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

2019 ◽  
Vol 42 (4) ◽  
pp. 786-794 ◽  
Author(s):  
Hongtao Zhang ◽  
Muhannand Alrifaai ◽  
Keming Zhou ◽  
Huosheng Hu

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.

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2254
Author(s):  
Francisco Javier González-Cañete ◽  
Eduardo Casilari

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.


2021 ◽  
Vol 2136 (1) ◽  
pp. 012053
Author(s):  
Zeyu Chen

Abstract With the rapid increase in the number of people living in the elderly population, reducing and dealing with the problem of falls in the elderly has become the focus of research for decades. It is impossible to completely eliminate falls in daily life and activities. Detecting a fall in time can protect the elderly from injury as much as possible. This article uses the Turtlebot robot and the ROS robot operating system, combined with simultaneous positioning and map construction technology, Monte Carlo positioning, A* path planning, dynamic window method, and indoor map navigation. The YOLO network is trained using the stance and fall data sets, and the YOLOv4 target detection algorithm is combined with the robot perception algorithm to finally achieve fall detection on the turtlebot robot, and use the average precision, precision, recall and other indicators to measure.


2017 ◽  
Vol 17 (07) ◽  
pp. 1740021
Author(s):  
JIN SEUNG CHOI ◽  
DONG WON KANG ◽  
JEONG WOO SEO ◽  
TAE HO KIM ◽  
HAE MI CHO ◽  
...  

Fall is the main cause of activity decline for the elderly. In general, it has been known to be closely related to decline in muscle strength and physical balance. The age profile of the elderly has a very wide range from 60 to 90 or more. Therefore, this study was to determine age-specific risk factors for falling in elderly individuals. Elderly females were included either in the old group ([Formula: see text]75 years; [Formula: see text], with 11 fallers and 24 nonfallers) or the older group ([Formula: see text]75 years; [Formula: see text], with 14 fallers and 22 nonfallers). The following were assessed and compared between the age-based groups and between fall-based subgroups (fallers and nonfallers within a given age group): height, weight, mental state, balance on the Berg balance scale (BBS), muscle strength of nine muscle groups, and 20 variables of postural sway. The two age-based groups differed significantly in terms of height, weight, balance, and postural sway (total sway area, 95% ellipse area, root mean square, mean distance, and total power); on the other hand, the fall-based subgroups differed only in terms of the median frequency of postural sway. Furthermore, the strength of the hip abductors displayed an interaction effect between age and fall status, and the result of main effect analysis revealed significant differences between fallers from the two age-based groups. These results confirm that extraction of specific factors for each age group is necessary to perform fall detection and prevention studies for the healthy elderly.


2014 ◽  
Vol 522-524 ◽  
pp. 1137-1142
Author(s):  
Seong Hyun Kim ◽  
Dong Wook Kim

As the society ages, the number of falls and fractures suffered by the elderly is increasing significantly in numbers. However, studies with reliable statistics and analysis on falls of this specific population were scarce. Fractures due to falls of the elderly are potentially of critical severity, and, therefore, it is important to detect such incidents with accuracy to prevent fractures. This necessitates an effective system to detect falls. For this reason, we induced simulated falls that resemble actual falls as much as possible by using a fall-inducing apparatus, and observed the movement of the body during the falls. The movement of the body was sensed using 3-axes acceleration sensors and bluetooth modules, which would not obstruct the movement as wired sensors or movement analysis systems would do. Using the acceleration data detected by the sensors, a fall detection algorithm was developed to detect a fall and, if any, its direction. Unlike existing studies that used sum-vectors and inclination sensors to detect the direction of falls, which took too much time, the system developed in this study could detect the direction of the fall by comparing only the acceleration data without requiring any other equations, resulting in faster response times.


2013 ◽  
Vol 461 ◽  
pp. 659-666
Author(s):  
Hui Qi Li ◽  
Ding Liang ◽  
Yun Kun Ning ◽  
Qi Zhang ◽  
Guo Ru Zhao

Falls are the second leading cause of unintentional injury deaths worldwide, so how to prevent falls has become a safety and security problem for elderly people. At present, because the sensing modules of most fall alarm devices generally only integrate the single 3-axis accelerometer, so the measured accuracy of sensing signals is limited. It results in that these devices can only achieve the alarm of post-fall detection but not the early pre-impact fall recognition in real fall applications. Therefore, this paper aimed to develop an early pre-impact fall alarm system based on high-precision inertial sensing units. A multi-modality sensing module embedded fall detection algorithm was developed for early pre-impact fall detection. The module included a 3-axis accelerometer, a 3-axis gyroscope and a 3-axis magnetometer, which could arouse the information of early pre-impact fall warning by a buzzer and a vibrator. Total 81 times fall experiments from 9 healthy subjects were conducted in simulated fall conditions. By combination of the early warning threshold algorithm, the result shows that the detection sensitivity can achieve 98.61% with a specificity of 98.61%, and the average pre-impact lead time is 300ms. In the future, GPS, GSM electronic modules and wearable protected airbag will be embedded in the system, which will enhance the real-time fall protection and timely immediate aid immensely for the elderly people.


Author(s):  
Ghassan Samara ◽  
Mohammad Hassan ◽  
Yahya Zayed

Wireless sensor networks (WSNs) has a practical ability to link a set of sensors to build a wireless network that can be accessed remotely; this technology has become increasingly popular in recent years. Wi-Fi-enabled sensor networks (WSNs) are used to gather information from the environment in which the network operates. Many obstacles prevent wireless sensor networks from being used in a wide range of fields. This includes maintaining network stability and extending network life. In a wireless network, sensors are the most essential component. Sensors are powered by a battery that has a finite amount of power. The battery is prone to power loss, and the sensor is therefore rendered inoperative as a result. In addition, the growing number of sensor nodes off-site affects the network's stability. The transmission and reception of information between the sensors and the base consumes the most energy in the sensor. An Intelligent Vice Cluster Head Selection Protocol is proposed in this study (IVC LEACH). In order to achieve the best performance with the least amount of energy consumption, the proposed hierarchical protocol relies on a fuzzy logic algorithm using four parameters to calculate the value of each node in the network and divides them into three hierarchical levels based on their value. This improves network efficiency and reliability while extending network life by 50 percent more than the original Low Energy Adaptive Clustering Hierarchy protocol. Keywords: Wireless Sensor Networks, Sensors, Communication Protocol, Fuzzy logic, Leach protocol.


2021 ◽  
Author(s):  
Qiushi Xiong ◽  
Danhong Chen ◽  
Ying Zhang ◽  
Zhen Gong

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