Gait speed as a powerful biomarker of mobility is mostly assessed in controlled environments, e.g. in the clinic. With wearable inertial sensors, gait speed can be estimated in an objective manner. However, most of the previous works have validated the gait speed estimation algorithms in clinical settings which can be different than the home assessments in which the patients demonstrate their actual performance. Moreover, to provide comfort for the users, devising an algorithm based on a single sensor setup is essential. To this end, the goal of this study was to develop and validate a new gait speed estimation method based on a machine learning approach to predict gait speed in both clinical and home assessments by a sensor on the lower back. Moreover, two methods were introduced to detect walking bouts during daily activities at home. We have validated the algorithms in 35 patients with multiple sclerosis as it often presents with mobility difficulties. Therefore, the robustness of the algorithm can be shown in an impaired or slow gait. Against silver standard multi-sensor references, we achieved a bias close to zero and a precision of 0.15 m/s for gait speed estimation. Furthermore, the proposed machine learning-based locomotion detection method had a median of 96.8% specificity, 93.0% sensitivity, 96.4% accuracy, and 78.6% F1-score in detecting walking bouts at home. The high performance of the proposed algorithm showed the feasibility of the unsupervised mobility assessment introduced in this study.

中文翻译：

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迈向步行步伐和速度的远程评估：在多发性硬化症患者中的应用。

步态速度是行动能力的强大生物标记，大多数情况下是在受控环境中（例如在诊所中）进行评估的。使用可穿戴的惯性传感器，可以客观地估算步态速度。但是，大多数先前的工作已经在临床环境中验证了步态速度估计算法，该算法可能不同于患者表现出实​​际表现的家庭评估。此外，为了给用户提供舒适感，设计基于单个传感器设置的算法至关重要。为此，本研究的目标是开发和验证一种新的步态速度估计方法，该方法基于机器学习方法，以通过下背部的传感器在临床和家庭评估中预测步态速度。此外，引入了两种方法来检测在家中日常活动中的步行跳动。我们已经在35例多发性硬化症患者中验证了该算法，因为它经常会出现行动困难。因此，可以在步态受损或步态缓慢时显示算法的鲁棒性。针对银标准的多传感器参考，我们获得了接近零的偏差和0.15 m / s的步态速度估计精度。此外，提出的基于机器学习的运动检测方法在在家中检测步行发作时的中位数特异性为96.8％，灵敏度为93.0％，准确性为96.4％，F1分数为78.6％。所提出算法的高性能表明了这项研究中引入的无监督移动性评估的可行性。该算法的鲁棒性可以在步态受损或步态缓慢时显示出来。针对银标准的多传感器参考，我们获得了接近零的偏差和0.15 m / s的步态速度估计精度。此外，提出的基于机器学习的运动检测方法在在家中检测步行发作时的中位数特异性为96.8％，灵敏度为93.0％，准确性为96.4％，F1分数为78.6％。所提出算法的高性能表明了这项研究中引入的无监督移动性评估的可行性。该算法的鲁棒性可以在步态受损或步态缓慢时显示出来。针对银标准的多传感器参考，我们获得了接近零的偏差和0.15 m / s的步态速度估计精度。此外，提出的基于机器学习的运动检测方法在在家中检测步行发作时的中位数特异性为96.8％，灵敏度为93.0％，准确性为96.4％，F1分数为78.6％。所提出算法的高性能表明了这项研究中引入的无监督移动性评估的可行性。在家中检测步行性跳动的F1得分为6％。所提出算法的高性能表明了这项研究中引入的无监督移动性评估的可行性。在家中检测步行搏动的F1得分为6％。所提出算法的高性能表明了这项研究中引入的无监督移动性评估的可行性。