Fall Detection FPGA-Based Systems: A Survey (original) (raw)
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A FPGA threshold-based fall detection algorithm for elderly fall monitoring with verilog
Bulletin of Electrical Engineering and Informatics, 2021
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 thresholdbased 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.
An Fpga Implementation Of Intelligent Visual Based Fall Detection
2013
Falling has been one of the major concerns and threats to the independence of the elderly in their daily lives. With the worldwide significant growth of the aging population, it is essential to have a promising solution of fall detection which is able to operate at high accuracy in real-time and supports large scale implementation using multiple cameras. Field Programmable Gate Array (FPGA) is a highly promising tool to be used as a hardware accelerator in many emerging embedded vision based system. Thus, it is the main objective of this paper to present an FPGA-based solution of visual based fall detection to meet stringent real-time requirements with high accuracy. The hardware architecture of visual based fall detection which utilizes the pixel locality to reduce memory accesses is proposed. By exploiting the parallel and pipeline architecture of FPGA, our hardware implementation of visual based fall detection using FGPA is able to achieve a performance of 60fps for a series of v...
DESIGN AND DEVELOPMENT OF FALL DETECTOR USING FALL ACCELERATION
The concept is to have a fall detection system which sends alarm to the concerned people or to the doctor, at the time of eventuality. To minimize fall and its related injuries continuous surveillance of subjects who are diseased and prone to fall is necessary. The article discusses the design and development of a prototype of an electronic gadget which is used to detect fall among elderly and the patients who are prone to it. In this article, the body posture is derived from change of acceleration in three axes, which is measured using triaxial accelerometer (adxl335). The sensor is placed on the lumbar region to study the tilt angle. The acceleration values in each axis are compared twice with threshold and also a delay of 20 secs between two comparisons, to reduce the false alarms. Values of the threshold voltage are selected by experimental methods. The algorithm is executed by microcontroller (PIC16F877A). The location of fall is determined by GPS receiver, which is programmed to track the subject continuously. On detection of fall, the device sends a text message through GSM modem, and communicates it to computer through ZigBee transceivers. The device can also be switched to only alarm if text message is not required. The prototype developed is tested on many subjects and also on volunteers who simulated fall. Out of 50 trials 96% of accuracy is achieved with zero false alarms for daily activities like jogging, skipping, walking on stairs, and picking up objects.
DESIGN AND DEVELOPMENT OF FALL DETECTOR USING FALL
The concept is to have a fall detection system which sends alarm to the concerned people or to the doctor, at the time of eventuality. To minimize fall and its related injuries continuous surveillance of subjects who are diseased and prone to fall is necessary. The article discusses the design and development of a prototype of an electronic gadget which is used to detect fall among elderly and the patients who are prone to it. In this article, the body posture is derived from change of acceleration in three axes, which is measured using triaxial accelerometer (adxl335). The sensor is placed on the lumbar region to study the tilt angle. The acceleration values in each axis are compared twice with threshold and also a delay of 20 secs between two comparisons, to reduce the false alarms. Values of the threshold voltage are selected by experimental methods. The algorithm is executed by microcontroller (PIC16F877A). The location of fall is determined by GPS receiver, which is programmed to track the subject continuously. On detection of fall, the device sends a text message through GSM modem, and communicates it to computer through ZigBee transceivers. The device can also be switched to only alarm if text message is not required. The prototype developed is tested on many subjects and also on volunteers who simulated fall. Out of 50 trials 96% of accuracy is achieved with zero false alarms for daily activities like jogging, skipping, walking on stairs, and picking up objects.
Evaluation of accelerometer-based fall detection algorithms on real-world falls
PloS one, 2012
Despite extensive preventive efforts, falls continue to be a major source of morbidity and mortality among elderly. Real-time detection of falls and their urgent communication to a telecare center may enable rapid medical assistance, thus increasing the sense of security of the elderly and reducing some of the negative consequences of falls. Many different approaches have been explored to automatically detect a fall using inertial sensors. Although previously published algorithms report high sensitivity (SE) and high specificity (SP), they have usually been tested on simulated falls performed by healthy volunteers. We recently collected acceleration data during a number of real-world falls among a patient population with a high-fall-risk as part of the SensAction-AAL European project. The aim of the present study is to benchmark the performance of thirteen published fall-detection algorithms when they are applied to the database of 29 real-world falls. To the best of our knowledge, this is the first systematic comparison of fall detection algorithms tested on real-world falls. We found that the SP average of the thirteen algorithms, was (mean6std) 83.0%630.3% (maximum value = 98%). The SE was considerably lower (SE = 57.0%627.3%, maximum value = 82.8%), much lower than the values obtained on simulated falls. The number of false alarms generated by the algorithms during 1-day monitoring of three representative fallers ranged from 3 to 85. The factors that affect the performance of the published algorithms, when they are applied to the real-world falls, are also discussed. These findings indicate the importance of testing fall-detection algorithms in real-life conditions in order to produce more effective automated alarm systems with higher acceptance. Further, the present results support the idea that a large, shared real-world fall database could, potentially, provide an enhanced understanding of the fall process and the information needed to design and evaluate a high-performance fall detector.
