Use of Wireless Smart Sensors for Detecting Human Falls through Structural Vibrations (original) (raw)

A Smart and Passive Floor-Vibration Based Fall Detector for Elderly

International Conference on Information and Communication Technologies: From Theory to Applications, 2006

Falls are very prevalent among the elderly. They are the second leading cause of unintentional-injury death for people of all ages and the leading cause of death for elders 79 years and older. Studies have shown that the medical outcome of a fall is largely dependent upon the response and rescue time. Hence, a highly accurate automatic fall detector is

Fall detection systems - A solution based on low cost sensors

2010

The problem of fall detection in elderly patients is particularly critical in persons who live alone or are alone most of the day. The use of information and communication technologies to facilitate their autonomy is a clear example of how technological advances can improve the quality of life of dependent people. This article presents a prototype developed with a low cost device (the gamepad of a known video console) using its Bluetooth communication capabilities and built-in accelerometer. The latter is much more sensitive than other similar devices integrated in mobile phones and much cheaper than industrial accelerometers. Besides its stand-alone use, the system can be connected to a generic remote monitoring system that has been developed as a software product line for use in aged people's residences.

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.

Fall detection with distributed floor-mounted accelerometers: An overview of the development and evaluation of a fall detection system within the project eHome

Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, 2011

Within the project "eHome" a prototype of an assistive home system was developed, aiming to prolong the independent life of elderly people at home. Besides communication, e-access and safety relevant features, a core part of this system is an automatic fall detection, which utilizes floor-mounted accelerometers to gather body-sound signals that typically occur during a human fall. This approach targets to avoid acceptance, usability and reliability issues of available body-mounted fall detectors. The system was developed with focus on practical applicability, reliability and exploitability. The prototype was evaluated successfully in laboratory and during 507 days in reallife at homes of persons from the target group. During the laboratory trials a sensitivity of 87% and a specificity of 97.7% could be achieved for a defined fall scenario and across four tested floors. Further research is suggested to investigate floor dependencies of the fall detection performance.

IRJET- FALL DETECTION AND ALARM SYSTEM FOR ELDERLY

IRJET, 2021

Falls are a serious health concern for the elderly who live in the neighborhood. For more than two decades, medical institutions have conducted substantial research on falls in order to attenuate their impact (e.g., loss of freedom, fear of falling, etc.) and minimize their consequences (e.g., Cost of hospitalization, etc.). However, the subject of elderly people falling has piqued the interest of scientists as well as health experts. Indeed, falls have been the subject of several scientific investigations as well as the aim of numerous commercial products from academia and industry. These studies have addressed the issue by employing fall detection algorithms that have exhausted a range of sensor methods. Recently, researchers have moved their focus to fall prevention, with the goal of detecting falls before they occur. The chief contribution of this study in this matter is to offer a thorough outline of elderly falls and to recommend an all-purpose solution of fall-related systems based on real time posture estimation. Based on this common ground classification, an extensive research plan ranging from fall detection to fall prevention technologies was also carried out. Data processing techniques are also featured in both the fall detection and fall prevention courses. The goal of this effort is to provide medical technologists in the field of public health with a good understanding of fall-related systems.

IJERT-Review of Fall Detection and Alert Systems for Elderly People

International Journal of Engineering Research and Technology (IJERT), 2021

https://www.ijert.org/review-of-fall-detection-and-alert-systems-for-elderly-people https://www.ijert.org/research/review-of-fall-detection-and-alert-systems-for-elderly-people-IJERTV10IS050358.pdf Nowadays since people are busy due to their schedule, it's not always possible to keep someone at home to take care of elder person. Most of the people who have fallen cannot get up without assistance. Tissue injuries, joint dislocations, bone fractures ,and head trauma are some of the damages caused by falling. The absence of movement of a person after a fall may cause severe complications regarding health and may even lead to death if immediate assistance is not provided. Fall detection system using sensors are available in the market. But they need to be attached to the body to detect fall. The elderly may forget to wear them and they can cause discomfort too. In order to overcome these challenges, automatic fall detection and alert system can be used at the home for quicker assistance. The solutions in these papers are implemented using Machine Learning, Deep Learning and Computer Vision technology. In this paper, we discuss different methodologies to detect human falls. This paper is aimed towards analyzing the effectiveness of those methods for the detection of human falls.

A Cost-Effective Fall-Detection Framework for the Elderly Using Sensor-Based Technologies

Sustainability

Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that have been investigated by researchers for over a decade, and several techniques and methods have been proposed to detect fall events. To overcome and mitigate elderly fall issues, such as being left to lie for a long time after a fall, this project presents a low-cost, motion-based technique for detecting all events. In this study, we used IRA-E700ST0 pyroelectric infrared sensors (PIR) that are mounted on walls around or near the patient bed in a horizontal field of view to detect regular motions and patient fall events; we used PIR sensors along with Arduino Uno to detect patient falls and save the collected data in Arduino SD for classification. For data collection, 20 persons contributed as patients performing fall events. When a patient ...

Assistive Technology for Fall Detection Development of Integrated Wearable Sensor to Smart Home System

Anais Estendidos do XI SimpĆ³sio Brasileiro de Engenharia de Sistemas Computacionais (SBESC Estendido 2021), 2021

Fall detection is an assistive technology for elderly people that helps in emergency situations. This work presents the development of a wearable device to detect falls connected to a ultra low power wireless network. The device is connected to a smart home system to trigger alarms when events are detected. The fall detection is done by a threshold algorithm based on data fusion from inertial sensors. The wearable sensor is based on EnOcean protocol, which includes a wireless connection with a smart home system, according to the KNX standard, through the Home Assistant platform. The tests were performed in a prototype and the results include the evaluation of fall and nonfall movements in two different body characteristics. The results revealed sensitivity and specificity of up to 96% and 100%, respectively.

Development of Fall Risk Detector for Elderly

TELKOMNIKA Telecommunication Computing Electronics and Control, 2018

In Malaysia, falls has become the most common injuries for elderly. Therefore, a wearable fall detector device is created to decrease the risk of serious injury among elderly. The device consists of an accelerometer (ADXL345) as a sensor, an Arduino Nano as a microcontroller, and a Global System for Mobile Communications (GSM) as a notifier. A group of 15 young people participated in performing several sets of different falls and ADL (daily life activities) to determine the ability of the device. The result shows a good functioning performance by 92.6% sensitivity to detect fall and 89.3% specificity in discriminate fall from daily life activity.