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.

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 Implementation of Fall Detection and Localized Caring System

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Ming-Chih Chen ◽  
Yi-Wen Chiu ◽  
Chien-Hsing Chen ◽  
Ei-Jo Chen
Keyword(s):  
Health Care ◽  
Wireless Sensor Network ◽  
Mobile Phone ◽  
Elderly People ◽  
Medical Staff ◽  
Care Center ◽  
Fall Detection ◽  
Wireless Sensor ◽  
Health Care Center ◽  
Care Services

We propose the fall detection and localized caring system to effectively detect the postures of a human and provide a service of remote connection with health care center. Especially, when elderly people fall down, they often need to be hospitalized. The system can inform the care center automatically through the wireless sensor network and send the rapid information of the incident for their family through the mobile phone. It also provides the location of incident for immediate rescue by dispatching medical staff from the center. The experimental results show that our system achieves the accurate rate of 99.9% for detecting a human fall and provides the care services effectively.

Motion Analysis of Elderly People Based on Fall Detection Algorithm

2015 ◽  
Vol 12 (1) ◽  
pp. 209-212
Author(s):  
S.Vinuraj Kumar ◽  
K. Manikandan ◽  
N. Kumar
Keyword(s):  
Elderly People ◽  
Motion Analysis ◽  
Fall Detection ◽  
Detection Algorithm

Real-Time Autonomous Structural Change Detection Onboard Wireless Sensor Platforms

2013 ◽  
Vol 569-570 ◽  
pp. 970-977
Author(s):  
Ricardo Simon Carbajo ◽  
Esther Simon Carbajo ◽  
Biswajit Basu ◽  
Ciaran McGoldrick
Keyword(s):  
Structural Change ◽  
Change Detection ◽  
Real Time ◽  
Power Efficiency ◽  
Discrete Dynamical System ◽  
Detection Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Radio Communications ◽  
Hilbert Huang Transform

Real-time structural health monitoring is becoming increasingly tractable on commodity wireless sensor devices and platforms. Such algorithmic implementations must be realised in as efficient a manner as possible, in terms of memory, computation, radio communications and power efficiency. This work describes an efficient, real-time, structural change detection algorithm implemented on constrained, commodity wireless sensor nodes. The algorithm, based on the Hilbert Huang transform, initially characterises the structure and reliably signals subsequent change using a hierarchical monitoring and alert infrastructure. The system operates entirely autonomously, and algorithmic parameterisations, such as sensitivity and training period duration, can be dynamically and remotely adjusted across the air interface. The system has been evaluated on two different structures which were subject to structural change during the experiments; a Single-Degree-of-Freedom discrete dynamical system, and a 5kW wind turbine blade. The system demonstrated a highly reliable capacity to promptly detect and actuate response to structural change.

scholarly journals Wearable Inertial Sensing for ICT Management of Fall Detection, Fall Prevention, and Assessment in Elderly

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 91 ◽  
Author(s):  
Vincenzo Genovese ◽  
Andrea Mannini ◽  
Michelangelo Guaitolini ◽  
Angelo Sabatini
Keyword(s):  
Elderly People ◽  
Information And Communication Technologies ◽  
Elderly Care ◽  
Fall Detection ◽  
Measurement Unit ◽  
Data Logger ◽  
Accidental Injury ◽  
Health And Social Care ◽  
Ict Service ◽  
Fall Detector

Falls are one of the most common causes of accidental injury: approximately, 37.3 million falls requiring medical intervention occur each year. Fall-related injuries may cause disabilities, and in some extreme cases, premature death among older adults, which has a significant impact on health and social care services. In recent years, information and communication technologies (ICT) have helped enhance the autonomy and quality of life of elderly people, and significantly reduced the costs associated with elderly care. We designed and developed an integrated fall detection and prevention ICT service for elderly people, which was based on two wearable smart sensors, called, respectively, WIMU fall detector and WIMU data-logger (Wearable Inertial Measurement Unit, WIMU); their goal was either to detect falls and promptly react in case of fall events, or to quantify fall risk instrumentally. The WIMU fall detector is intended to be worn at the waist level for use during activities of daily living; the WIMU logger is intended for the quantitative assessment of tested individuals during the execution of clinical tests. Both devices provide their service in conjunction with an Android mobile device. The ICT service was developed and tested within the European project I-DONT-FALL (Integrated prevention and Detection sOlutioNs Tailored to the population and risk factors associated with FALLs, funded by EU, action EU CIP-ICT-PSP-2011-5: GA #CIP-297225). Sensor description and preliminary testing results are provided in this paper.

scholarly journals An Efficient Machine Learning-based Elderly Fall Detection Algorithm

2020 ◽  
Author(s):  
Faisal Hussain ◽  
Muhammad Basit Umair ◽  
Muhammad Ehatisham-ul-Haq ◽  
Ivan Miguel Pires ◽  
Tânia Valente ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elderly People ◽  
Daily Life ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Fall Incident ◽  
Efficient Machine

Abstract Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.

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.

Development of Database of Children’s Fall Dynamics Using Daily Behavior Observing System

2012 ◽  
Vol 24 (5) ◽  
pp. 802-810
Author(s):  
Hiroyuki Kakara ◽  
◽  
Yoshifumi Nishida ◽  
Sang Min Yoon ◽  
Hiroshi Mizoguchi ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Daily Living ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Living Environment ◽  
Sensor Data ◽  
Motion Data ◽  
Living Space ◽  
Biomechanical Simulation ◽  
Long Time

This paper describes the development of a fall database for biomechanical simulation. First, data on children’s daily activities were collected at a “sensor home,” which is a imitation daily living space. The sensor-based home comprises a video-surveillance system embedded into a daily-living environment and a wearable acceleration-gyro sensor. Falls were then detected from sensor data using a fall detection algorithm that we developed, and videos of detected falls were extracted from long-time recorded video. Extracted videos were used for fall motion analysis. A new Computer Vision (CV) algorithm was developed to automate fall motion analysis. Using the CV algorithm, fall motion data were accumulated into a database. The database allows a user to perform conditional searches for fall data by inputting search conditions, such as a child’s attributes, and fall situations.

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