scholarly journals Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Quoc T. Huynh ◽  
Uyen D. Nguyen ◽  
Lucia B. Irazabal ◽  
Nazanin Ghassemian ◽  
Binh Q. Tran
Keyword(s):  
Sensitivity And Specificity ◽  
Human Subjects ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Motion Data ◽  
Time Motion ◽  
High Acceleration ◽  
The Usa ◽  
Velocity Changes

Falling is a common and significant cause of injury in elderly adults (>65 yrs old), often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS) comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of various simulated daily activities (i.e., walking, running, stepping, and falling). Tests were conducted on 36 human subjects with a total of 702 different movements collected in a laboratory setting. Half of the dataset was used for development of the fall detection algorithm including investigations of critical sensor thresholds and the remaining dataset was used for assessment of algorithm sensitivity and specificity. Experimental results show that the algorithm detects falls compared to other daily movements with a sensitivity and specificity of 96.3% and 96.2%, respectively. The addition of gyroscope information enhances sensitivity dramatically from results in the literature as angular velocity changes provide further delineation of a fall event from other activities that may also experience high acceleration peaks.

scholarly journals Preimpact Fall Detection for Elderly Based on Fractional Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ning Liu ◽  
Dedi Zhang ◽  
Zhong Su ◽  
Tianrun Wang
Keyword(s):  
Social Cost ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Selection Method ◽  
Excellent Performance ◽  
Motion Data ◽  
Algorithm Framework ◽  
Warning Effect ◽  
Mean Filtering

The aging population has become a growing worldwide problem. Every year, deaths and injuries caused by elderly people's falls bring huge social costs. To reduce the rate of injury and death caused by falls among the elderly and the following social cost, the elderly must be monitored. In this context, falls detecting has become a hotspot for many research institutions and enterprises at home and abroad. This paper proposes an algorithm framework to prealarm the fall based on fractional domain, using inertial data sensor as motion data collection devices, preprocessing the data by axis synthesis and mean filtering, and using fractional-order Fourier transform to convert the collected data from time domain to fractional domain. Based on the above, a multilayer dichotomy classifier is designed, and each node parameter selection method is given, which constructed a preimpact fall detection system with excellent performance. The experiment result demonstrates that the algorithm proposed in this paper can guarantee better warning effect and classification accuracy with fewer features.

scholarly journals Implementation and Application of Fall Detection on Mobile Perception Robot

2021 ◽  
Vol 2136 (1) ◽  
pp. 012053
Author(s):  
Zeyu Chen
Keyword(s):  
Elderly Population ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Data Sets ◽  
Construction Technology ◽  
Robot Operating System ◽  
Map Construction ◽  
Robot Perception ◽  
Window Method

Abstract With the rapid increase in the number of people living in the elderly population, reducing and dealing with the problem of falls in the elderly has become the focus of research for decades. It is impossible to completely eliminate falls in daily life and activities. Detecting a fall in time can protect the elderly from injury as much as possible. This article uses the Turtlebot robot and the ROS robot operating system, combined with simultaneous positioning and map construction technology, Monte Carlo positioning, A* path planning, dynamic window method, and indoor map navigation. The YOLO network is trained using the stance and fall data sets, and the YOLOv4 target detection algorithm is combined with the robot perception algorithm to finally achieve fall detection on the turtlebot robot, and use the average precision, precision, recall and other indicators to measure.

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.

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.

Fall Detection Algorithm of the Elderly Based on BP Neural Network

2021 ◽  
Author(s):  
Qiushi Xiong ◽  
Danhong Chen ◽  
Ying Zhang ◽  
Zhen Gong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm

scholarly journals Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Dongha Lim ◽  
Chulho Park ◽  
Nam Ho Kim ◽  
Sang-Hoon Kim ◽  
Yun Seop Yu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Daily Living ◽  
Hidden Markov ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Threshold Method ◽  
Simple Method ◽  
Detection Systems

Falls are a serious medical and social problem among the elderly. This has led to the development of automatic fall-detection systems. To detect falls, a fall-detection algorithm that combines a simple threshold method and hidden Markov model (HMM) using 3-axis acceleration is proposed. To apply the proposed fall-detection algorithm and detect falls, a wearable fall-detection device has been designed and produced. Several fall-feature parameters of 3-axis acceleration are introduced and applied to a simple threshold method. Possible falls are chosen through the simple threshold and are applied to two types of HMM to distinguish between a fall and an activity of daily living (ADL). The results using the simple threshold, HMM, and combination of the simple method and HMM were compared and analyzed. The combination of the simple threshold method and HMM reduced the complexity of the hardware and the proposed algorithm exhibited higher accuracy than that of the simple threshold method.

scholarly journals Smart Fall Detection Framework Using Hybridized Video and Ultrasonic Sensors

Micromachines ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 508
Author(s):  
Feng-Shuo Hsu ◽  
Tang-Chen Chang ◽  
Zi-Jun Su ◽  
Shin-Jhe Huang ◽  
Chien-Chang Chen
Keyword(s):  
Density Functional ◽  
Probability Model ◽  
Three Dimensional ◽  
Fall Detection ◽  
The Elderly ◽  
Gaussian Mixture ◽  
Detection Accuracy ◽  
Movement Trajectory ◽  
Motion Data ◽  
Comparable Performance

Fall accidents can cause severe impacts on the physical health and the quality of life of those who suffer limb diseases or injuries, the elderly, and their caregivers. Moreover, the later the accident is discovered, the lower the chance of recovery of the injured one. In order to detect accidents earlier, we propose a data-driven human fall detection framework. By combining the sensing mechanism of a commercialized webcam and an ultrasonic sensor array, we develop a probability model for automatic human fall monitoring. The webcam and ultrasonic array respectively collect the transverse and longitudinal time-series signals from a moving subject, and then these signals are assembled as a three-dimensional (3D) movement trajectory map. We also use two different detection-tracking algorithms for recognizing the tracked subjects. The mean height of the subjects is 164.2 ± 12 cm. Based on the data density functional theory (DDFT), we use the 3D motion data to estimate the cluster numbers and their cluster boundaries. We also employ the Gaussian mixture model as the DDFT kernel. Then, we utilize those features to build a probabilistic model of human falling. The model visually exhibits three possible states of human motions: normal motion, transition, and falling. The acceptable detection accuracy and the small model size reveals the feasibility of the proposed hybridized platform. The time from starting the alarm to an actual fall is on average about 0.7 s in our platform. The proposed sensing mechanisms offer 90% accuracy, 90% sensitivity, and 95% precision in the data validation. Then these vital results validate that the proposed framework has comparable performance to the contemporary methods.

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