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

Effective Falls Detection Method Using Two Tri-Axial Accelerometers

2013 ◽  
Vol 647 ◽  
pp. 854-860
Author(s):  
Gye Rok Jeon ◽  
Young Jae Kim ◽  
Ah Young Jeon ◽  
Sang Hoon Lee ◽  
Jae Hyung Kim ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Detection Rate ◽  
Elderly Population ◽  
Detection Method ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Systems

Falls detection systems have been developed in recent years because falls are detrimental events that can have a devastating effect on health of the elderly population. Current fall detecting methods mainly employ accelerometer to discriminate falls from activities of daily living (ADL). However, this makes it difficult to distinguish real falls from certain fall-like activities such as jogging and jumping. In this paper, an accurate fall detection system was implemented using two tri-axial accelerometers. By attaching the accelerometers on the chest and the abdomen, our system can effectively differentiate between falls and non-fall events.The Diff_Z and Sum_diff_Z parameter resulted in falls detection rate of 100%, respectively.

scholarly journals Possible Life Saver: A Review on Human Fall Detection Technology

Robotics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 55 ◽  
Author(s):  
Zhuo Wang ◽  
Vignesh Ramamoorthy ◽  
Udi Gal ◽  
Allon Guez
Keyword(s):  
Elderly Population ◽  
Fall Detection ◽  
The Elderly ◽  
Severe Injuries ◽  
Detection Systems ◽  
Detection Technology ◽  
Ambient Systems ◽  
Measure Of Performance ◽  
Privacy Issues ◽  
Current Systems

Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.

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 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.

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