Wearable Computing Lab., ETH Zurich, Gloriastrasse 35, Zurich, Switzerland.
J Neuroeng Rehabil. 2013 Jul 30;10:83. doi: 10.1186/1743-0003-10-83.
Rehabilitation services use outcome measures to track motor performance of their patients over time. State-of-the-art approaches use mainly patients' feedback and experts' observations for this purpose. We aim at continuously monitoring children in daily life and assessing normal activities to close the gap between movements done as instructed by caregivers and natural movements during daily life. To investigate the applicability of body-worn sensors for motor assessment in children, we investigated changes in movement capacity during defined motor tasks longitudinally.
We performed a longitudinal study over four weeks with 4 children (2 girls; 2 diagnosed with Cerebral Palsy and 2 with stroke, on average 10.5 years old) undergoing rehabilitation. Every week, the children performed 10 predefined motor tasks. Capacity in terms of quality and quantity was assessed by experts and movement was monitored using 10 ETH Orientation Sensors (ETHOS), a small and unobtrusive inertial measurement unit. Features such as smoothness of movement were calculated from the sensor data and a regression was used to estimate the capacity from the features and their relation to clinical data. Therefore, the target and features were normalized to range from 0 to 1.
We achieved a mean RMS-error of 0.15 and a mean correlation value of 0.86 (p < 0.05 for all tasks) between our regression estimate of motor task capacity and experts' ratings across all tasks. We identified the most important features and were able to reduce the sensor setup from 10 to 3 sensors. We investigated features that provided a good estimate of the motor capacity independently of the task performed, e.g. smoothness of the movement.
We found that children's task capacity can be assessed from wearable sensors and that some of the calculated features provide a good estimate of movement capacity over different tasks. This indicates the potential of using the sensors in daily life, when little or no information on the task performed is available. For the assessment, the use of three sensors on both wrists and the hip suffices. With the developed algorithms, we plan to assess children's motor performance in daily life with a follow-up study.
康复服务利用结果测量来跟踪患者的运动表现随时间的变化。最先进的方法主要使用患者的反馈和专家的观察来实现这一目的。我们的目标是在日常生活中持续监测儿童，并评估正常活动，以缩小照护者指导下的动作与日常生活中自然动作之间的差距。为了研究可穿戴传感器在儿童运动评估中的适用性，我们在四周的时间内对四个孩子（两个女孩；两个患有脑瘫，两个患有中风，平均年龄为 10.5 岁）进行了纵向研究。每周，孩子们都会完成 10 个预先定义的运动任务。专家会评估质量和数量方面的能力，使用 10 个 ETH 方位传感器（ETHOS）监测运动，ETHOS 是一种小型且不显眼的惯性测量单元。传感器数据计算得出运动的平滑度等特征，并使用回归分析来根据特征及其与临床数据的关系来估计能力。因此，目标和特征被归一化为 0 到 1 的范围。
我们在所有任务中实现了回归估计运动任务能力与专家评分之间的平均均方根误差为 0.15 和平均相关值为 0.86（所有任务的 p 值均<0.05）。我们确定了最重要的特征，并能够将传感器设置从 10 个减少到 3 个。我们研究了一些特征，这些特征可以在不考虑执行任务的情况下很好地估计运动能力，例如运动的平滑度。
我们发现可以从可穿戴传感器评估儿童的任务能力，并且一些计算出的特征可以很好地估计不同任务的运动能力。这表明传感器在日常生活中具有潜力，尤其是在任务信息很少或无法获得的情况下。评估时，在两个手腕和臀部使用三个传感器就足够了。我们计划使用开发的算法在后续研究中评估儿童的日常生活中的运动表现。
