A Machine Learning Approach for Fall Detection Based on the Instantaneous Doppler Frequency

Abstract

Modern societies are facing an ageing problem that is accompanied by increasing healthcare costs. A major share of this ever-increasing cost is due to fall-related injuries, which urges the development of fall detection systems. In this context, this paper paves the way for the development of radio-frequencybased fall detection systems, which do not require the user to wear any device and can detect falls without compromising the user's privacy. For the design of such systems, we present an activity simulator that generates the complex path gain of indoor channels in the presence of one person performing three different activities: slow fall, fast fall, and walking. We have developed a machine learning framework for activity recognition based on the complex path gain. Additionally, we propose a novel method that accurately estimates the instantaneous Doppler frequency (IDF) from the complex path gain. Then, we extract six features from the IDF and provide the feature vector as input to the classifier, which has to predict the user's activity. We assess the recognition accuracy of four different classification algorithms: K-nearest neighbors (KNN), decision tree, artificial neural network (ANN), and cubic support vector machine (SVM). Our analysis reveals that the KNN, decision tree, ANN, and cubic SVM achieve an overall recognition accuracy of 86.1%, 94%, 98.9%, and 99.9%, respectively. The best performing algorithm, cubic SVM, has a fall detection accuracy of 100% with zero false alarms and zero undetected falls, which represents the best achievable performance. By comparing our fall detection system with existing ones in the literature, we demonstrate the superiority of our proposed solution.

Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems

“…Furthermore, they show that it is necessary to establish the necessary trade-offs in terms of performance, time-consuming, complexity, and the number of features in these applications. A fall detection platform implemented in [ 26 ] shows the performance of different classification algorithms using six features extracted of the instantaneous Doppler frequency of a RF signal. Detection accuracy was increased by implementing different classification algorithms such as artificial neural network (ANN), K-nearest neighbors (KNN), quadratic support vector machine (QSVM), or ensemble bagged tree.…”

“…Support Vector Machine (SVM) was employed due to its balance between complexity and accuracy. Additionally, SVM has demonstrated its effectiveness in fall detection [ 13 , 26 ]. SVM classification allow us to measure the impact of the antenna orientation in the accuracy of the overall fall detection system.…”

Bedtime Monitoring for Fall Detection and Prevention in Older Adults

“…Although this work does not specifically evaluate user acceptance, the fact that it does not require any specific sensor or hardware is pointed out as a factor to increase acceptance. Similarly, the work in [ 28 ] also employs the 2.4 GHz WiFi band. The transmitter Tx emits electromagnetic waves that propagate in the indoor environment.…”

“…The analysis of the state of the art for fall detection systems shows that some works pay attention to such factors. In this sense, unobtrusiveness or being comfortable are pursued as a relevant factor for acceptance in [ 9 , 12 , 23 , 24 , 25 , 27 , 28 , 30 , 37 , 38 , 44 , 46 , 50 , 56 ]. Privacy and increased safety is pointed out as a user acceptance factor and pursued in [ 8 , 25 , 26 , 30 , 36 , 38 , 39 , 40 , 43 , 57 ].…”

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

“…There are other types of external sensing devices. Chelli et al and Wang et al used Wi-Fi to detect human motions with a support vector machine (SVM) [ 22 , 23 ]. Mokhtari et al developed a fall detection system based on an ultra-wide band (UWB) radar with an SVM [ 24 ].…”