Design and Development of a Wireless Fall Detection Module for Homecare

2014 ◽  
Author(s):  
Paul C.-P. Chao ◽  
Li-Chi Hsu ◽  
Yu-Feng Li ◽  
Chin-Wei Chun
Keyword(s):  
Real Time ◽  
Fall Detection ◽  
The Elderly ◽  
Design And Development ◽  
Aging Society ◽  
Common Trend ◽  
Remote Hospital ◽  
Senior Population ◽  
Time Fall ◽  
Elderly Falls

A novel wireless circuit module is designed in this study to perform ubiquitous fall detections and then real-time fall detections of help messages. It is a common trend that as the demand for living quality increases tremendously while the technologies of electronics and medicine advances greatly, personal cares are elevated to the next level. As for the aging society, the issue of injuries due to falls among senior population arises rapidly [1,2]. Costly prices are often paid as the elderly falls without notice from companions at the site. Therefore, various modules and/or systems of automatic and wireless fall detection are developed into a past pace. Such fall-detection modules are demanded to be able to automatically detect falls of subjects and then send the help message to a remote hospital for an immediate help.

A Novel Real-Time Fall Detection System Based on Real-Time Video and Mobile Phones

2016 ◽  
Vol 26 (04) ◽  
pp. 1750056
Author(s):  
Chao Tong ◽  
Yu Lian ◽  
Yang Zhang ◽  
Zhongyu Xie ◽  
Xiang Long ◽  
...  
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Mobile Phones ◽  
Surveillance System ◽  
Fall Detection ◽  
The Elderly ◽  
Video Surveillance System ◽  
Detection Scheme ◽  
Detection Systems ◽  
Time Fall

In recent years, due to the growing population of the elderly, falls of elderly people have aroused wide public concern. Detecting timely falls of the elderly is significant to their safety. Numerous challenges exist in real-time fall detection systems because some features of normal human activities are greatly similar to the characteristics of falls. To address these problems, we propose a novel fall detection scheme and build a health-care system to detect falls of the elderly based on a real-time video surveillance system and a smart phone. The system contains two major modules. The first module is a feature extraction module. We adopt the Gaussian mixture model, tracking learning detecting algorithm and logpolar histogram to extract the characteristics of falls from the video surveillance system and the sensors embedded in mobile phones. The main purpose of the second module is to detect a fall-based on the features obtained in the first module. The experimental results show that every module is significant. Besides, our system is effective to separate falls from other similar actions such as bend down with an accuracy rate of more than 98% and performs better than other state-of-the-art fall detection systems.

Radar Based Real-Time Fall Detection System with Low Power Consumption

2021 ◽  
Author(s):  
Jincheng Lu ◽  
Zixuan Ou ◽  
Ziyu Liu ◽  
Cheng Han ◽  
Wenbin Ye
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Real Time ◽  
Detection System ◽  
Fall Detection ◽  
Low Power Consumption ◽  
Time Fall

Using a Smartphone as a Track and Fall Detector

2015 ◽  
pp. 272-295
Author(s):  
Chia-Yin Ko ◽  
Fang-Yie Leu ◽  
I-Tsen Lin
Keyword(s):  
Social Networks ◽  
Emergency Medical Services ◽  
Real Time ◽  
Family Caregivers ◽  
Fall Detection ◽  
The Elderly ◽  
Elderly Individual ◽  
Communication Services ◽  
People With Dementia

This chapter proposes a smartphone-based system for both indoor and outdoor monitoring of people with dementia. The whole system comprises wandering detection, safety-zone monitoring, fall detection, communication services, alert notifications, and emergency medical services. To effectively track the elderly, the proposed system uses a smartphone camera to take real-time pictures along the user's path as he or she moves about. Those photos, accompanied with time and GPS signals, are delivered to and stored on the Cloud system. When necessary, family caregivers can download those data to quickly find a way to help the elderly individual. Additionally, this study uses tri-axial accelerometers to examine falls. To assure individuals' data is safeguarded appropriately, an RSA method has been adopted by the system to encrypt stored data. This reliable and minimally intrusive system provides people with dementia with an opportunity to maintain their social networks and to improve their quality of lives.

Implementation of a real-time fall detection system for elderly Korean farmers using an insole-integrated sensing device

2019 ◽  
Vol 48 (1) ◽  
pp. 22-42 ◽  
Author(s):  
Insoo Kim ◽  
Kyung-Suk Lee ◽  
Kyungran Kim ◽  
Kyungsu Kim ◽  
Hye-Seon Chae ◽  
...  
Keyword(s):  
Real Time ◽  
Detection System ◽  
Fall Detection ◽  
Time Fall

Real-Time Fall Detection Using Uncalibrated Fisheye Cameras

2020 ◽  
Vol 12 (3) ◽  
pp. 588-600
Author(s):  
Konstantina N. Kottari ◽  
Konstantinos K. Delibasis ◽  
Ilias G. Maglogiannis
Keyword(s):  
Real Time ◽  
Fall Detection ◽  
Time Fall

scholarly journals The real-time elderly fall posture identifying scheme with wearable sensors

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988561
Author(s):  
Tao Xu ◽  
Wei Sun ◽  
Shaowei Lu ◽  
Ke-ming Ma ◽  
Xiaoqiang Wang
Keyword(s):  
Activities Of Daily Living ◽  
Real Time ◽  
Daily Living ◽  
Wearable Sensors ◽  
Smart Phone ◽  
The Elderly ◽  
The Body ◽  
Short Message ◽  
Triaxial Accelerometer ◽  
Time Fall

The accidental fall is the major risk for elderly especially under unsupervised states. It is necessary to real-time monitor fall postures for elderly. This paper proposes the fall posture identifying scheme with wearable sensors including MPU6050 and flexible graphene/rubber. MPU6050 is located at the waist to monitor the attitude of the body with triaxial accelerometer and gyroscope. The graphene/rubber sensors are located at the knees to monitor the moving actions of the legs. A real-time fall postures identifying algorithm is proposed by the integration of triaxial accelerometer, tilt angles, and the bending angles from the graphene/rubber sensors. A volunteer is engaged to emulate elderly physical behaviors in performing four activities of daily living and six fall postures. Four basic fall down postures can be identified with MPU6050. Integrated with graphene/rubber sensors, two more fall postures are correctly identified by the proposed scheme. Test results show that the accuracy for activities of daily living detection is 93.5% and that for fall posture identifying is 90%. After the fall postures are identified, the proposed system transmits the fall posture to the smart phone carried by the elderly via Bluetooth. Finally, the posture and location are transmitted to the specified mobile phone by short message.

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