Abstract
Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power.
Highlights
The world is currently experiencing an unprecedented aging of the population [1]
Experiment results have shown the proposed convolutional neural network (CNN) model could achieve an accuracy of 99.1%. Another CNN model composed of two convolutional and two max-pooling layers was used in [23] to detect falls and the results proved the CNN could achieve an accuracy of 98.61%
Conventional machine learning methods used in this work include support vector machine (SVM), decision tree (DT), k-nearest neighbors (KNN), and extreme gradient boosting method (XGB)
Summary
The world is currently experiencing an unprecedented aging of the population [1]. It has been estimated that the population of elder people aged 60 and over will keep increasing rapidly and exceed three billion by 2100. Neural networks have been increasingly popular in the machine learning field due to the improvement of computation force and the breakthrough of theory Their more advanced modeling capability has attracted a large amount of attention in the fall detection field [19]. Different types of lightweight neural networks, including the supervised and unsupervised models are explored in fall detection based on an accelerometer worn on the human waist. The performance of these lightweight neural networks is evaluated against both the conventional machine learning methods and the deep neural networks used in literature.
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