Rispens Sietse M, Cox Lieke G E, Ejupi Andreas, Delbaere Kim, Annegarn Janneke, Bonomi Alberto G
Philips Research, Remote Patient Management and Chronic Care Department, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands.
Philips Research, Patient Care & Monitoring Department, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands.
Sensors (Basel). 2021 Mar 6;21(5):1854. doi: 10.3390/s21051854.
Walking speed is a strong indicator of the health status of older people and patients. Using algorithms, the walking speed can be estimated from wearable accelerometers, which enables minimally obtrusive (longitudinal) monitoring. We evaluated the performance of two algorithms, the inverted pendulum (IP) algorithm, and a novel adaptation correcting for lateral step movement, which aimed to improve accuracy during slow walking. To evaluate robustness, we gathered data from different groups (healthy adults, elderly, and elderly patients) of volunteers (n = 159) walking under various conditions (over ground, treadmill, using walking aids) at a broad range of speeds (0.11-1.93 m/s). Both of the algorithms showed good agreement with the reference values and similar root-mean-square errors (RMSEs) for walking speeds ≥0.5 m/s, which ranged from 0.09-0.16 m/s for the different positions, in line with the results from others. However, for slower walking, RMSEs were significantly better for the new method (0.06-0.09 m/s versus 0.15-0.19 m/s). Pearson correlation improved for speeds <0.5 m/s (from 0.67-0.72 to 0.73-0.82) as well as higher speeds (0.87-0.97 to 0.90-0.98) with the new method. Overall, we found that IP(-based) walking speed estimation proved to be applicable for a variety of wearing positions, conditions and speeds, indicating its potential value for health assessment applications.
步行速度是老年人和患者健康状况的有力指标。通过算法，可以从可穿戴式加速度计估计步行速度，从而实现微创（纵向）监测。我们评估了两种算法的性能，即倒立摆（IP）算法和一种针对横向步移进行校正的新型算法，该算法旨在提高慢走时的准确性。为了评估稳健性，我们收集了不同组（健康成年人、老年人和老年患者）的志愿者（n = 159）在各种条件下（在地面、跑步机上、使用助行器）以广泛速度（0.11 - 1.93米/秒）行走的数据。对于步行速度≥0.5米/秒，两种算法与参考值都显示出良好的一致性，并且不同位置的均方根误差（RMSE）相似，范围为0.09 - 0.16米/秒，与其他研究结果一致。然而，对于较慢的步行速度，新方法的RMSE明显更好（0.06 - 0.09米/秒对0.15 - 0.19米/秒）。新方法在速度<0.5米/秒时（从0.67 - 0.72提高到0.73 - 至0.82）以及较高速度时（从0.87 - 0.97提高到0.90 - 0.98）的皮尔逊相关性也有所提高。总体而言，我们发现基于IP的步行速度估计适用于各种佩戴位置、条件和速度，表明其在健康评估应用中的潜在价值。
