A novel fuzzy logic algorithm for accurate fall detection of smart wristband

Abstract

Falling is a major cause of serious injury or even death for the elderly population. To improve the safety of elderly people, a wide range of wearable fall detection devices have been developed over recent years, such as smart watches, waistbands and other wearable fall detectors. However, most of these fall detection devices are threshold-based and have a high rate of false alarm. This paper presents a novel fuzzy logic fall detection algorithm used in smart wristbands to reduce false alarms and achieve accurate fall detection. Experiments have been conducted in our laboratory and the results show that the proposed algorithm can accurately distinguish fall events from non-fall daily activities such as walking, jumping, clapping, and so forth. It shows good potential for commercial applications.

A Novel On-Wrist Fall Detection System Using Supervised Dictionary Learning Technique

“…Consequently, the LRSDL has reached the best accuracy of 99.5%, when processed with Euler angles an input data and D presents a total of 400 atoms. [34], 2014 [7], 2014 [14], 2016 [19], 2018 [11], 2018 [20], 2019 [32], 2019 [33] We listed in Table 3 a full synthesis of performances, in terms of sensitivity, specificity, and accuracy of prior works related to the on-wrist fall detection system. Zheng et al [33] achieved the best accuracy performance of 99.86% with the use of an accelerometer and gyroscope using the Convolution Neural Network (CNN) architecture, yet very close with the one accomplished with our proposed study using a single sensor adopting a simpler algorithm SRC.…”

“…Considering that the wrist-worn devices are the most comfortable body location for the patient [18], they are yet very unstable for the IMU [32]. Since arms are usually very moving parts of the body, many hand movements, i.e clapping, rising, and releasing hands, may present similar motion patterns compared with fall movements.…”

“…Finding significant attributes that better illustrate the raw data has always been a challenge depending on the device's on-body position. For instance, most on-wrist solution presented in the literature depends mainly on accelerometer [4,11,20,34], while some others fuses it with other sensors like gyroscope [7,32,33], gyroscope and magnetometer [19], or heart rate sensor [14]. This work implements SDL for classification approaches in a wrist-based fall detection system with the aim of benefiting of its capacity to generate more discriminative features using sparse representation.…”

Design of a fuzzy safety margin derivation system for grip force control of robotic hand in precision grasp task

“…Fuzzy logic systems process inputs consisting of linguistic rules to produce an output. 28 Fuzzy logic, first introduced by Zadeh, 29 is a method that can model a specialist's reasoning and decision-making features with algorithms. Fuzzy logic is successfully used in industrial applications, where uncertainty is high and it is hard to find a complex and mathematical model.…”

Sistema de detección de caídas en personas utilizando vídeo vigilancia

“…tipo Takagi-Sugeno, porque este enfoque permite la integración de conocimientos específicos de las caídas como la capacidad para generalizar y ha sido utilizado como método de detección de caídas reduciendo las falsas alarmas [15]. El algoritmo difuso sugerido en este artículo utiliza la velocidad como entrada con 4 funciones de membresía (caminar, sentarse, agacharse y caer) y como salida 4 conjuntos tipo singleton.…”