Characterization of Body Movement for the Real Time Fall Detection Using Wireless Sensor Module

2013 ◽  
Vol 694-697 ◽  
pp. 1128-1134 ◽  
Author(s):  
Seong Hyun Kim ◽  
Dong Wook Kim
Keyword(s):  
Body Movement ◽  
Ground Surface ◽  
Fall Detection ◽  
The Elderly ◽  
The Body ◽  
Wireless Sensor ◽  
Sensor Module ◽  
Activity Of Daily Life ◽  
Time Fall

As fall is an accident being taken place ordinarily to the elderly, it is important to prevent fractures as a result of fall by detecting fall behavior in advance. The objective of this study is to distinguish activity of daily life (ADL) from fall by using wireless sensor module. This study intends to determine fall status before the body contacts ground surface after the fall starts. In this study, natural fall and acceleration being taken place during ADL were analyzed by using tri-axial accelerometer, tilt sensor and bluetooth module so that the body will not be bound at the time of fall. Test was performed on a soft mattress so that subjects will not be injured during the test process and fall was induced through rapid movement of mattress by using a pneumatic actuator. A sensor for detecting body movement was attached to the back and waist of the subject. Fall status was determined by using acceleration value being generated from the body and direction of fall was judged by angle value. As a result of the test, in case of using this system, fall status and its direction could be correctly detected.

The Analysis of Body Movement during a Fall by Using a Wireless Sensor Module and the Development of a Fall Detection Algorithm

2014 ◽  
Vol 522-524 ◽  
pp. 1137-1142
Author(s):  
Seong Hyun Kim ◽  
Dong Wook Kim
Keyword(s):  
Response Times ◽  
Body Movement ◽  
Movement Analysis ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
The Body ◽  
Acceleration Data ◽  
Acceleration Sensors ◽  
Sensor Module

As the society ages, the number of falls and fractures suffered by the elderly is increasing significantly in numbers. However, studies with reliable statistics and analysis on falls of this specific population were scarce. Fractures due to falls of the elderly are potentially of critical severity, and, therefore, it is important to detect such incidents with accuracy to prevent fractures. This necessitates an effective system to detect falls. For this reason, we induced simulated falls that resemble actual falls as much as possible by using a fall-inducing apparatus, and observed the movement of the body during the falls. The movement of the body was sensed using 3-axes acceleration sensors and bluetooth modules, which would not obstruct the movement as wired sensors or movement analysis systems would do. Using the acceleration data detected by the sensors, a fall detection algorithm was developed to detect a fall and, if any, its direction. Unlike existing studies that used sum-vectors and inclination sensors to detect the direction of falls, which took too much time, the system developed in this study could detect the direction of the fall by comparing only the acceleration data without requiring any other equations, resulting in faster response times.

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.

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.

scholarly journals Fall Detection Based on Key Points of Human-Skeleton Using OpenPose

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 744 ◽  
Author(s):  
Weiming Chen ◽  
Zijie Jiang ◽  
Hailin Guo ◽  
Xiaoyang Ni
Keyword(s):  
Human Body ◽  
Detection Method ◽  
Fall Detection ◽  
The Elderly ◽  
The Body ◽  
Critical Parameters ◽  
Height Ratio ◽  
Symmetry Principle ◽  
Key Points ◽  
Standing Up

According to statistics, falls are the primary cause of injury or death for the elderly over 65 years old. About 30% of the elderly over 65 years old fall every year. Along with the increase in the elderly fall accidents each year, it is urgent to find a fast and effective fall detection method to help the elderly fall.The reason for falling is that the center of gravity of the human body is not stable or symmetry breaking, and the body cannot keep balance. To solve the above problem, in this paper, we propose an approach for reorganization of accidental falls based on the symmetry principle. We extract the skeleton information of the human body by OpenPose and identify the fall through three critical parameters: speed of descent at the center of the hip joint, the human body centerline angle with the ground, and width-to-height ratio of the human body external rectangular. Unlike previous studies that have just investigated falling behavior, we consider the standing up of people after falls. This method has 97% success rate to recognize the fall down behavior.

WeMos IoT Controller-Based Low-Cost Fall Detection System for Elderly Users

2021 ◽  
Vol 50 ◽  
pp. 59-72
Author(s):  
Mohammed Faeik Ruzaij Al-Okby ◽  
Kerstin Thurow
Keyword(s):  
False Positive ◽  
Detection System ◽  
Low Cost ◽  
False Negative ◽  
Lithium Ion ◽  
Fall Detection ◽  
The Elderly ◽  
The Body ◽  
Detection Systems ◽  
Alarm Message

Fall detection systems for the elderly are very important to protect this type of users. The early detection of the fall of the elderly has a major impact on saving their lives and avoiding the deterioration of the negative medical effects resulting from the effect of the patient falling on a hard surface. One of the constraints in fall detection systems are false-negative errors (no fall detection) or false-positive errors (sending a false warning without real fall accident). These errors have to be reduced significantly. In this paper, an innovative method to reduce fall detection system errors is proposed. The system consists of two orientation detection sensors to track the body orientation instead of using a single sensor in the previous systems which enhances the system accuracy and reduces the false-negative and false-positive errors. The system uses a small size IoT-based controller to process the sensor's information and make the alarm decision based on specific thresholds. The output alarm of the system includes an email sent to the caregivers via the embedded Wi-Fi ESP8266 module as well as an SMS message to the caregivers’ phones via GSM modules to ensure that the alarm message arrives in the absence of internet coverage for the patient or the caregiver. The system is powered by a small lithium-Ion battery. All sensors and modules of the system are combined in a small rubber box that can be fixed in a waist belt or the chest rejoin of the user body. Several tests have been made in different procedures. The tests revealed that the new approach improves the accuracy of the system and reduces the possibility of triggering wrong alarms.

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.

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