Wearable Sensor Gait Analysis of Fall Detection using Attention Network

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.