A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials
A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials
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DOI：
10.1007/s11517-016-1504-y
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发表时间：
2017-01-01
影响因子：
3.2
通讯作者：
Robinovitch, Stephen N.
中科院分区：
文献类型：
--
作者：
Aziz, Omar;Musngi, Magnus;Robinovitch, Stephen N.
跌倒是老年人与伤害相关的发病率和死亡率的主要原因。在老年人中，超过90%的髋关节和手腕骨折以及60%的创伤性脑损伤是由跌倒引起的。老年人跌倒的另一个严重后果是，跌倒后无法站起来并长时间躺在地上的人会经历“长时间的谎言”。在过去的十年里，人们对可穿戴式传感器系统的设计进行了大量的研究，这种系统可以自动检测跌倒并向护理人员发送警报，以减少长时间撒谎的频率和严重性。虽然到目前为止描述的大多数系统都采用了基于阈值的算法，但机器学习算法可能会在检测跌倒时提供更高的精度。在目前的研究中，我们通过对10名年轻参与者进行一系列全面的坠落实验，比较了这两种方法在检测跌倒方面的准确性。参与者佩戴安装在腰部的三轴加速计，模拟老年人观察到的最常见的跌倒原因，以及近距离摔倒和日常生活活动。五种机器学习算法的总体性能高于文献中描述的五种基于阈值的算法的性能，其中支持向量机提供了最高的灵敏度和特异度组合。
Falls are the leading cause of injury-related morbidity and mortality among older adults. Over 90 % of hip and wrist fractures and 60 % of traumatic brain injuries in older adults are due to falls. Another serious consequence of falls among older adults is the 'long lie' experienced by individuals who are unable to get up and remain on the ground for an extended period of time after a fall. Considerable research has been conducted over the past decade on the design of wearable sensor systems that can automatically detect falls and send an alert to care providers to reduce the frequency and severity of long lies. While most systems described to date incorporate threshold-based algorithms, machine learning algorithms may offer increased accuracy in detecting falls. In the current study, we compared the accuracy of these two approaches in detecting falls by conducting a comprehensive set of falling experiments with 10 young participants. Participants wore waist-mounted tri-axial accelerometers and simulated the most common causes of falls observed in older adults, along with near-falls and activities of daily living. The overall performance of five machine learning algorithms was greater than the performance of five threshold-based algorithms described in the literature, with support vector machines providing the highest combination of sensitivity and specificity.