 运动分析输出进行比较，22 名身体健全的志愿者对采用这种新型足部跟踪算法的智能走廊进行了测试。结果：智能走廊帧速率约为 60fps，时间步幅参数绝对平均差异小于 30ms。观察到足部点云周围的随机噪声，尤其是在足部撞击阶段。这导致了内外侧轴相关参数（例如步宽和脚角）的误差。前后依赖（步幅、步长）绝对平均差异小于 25 毫米。结论：这种新颖的无标记智能走廊方法为步幅分析提供了有希望的结果，时间步幅参数、前后步幅参数、