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Distinguishing the causes of falls in humans using an array of wearable tri-axial accelerometers

机译：使用一系列可穿戴式三轴加速度计来识别跌倒的原因

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Falls are the number one cause of injury in older adults. Lack of objective evidence on the cause and circumstances of falls is often a barrier to effective prevention strategies. Previous studies have established the ability of wearable miniature inertial sensors (accelerometers and gyroscopes) to automatically detect falls, for the purpose of delivering medical assistance. In the current study, we extend the applications of this technology, by developing and evaluating the accuracy of wearable sensor systems for determining the cause of falls. Twelve young adults participated in experimental trials involving falls due to seven causes: slips, trips, fainting, and incorrect shifting/transfer of body weight while sitting down, standing up from sitting, reaching and turning. Features (means and variances) of acceleration data acquired from four tri-axial accelerometers during the falling trials were input to a linear discriminant analysis technique. Data from an array of three sensors (left ankle. +. right ankle. +. sternum) provided at least 83% sensitivity and 89% specificity in classifying falls due to slips, trips, and incorrect shift of body weight during sitting, reaching and turning. Classification of falls due to fainting and incorrect shift during rising was less successful across all sensor combinations. Furthermore, similar classification accuracy was observed with data from wearable sensors and a video-based motion analysis system. These results establish a basis for the development of sensor-based fall monitoring systems that provide information on the cause and circumstances of falls, to direct fall prevention strategies at a patient or population level.
机译：跌倒是老年人受伤的第一原因。缺乏关于跌倒的原因和情况的客观证据通常是有效预防策略的障碍。先前的研究已经建立了可穿戴微型惯性传感器（加速度计和陀螺仪）自动检测跌倒的能力，以提供医疗救助。在当前的研究中，我们通过开发和评估可穿戴传感器系统的准确性来确定跌倒原因，从而扩展了该技术的应用范围。十二名年轻人参加了涉及以下七种原因的跌倒实验试验：滑倒，绊倒，昏厥以及坐下，站立时站立，伸手和转身时体重的不正确转移/转移。在跌落试验期间从四个三轴加速度计获取的加速度数据的特征（均值和方差）被输入到线性判别分析技术中。来自由三个传感器（左脚踝+。右脚踝+。胸骨）组成的数组的数据在归因于滑倒，绊倒以及在坐着，伸直和伸直和转。在所有传感器组合中，由于晕倒和上升过程中的不正确偏移而导致的跌倒分类不太成功。此外，使用可穿戴式传感器和基于视频的运动分析系统得到的数据，观察到了相似的分类精度。这些结果为开发基于传感器的跌倒监测系统奠定了基础，该系统提供有关跌倒原因和情况的信息，以指导患者或人群的跌倒预防策略。

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