scholarly journals A FPGA threshold-based fall detection algorithm for elderly fall monitoring with verilog

2021 ◽  
Vol 10 (5) ◽  
pp. 2477-2487
Author(s):  
Pui Mun Lo ◽  
Azniza Abd Aziz
Keyword(s):  
Elderly Population ◽  
Unintentional Injury ◽  
Fall Detection ◽  
Body Part ◽  
Detection Algorithm ◽  
Medical Attention ◽  
Sitting Position ◽  
Lower Body ◽  
Serious Injuries ◽  
Fall Detector

Fall is one of the leading causes of accidental or unintentional injury deaths worldwide due to serious injuries such as head traumas and hip fractures. As life expectancy improved, the rapid increase in aging population implied the need for the development of vital sign detector such as fall detector to help elderly in seeking for medical attention. Immediate rescue could prevent victims from the risk of suspension trauma and reduce the mortality rate among elderly population due to fall accident effectively. This paper presents the development of FPGA-based fall detection algorithm using a threshold-based analytical method. The proposed algorithm is to minimize the rate of false positive fall detection proposed from other researchers by including the non-fall events in the data analysis. Based on the performance evaluation, the proposed algorithm successfully achieved a sensitivity of 97.45% and specificity of 97.38%. The proposed algorithm was able to differentiate fall events and non-fall events effectively, except for fast lying and fall that ending with sitting position. The proposed algorithm shows a good result and the performance of the proposed algorithm can be further improved by using an additional gyroscope to detect the posture of the lower body part.

scholarly journals Implementation and Application of Fall Detection on Mobile Perception Robot

2021 ◽  
Vol 2136 (1) ◽  
pp. 012053
Author(s):  
Zeyu Chen
Keyword(s):  
Elderly Population ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Data Sets ◽  
Construction Technology ◽  
Robot Operating System ◽  
Map Construction ◽  
Robot Perception ◽  
Window Method

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.

Design and Realization of an Early Pre-Impact Fall Alarm System Based on MEMS Inertial Sensing Units

2013 ◽  
Vol 461 ◽  
pp. 659-666
Author(s):  
Hui Qi Li ◽  
Ding Liang ◽  
Yun Kun Ning ◽  
Qi Zhang ◽  
Guo Ru Zhao
Keyword(s):  
Elderly People ◽  
Unintentional Injury ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Detection Sensitivity ◽  
Alarm System ◽  
Inertial Sensing ◽  
Security Problem ◽  
Fall Protection

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.

scholarly journals Hardware Design of the Energy Efficient Fall Detection Device

2016 ◽  
Vol 53 (2) ◽  
pp. 58-67
Author(s):  
A. Skorodumovs ◽  
E Avots ◽  
J Hofmanis ◽  
G. Korāts
Keyword(s):  
Elderly People ◽  
Power Efficiency ◽  
Injury Risk ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Health Issues ◽  
Long Time ◽  
Unintentional Falls ◽  
Fall Detector

Abstract Health issues for elderly people may lead to different injuries obtained during simple activities of daily living. Potentially the most dangerous are unintentional falls that may be critical or even lethal to some patients due to the heavy injury risk. In the project “Wireless Sensor Systems in Telecare Application for Elderly People”, we have developed a robust fall detection algorithm for a wearable wireless sensor. To optimise the algorithm for hardware performance and test it in field, we have designed an accelerometer based wireless fall detector. Our main considerations were: a) functionality – so that the algorithm can be applied to the chosen hardware, and b) power efficiency – so that it can run for a very long time. We have picked and tested the parts, built a prototype, optimised the firmware for lowest consumption, tested the performance and measured the consumption parameters. In this paper, we discuss our design choices and present the results of our work.

Fall detection smart-shoe enabled with wireless IoT device

Circuit World ◽  
2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abanah Shirley J. ◽  
Esther Florence Sundarsingh ◽  
Saraswathi V. ◽  
Sankareshwari S. ◽  
Sona S.
Keyword(s):  
Design Methodology ◽  
Threshold Value ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Medical Attention ◽  
Hardware Complexity ◽  
Rule Based ◽  
Content Type ◽  
Force Sensitivity ◽  
Compact Module

Purpose Fall detection is a primary necessity for elderly people with medically tested nervous problems. This paper aims important to detect fall and prevent fatal injuries and untreated attention for long hours. Design/methodology/approach The project is focused on developing a smart shoe with force-sensitive resistors placed at plantar pressure points to detect fall. This could draw immediate medical attention to the patient. The device is developed using sensors, microcontroller and accelerometer integrated into a compact module. A rule-based detection algorithm helps in transmitting the alert to an Internet of Things device when a fall is detected. Findings Based on the pressure applied, there is a change in resistive value of force sensitivity resistor. When it reaches the threshold value, fall gets detected and alert gets triggered through telegram bot with latitude and longitude details of the location. Originality/value The challenge in developing this device is to make it wearable reducing the overall hardware complexity. The entire module placed inside the sole of the shoe avoids inconvenience to the patients.

DEVELOPMENT OF FALL DETECTION ALGORITHM FOR TELEMONITORING SYSTEM

2019 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
C. A. MEBRIM ◽  
O. C. UBADIKE ◽  
A. M. AIBINU ◽  
I. I. ALEGBELEYE ◽  
A. J. ONUMANYI ◽  
...  
Keyword(s):  
Fall Detection ◽  
Detection Algorithm

scholarly journals Human Fall Detection Algorithm Design Based on Sensor Fusion and Multi-threshold Comprehensive Judgment

2020 ◽  
Vol 32 (4) ◽  
pp. 1209 ◽  
Author(s):  
Junsuo Qu ◽  
Chen Wu ◽  
Qian Li ◽  
Ting Wang ◽  
Abdel Hamid Soliman
Keyword(s):  
Sensor Fusion ◽  
Algorithm Design ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Comprehensive Judgment

Fall Detection Algorithm using Body Angle for Accurate Classification of Falls and ADLs

2021 ◽  
Author(s):  
Ali Ibrahim ◽  
Kabalan Chaccour ◽  
Georges Badr ◽  
Amir Hajjam El Hassani
Keyword(s):  
Fall Detection ◽  
Detection Algorithm ◽  
Body Angle

scholarly journals A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1831
Author(s):  
Armando Collado-Villaverde ◽  
Mario Cobos ◽  
Pablo Muñoz ◽  
David F. Barrero
Keyword(s):  
Machine Learning ◽  
Elderly Population ◽  
Real Data ◽  
Fall Detection ◽  
Wearable Devices ◽  
Accelerometer Data ◽  
Detection Systems ◽  
Detection Software ◽  
Health Complications ◽  
Public Datasets

People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.

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