Nassajpour Marjan, Seifallahi Mahmoud, Rosenfeld Amie, Tolea Magdalena I, Galvin James E, Ghoraani Behnaz
Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA.
Comprehensive Center for Brain Health, Department of Neurology, University of Miami, Boca Raton, FL 33433, USA.
Sensors (Basel). 2025 Sep 4;25(17):5501. doi: 10.3390/s25175501.
Accurate and scalable gait assessment is essential for clinical and research applications, including fall risk evaluation, rehabilitation monitoring, and early detection of neurodegenerative diseases. While electronic walkways remain the clinical gold standard, their high cost and limited portability restrict widespread use. Wearable inertial measurement units (IMUs) and markerless depth cameras have emerged as promising alternatives; however, prior studies have typically assessed these systems under tightly controlled conditions, with single participants in view, limited marker sets, and without direct cross-technology comparisons. This study addresses these gaps by simultaneously evaluating three sensing technologies-APDM wearable IMUs (tested in two separate configurations: foot-mounted and lumbar-mounted) and the Azure Kinect depth camera-against ProtoKinetics Zeno™ Walkway Gait Analysis System in a realistic clinical environment where multiple individuals were present in the camera's field of view. Gait data from 20 older adults (mean age 70.06±9.45 years) performing Single-Task and Dual-Task walking trials were synchronously captured using custom hardware for precise temporal alignment. Eleven gait markers spanning macro, micro-temporal, micro-spatial, and spatiotemporal domains were compared using mean absolute error (MAE), Pearson correlation (), and Bland-Altman analysis. Foot-mounted IMUs demonstrated the highest accuracy (MAE =0.00-6.12, r=0.92-1.00), followed closely by the Azure Kinect (MAE =0.01-6.07, r=0.68-0.98). Lumbar-mounted IMUs showed consistently lower agreement with the reference system. These findings provide the first comprehensive comparison of wearable and depth-sensing technologies with a clinical gold standard under real-world conditions and across an extensive set of gait markers. The results establish a foundation for deploying scalable, low-cost gait assessment systems in diverse healthcare contexts, supporting early detection, mobility monitoring, and rehabilitation outcomes across multiple patient populations.
准确且可扩展的步态评估对于临床和研究应用至关重要，包括跌倒风险评估、康复监测以及神经退行性疾病的早期检测。虽然电子步道仍然是临床金标准，但其高成本和有限的便携性限制了其广泛应用。可穿戴惯性测量单元（IMU）和无标记深度相机已成为有前景的替代方案；然而，先前的研究通常在严格控制的条件下评估这些系统，观察单个参与者，使用有限的标记集，并且没有直接的跨技术比较。本研究通过在现实临床环境中同时评估三种传感技术——APDM可穿戴IMU（在两种单独配置下测试：足部安装和腰部安装）和Azure Kinect深度相机——与ProtoKinetics Zeno™步道步态分析系统，来填补这些空白，该环境中有多个人出现在相机视野中。使用定制硬件同步捕获了20名老年人（平均年龄70.06±9.45岁）进行单任务和双任务步行试验的步态数据，以实现精确的时间对齐。使用平均绝对误差（MAE）、皮尔逊相关性（）和布兰德-奥特曼分析比较了跨越宏观、微时间、微空间和时空领域的11个步态标记。足部安装的IMU显示出最高的准确性（MAE =0.00 - 6.12，r =0.92 - 1.00），紧随其后的是Azure Kinect（MAE =0.01 - 6.07，r =0.68 - 0.98）。腰部安装的IMU与参考系统的一致性始终较低。这些发现首次在现实世界条件下并针对广泛的步态标记集，对可穿戴和深度传感技术与临床金标准进行了全面比较。结果为在不同医疗环境中部署可扩展、低成本的步态评估系统奠定了基础，支持对多个患者群体的早期检测、活动监测和康复结果评估。
