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.

Metabolic Methanol: Molecular Pathways and Physiological Roles

2015 ◽  
Vol 95 (2) ◽  
pp. 603-644 ◽  
Author(s):  
Yuri L. Dorokhov ◽  
Anastasia V. Shindyapina ◽  
Ekaterina V. Sheshukova ◽  
Tatiana V. Komarova
Keyword(s):  
Human Body ◽  
The Elderly ◽  
The Body ◽  
Alcoholic Beverages ◽  
Limited Attention ◽  
Metabolic Clearance ◽  
Healthy Human ◽  
Neurological Patients ◽  
Adenosyl Methionine ◽  
Low Levels

Methanol has been historically considered an exogenous product that leads only to pathological changes in the human body when consumed. However, in normal, healthy individuals, methanol and its short-lived oxidized product, formaldehyde, are naturally occurring compounds whose functions and origins have received limited attention. There are several sources of human physiological methanol. Fruits, vegetables, and alcoholic beverages are likely the main sources of exogenous methanol in the healthy human body. Metabolic methanol may occur as a result of fermentation by gut bacteria and metabolic processes involving S-adenosyl methionine. Regardless of its source, low levels of methanol in the body are maintained by physiological and metabolic clearance mechanisms. Although human blood contains small amounts of methanol and formaldehyde, the content of these molecules increases sharply after receiving even methanol-free ethanol, indicating an endogenous source of the metabolic methanol present at low levels in the blood regulated by a cluster of genes. Recent studies of the pathogenesis of neurological disorders indicate metabolic formaldehyde as a putative causative agent. The detection of increased formaldehyde content in the blood of both neurological patients and the elderly indicates the important role of genetic and biochemical mechanisms of maintaining low levels of methanol and formaldehyde.

Parametric Human Body Model for Digital Apparel Design

2013 ◽  
Vol 365-366 ◽  
pp. 121-124
Author(s):  
Shu Xia Wang ◽  
Sheng Feng Qin ◽  
Cong Ying Guan ◽  
Sui Huai Yu
Keyword(s):  
Cross Section ◽  
Human Body ◽  
Body Shape ◽  
Body Part ◽  
The Body ◽  
Human Model ◽  
Body Model ◽  
Key Points ◽  
Cloud Points ◽  
Body Scanner

With the advance in 3D body scanning technology, it opens opportunities for virtual try-on and automatic made-to-measure in apparel products domain. This paper proposed a novel feature-based parametric method of human body shape from the cloud points of 3D body scanner [T2. Firstly, we improved the skeleton construction through adding and adjusting the position of joints. Secondly, automatic extraction approach of semantic feature cross-sections is developed based on the hierarchy. According to the unique distribution of cloud points of each cross-section of each body part, the extraction method of key points on the cross-section is described. Thirdly, we presented an interpolation approach of key points which fit cardinal spline to cross-section for each body part, in which tension parameter is used to represent the simple deformation of body shape. Finally, a connection approach of body part is proposed by sharing a boundary curve. The proposed method has been tested with our virtual human model (VHM) system which is robust and easier to use. The process generally requires about five minutes for generating a full body model that represents the body shape captured by 3D body scanner. The model can be imported in a CAD environment for application to a wide variety of ergonomic analyses.

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.

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 Development of a Wearable-Sensor-Based Fall Detection System

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Falin Wu ◽  
Hengyang Zhao ◽  
Yan Zhao ◽  
Haibo Zhong
Keyword(s):  
Physical Fitness ◽  
Human Body ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Daily Activities ◽  
Public Healthcare ◽  
Wearable Sensor ◽  
Negative Effects ◽  
The Public

Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls. This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregivers with the patient’s location.

scholarly journals A Study of the Control of the Variable Structure-Type Locomotive Robot (4th Report, Postural Change and Locomotion by Jumping of the Control Arm/Leg-Type)

1992 ◽  
Vol 4 (6) ◽  
pp. 472-479
Author(s):  
Tomokazu Hirabayashi ◽  
◽  
Kazuo Yamafuji ◽  
Keyword(s):  
Human Body ◽  
Variable Structure ◽  
Structure Type ◽  
Center Of Gravity ◽  
The Body ◽  
Control Methods ◽  
Postural Change ◽  
Feedback Controls ◽  
Standing Up ◽  
Selection Of

The variable structure type locomotive robot developed in this study can be any of three variations (models) by the selection of controlling arms, wheels, and legs, in addition to the body. This paper reports the postural change and jumping motion of a third model of the robot which is composed of a pair of controlling arms and a leg mounted on each end of the body. Because the model was constructed to simulate the human body, its motions bear quite a resemblance to those of a human. The main results obtained by this study are as follows: (1) Sittingdown and standing-up motions were achieved by using control methods proposed in this study; (2) Locomotion by jumping at the standing-up posture of the robot was attained; and (3) It was verified experimentally that the compound center of gravity feedback controls unstable postures of the robot such as jumping and standing on the tip of the leg.

