Wearable Sensor Gait Analysis of Fall Detection using Attention Network.
Wearable Sensor Gait Analysis of Fall Detection using Attention Network.
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DOI：
10.1109/bibm52615.2021.9669795
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发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Audette, Michel
中科院分区：
文献类型：
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作者：
Yhdego, Haben;Li, Jiang;Paolini, Christopher;Audette, Michel
美国国家老龄化委员会的统计数据表明，美国每 19 分钟就有一名老年人因跌倒而死亡。通过检测跌倒事件，特别是当跌倒事件触发保护性安全气囊的气动启动时，可以改善对老年人的护理。这项工作的重点是检测老年人群中老年受试者即将发生的跌倒风险，制定通过气动安全气囊部署来减轻跌倒伤害的计划方法。随着可穿戴传感器的广泛采用，人们越来越重视有效处理加速度信号数据的跌倒预测模型。跌倒检测和步态分类是具有挑战性的任务，特别是在区分跌倒和接近跌倒方面。我们建议关注已知发生跌倒的加速度数据的深度神经网络 (DNN) 分析。我们取传感器信号的最大值来定义探测器的观察窗口。在带有词嵌入的 Transformer DNN 的支持下，注意力网络在自然语言处理 (NLP) 任务中实现了最先进的技术。除了 Transformer 在有效处理长序列方面的成功之外，它还支持快速计算的并行计算。在本文中，我们提出了一种新颖的变压器注意网络，用于基于 Time2Vec 位置编码的跌倒检测建模的步态分析 - 建立在 Masked Transformer 网络上。使用我们的数据集，我们证明所提出的方法比现有模型具有更好的特异性和敏感性。
The statistical data from the National Council on Aging indicates that a senior adult dies in the US from a fall every 19 minutes. The care of elderly people can be improved by enabling the detection of falling events, especially if it triggers the pneumatic actuation of a protective airbag. This work focuses on detecting impending fall risk of senior subjects within the geriatric population, towards a planned approach to mitigating fall injuries through pneumatic airbag deployment. With the widespread adoption of wearable sensors, there is an increased emphasis on fall prediction models that effectively cope with accelerometry signal data. Fall detection and gait classification are challenging tasks, especially in differentiating falls from near falls. We propose to apply attention to the deep neural network (DNN) analysis of acceleration data where a fall is known to have occurred. We take the maximum value of the sensor signals to define the observation window of the detector. Powered by a transformer DNN with word embedding, attention networks have achieved a state-of-the-art in natural language processing (NLP) tasks. Besides the success of the transformer for efficiently processing long sequences, it supports parallel computing with fast computation. In this paper, we propose a novel transformer attention network for gait analysis of fall detection modeling with Time2Vec positional encoding- founded on a Masked Transformer Network. Using our dataset, we demonstrate that the proposed approach achieves better specificity and sensitivity than the present models.