A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals

Ping Wang,Qimeng Li,Peng Yin,Zhonghao Wang,Raffaele Gravina,Yu Ling,Ye Li

doi:10.1007/s00521-021-06795-w

Abstract

According to the World Health Organization and other authorities, falls are one of the main causes of accidental injuries among the elderly population. Therefore, it is essential to detect and predict the fall activities of older persons in indoor environments such as homes, nursing, senior residential centers, and care facilities. Due to non-contact and signal confidentiality characteristics, radar equipment is widely used in indoor care, detection, and rescue. This paper proposes an adaptive channel selection algorithm to separate the activity signals from the background using an ultra-wideband radar and to generalize fused features of frequency- and time-domain images which will be sent to a lightweight convolutional neural network to detect and recognize fall activities. The experimental results show that the method is able to distinguish three types of fall activities (i.e., stand to fall, bow to fall, and squat to fall) and obtain a high recognition accuracy up to 95.7%.

Highlights

In today’s world, the population is growing and aging, resulting in an unbalanced population structure
This paper proposes an adaptive channel selection algorithm to separate the activity signals from the background using an ultra-wideband radar and to generalize fused features of frequency- and time-domain images which will be sent to a lightweight convolutional neural network to detect and recognize fall activities
In [31], the stationary and non-stationary clutters were removed by employing the singular value decomposition (SVD) algorithm when the signal-to-noise ratio (SNR) is low

Summary

Introduction

In today’s world, the population is growing and aging, resulting in an unbalanced population structure. The United Nations and Social Affairs reported that by 2050 the world’s population will reach 10 billion from the current 7.7 billion [1]. The population over the age of 65 accounts for about 9.1% (i.e., 701 million) and will reach 16% in 2050. Elderly care will be one of the most prominent issues in the world. According to the World Health Organization and other authorities, falls account for 50.96% of the causes of accidental injuries [2] and even deaths among older people. Timely fall detection and treatment are essential to protect the health of the elderly

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Conclusion