scholarly journals The Role of Computer Remote Monitoring Technology for Nursing Care in Elderly Breast Cancer Complications

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ling Wang ◽  
Huang Yan ◽  
Jing Yan ◽  
Liyuan Qian
Keyword(s):  
Breast Cancer ◽  
Pattern Recognition ◽  
Human Body ◽  
Medical Information ◽  
Fall Detection ◽  
The Elderly ◽  
Recognition Algorithm ◽  
Gastrointestinal Function ◽  
Medical Monitoring ◽  
Pattern Recognition Algorithm

Geriatric patients undergoing mastectomy have a weakened organism and slow recovery of gastrointestinal function after surgery, which may lead to various complications, affect the absorption of intestinal nutrients, and prolong the healing rate of wounds. Therefore, it is necessary to find an effective nursing program to promote the recovery of gastrointestinal function and prevent postoperative complications in elderly patients undergoing mastectomy. With the continuous development and advancement of computer and communication technologies, telecare is gaining more and more attention and has become an important part of medical information technology construction. Falls endanger the elderly and other special populations, especially after a sudden but unassisted fall, which may be life-threatening. Timely fall detection and rescue can win valuable time for treatment and rescue, which is very important to protect users’ health and improve medical monitoring. In order to provide better medical care to the elderly population and reduce the harm caused by falls, this paper will focus on the fall problem of the elderly in telecare. In order to facilitate the detection of falls of the elderly, we design an Android sensor-based data acquisition scheme, using the built-in acceleration sensor in the Android system to collect the human acceleration information, and through the JMS middleware technology, the collected data are transmitted to MATLAB for analysis and processing in real time. This paper preprocesses and synthesizes the collected human body data and visualizes the acceleration changes of various typical daily activities of the human body and breast cancer, then extracts the relevant data features according to the synthesized SVM curve, constructs a pattern recognition algorithm using the extracted features, and verifies the effectiveness of the pattern recognition algorithm through experiments.

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.

Key Points of Human Body Location Based on Single Depth Map

2012 ◽  
Vol 203 ◽  
pp. 76-82
Author(s):  
Hai Hu ◽  
Bin Li ◽  
Ben Xiong Huang ◽  
Xiao Lei He
Keyword(s):  
Human Body ◽  
Motion Tracking ◽  
Tracking System ◽  
Depth Map ◽  
Human Motion ◽  
The Body ◽  
Body Region ◽  
Automatic Initialization ◽  
Key Points ◽  
Background Segmentation

This paper presents a method of using single depth map to locate the key points of frontal human body. Human motion capture is the premise of motion analysis and understanding, and it has widely application prospects. There are many problems on former way to capture the state of human motion. For example, it can’t initialize automatically, it can not recover from tracking failure, it can not solve the problem caused by occlusion, or there are many constraints on participant, and so on. This article uses Kinect, which from Microsoft, to get depth maps, and use a single map as input to locate the key points of human body. First, depth map can reflect the distance, so background segmentation can be done easily by the characteristic. Then, extract the skeleton of the body’s silhouette. Finally, using the inherent connectivity features of human body, the key points of the body can be determined on the skeleton. Locating the key points from single depth map solve the problem of automatic initialization and recovery directly. The depth map can reflect distance on grayscale, which makes it easy to split the body region from the background. In addition, depth map contains some useful information can be used to solve the problem of occlusion. Using depth map can remove some constraints on the human body, as well as to reduce the influence of clothing and surround lighting, and so on. The experiment shows that this method is very accurate in locating the key points of frontal stand human body, and can solve some problems of occlusion. It is ideal used in a motion tracking system for automatic initialization and self-recovery when tracking failed

scholarly journals Fall prevention system based on airbag protection and mechanical exoskeleton support

2021 ◽  
Vol 336 ◽  
pp. 02015
Author(s):  
Shuaibo Wang ◽  
Jiaxing Sun ◽  
Shuwen Liu
Keyword(s):  
Human Body ◽  
Detection System ◽  
Effective Means ◽  
Fall Detection ◽  
The Elderly ◽  
Accidental Injury ◽  
Hip Joints ◽  
Prevention System ◽  
Fall Protection

The aging of population is a worldwide social problem that all countries will face in the 21st century. The health and quality of life of the elderly will have a significant impact on the country and society. In fact, falls are the leading cause of accidental injury or death in the elderly. Fortunately, using inflatable airbags as a buffer to reduce the injuries caused by falls is currently the most effective means of fall protection. This paper designs an indoor protection device for elderly patients in the rehabilitation stage. It not only includes an accurate and effective fall detection system, but also can use airbags and mechanical exoskeleton to perform head, waist and hip joints on patients who are about to fall. Through experiments, the designed airbag can be ejected within a specified time, and the designed algorithm can accurately distinguish the fall of the human body from the daily behaviour of human body.

Sign in / Sign up

Export Citation Format

Share Document