Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear

Abstract

The increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively.

Detecting Elderly Behaviors Based on Deep Learning for Healthcare: Recent Advances, Methods, Real-World Applications and Challenges

“…Hybridizing deep learning could be in different tracks like hybridizing two or more different architecture of the deep learning or hybridizing the deep learning architecture with a shallow traditional algorithm. For example, [29] hybridized RestNet-50 and support vector machine (SVM), [30] hybridized CNN-LSTM and auto/encoder-CNN-LSTM, [31] hybridized CNN-LSTM, [29] hybridized CNN and SVM, [32] hybridized VGG-16 and LSTM, [33] hybridized CNN and deep reinforcement learning (CNN-DQN), [34] hybridized ANN, CNN and LSTM (ANN-CNN-LSTM), [35] proposed hybrid of MLP-auto-encoder called dense auto-encoder (DAE) and hybrid of CNN-auto-encoder known as convolutional autoencoder (CAE)) algorithms. In examples provided in the previous paragraph, each constituent algorithm in the model play's particular role and the rest handles different roles to work cooperatively to generate the required result.…”

“…The result obtained showed that the CNN-DQN based agent surpasses the rule based agent. [34] hybridized ANN, CNN and LSTM (ANN-CNN-LSTM) for walking behaviour detection for possible health problems prevention in the elderly. As LSTM handles recurrence and learning of dependencies not only in short but also long term, the CNN uses the pooling layer for image map extraction and then the ANN does the detection of the walking behaviour, the result obtained shows that the has the potential to actually detect walking behaviour and prevent potential health problems in the elderly.…”

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