Abstract
Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into “Fall” and “Activities of daily living (ADL)” while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into “Fall” and “ADL.” The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.
Highlights
Falls are the leading cause of fatal injuries in the elderly, such as fractures
By combining an impulse-radio ultra wideband (IR-ultra wideband (UWB)) radar sensor and a convolutional neural network (CNN) algorithm, this study proposed a development framework for a fall/Activities of daily living (ADL) classifier that can classify the behavior types of objects
To interpret IR-UWB radar sensor data using the CNN algorithm, we proposed a preprocessing method that converts one-dimensional time series signal data of IR-UWB radar sensor to a distance-time two dimensional image that contains features of signal change effectively
Summary
Falls are the leading cause of fatal injuries in the elderly, such as fractures. According to the World Health Organization [1], between 28% and 35% of the population aged 65 years and older experience at least one fall each year. These falls account for at least 50%. Medical analyses of the damage caused by falls have demonstrated that this is highly dependent on the response and rescue time [2]. The earlier the occurrence of the fall is discovered, the lower the chance of fatality due to secondary damage. Death refers to the phenomenon of a person dying alone and remaining undiscovered for several days, in which the person typically lives in a house and has no one to nurse or care for him or her
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