Increased Fall Detection Accuracy in an Accelerometer-Based Algorithm Considering Residual Movement
Every year over 11 million falls are registered. Falls play a critical role in the deterioration of the health of the elderly and the subsequent need of care. This paper presents a fall detection system running on a smartwatch (F2D). Data from the accelerometer is collected, passing through an adaptive threshold-based algorithm which detects patterns corresponding to a fall. A decision module takes into account the residual movement of the user, matching a detected fall pattern to an actual fall. Unlike traditional systems which require a base station and an alarm central, F2D works completely independently. To the best of our knowledge, this is the first fall detection system which works on a smartwatch, being less stigmatizing for the end user. The fall detection algorithm has been tested by Fondation Suisse pour les Téléthèses (FST), the project partner for the commercialization of our system. Taking advantage of their experience with the end users, we are confident that F2D meets the demands of a reliable and easily extensible system. This paper highlights the innovative algorithm which takes into account residual movement to increase the fall detection accuracy and summarizes the architecture and the implementation of the fall detection system.
Fall detection system with 3-axis accelerometer
Journal of Physics: Conference Series, 2019
In this work, the designed and developed process of a fall detection system using a NodeMCU microcontroller is described. A 3-axis acceleration sensor is used to obtain the data of the movement of humans and changes in acceleration are used to determine trends in falling. The ESP8266 microcontroller is used to the receive the data in real time and alert the family through the LINE application. Experimental results show that the system is capable of precisely producing alarms for falling actions and has a high detection precision and effectiveness.
Arabian Journal for Science and Engineering, 2018
This paper proposes to develop of a low cost fall detection system to precisely detect an event where an elderly person accidentally falls. The fall detection algorithm compares the acceleration with Lower fall Threshold (LFT) and Upper Fall Threshold (UFT) values to accurately detect a fall event. The post-fall recognition module is the combination of posture recognition and vertical velocity estimation that has been added in our proposed method to enhance the performance and accuracy of it. In case of a fall, our device will transmit the location information to the contacts instantly via SMS and voice call. The smartphone application will ensure that the notification is delivered to the elderly person's relatives so medical attention can be provided with minimal delay. The system was tested with volunteers and achieved 100% sensitivity and accuracy. This was confirmed by testing with public data and it also achieved the same percentage in sensitivity and accuracy as in our recorded data.
Reliable fall detection system using an 3-DOF accelerometer and cascade posture recognitions
Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, 2014
An unintentional fall can make injure to elderly. This paper aims to develop a portable and efficient device to monitor the falls in the elderly population by integrating a micro controller, a 3-DOF acceleration sensor, a GSM/GPRS modem and corresponding embedded fall detection algorithms. This system can work well in both indoor and outdoor environments. The human activities can be sensed by the low-cost and low power 3-DOF accelerometer. The acceleration signals are brought to the micro controller to monitor and alert the fall events. The cascade posture recognitions are proposed to enhance the fall detection accuracy by determining if the posture is a result of a fall. If the people fall, an alert message would be sent to their relative or a health center via the GSM/GPRS modem.
2011-Recognition of false alarms in fall detection systems
Falls are a major cause of hospitalization and injuryrelated deaths among the elderly population. The detrimental effects of falls, as well as the negative impact on health services costs, have led to a great interest on fall detection systems by the health-care industry. The most promising approaches are those based on a wearable device that monitors the movements of the patient, recognizes a fall and triggers an alarm. Unfortunately such techniques suffer from the problem of false alarms: some activities of daily living are erroneously reported as falls, thus reducing the confidence of the user. This paper presents a novel approach for improving the detection accuracy which is based on the idea of identifying specific movement patterns into the acceleration data. Using a single accelerometer, our system can recognize these patterns and use them to distinguish activities of daily living from real falls; thus the number of false alarms is reduced.