scholarly journals A Computer Vision-Based Dual Network Approach for Indoor Fall Detection

2020 ◽  
Vol 5 (8) ◽  
pp. 939-943 ◽  
Author(s):  
Lu Zhang ◽  
Chun Fang ◽  
Ming Zhu
Keyword(s):  
Fall Detection ◽  
The Elderly ◽  
Video Stream ◽  
Detection Algorithm ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set ◽  
Family Activities ◽  
Safety Risks ◽  
Indoor Scenarios

In order to strengthen the monitoring of the elderly and reduce the safety risks caused by falls, a video-based indoor fall detection algorithm using a dual network structure is proposed. Firstly, for the recorded video stream, we apply the fine-tuned YOLACT network to extract the contours of the human body, and then design a simple convolutional neural network to distinguish the categories of different family activities (including bending, standing, sitting and lying), and finally make a fall decision. When a lying position is detected on the floor region, it is considered as a fall. Experiments show that the proposed algorithm can successfully detect fall events in different indoor scenarios, and have a low false detection rate on the constructed data set.

scholarly journals An Improved K -Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bin Xie ◽  
Xinyu Dong ◽  
Changguang Wang
Keyword(s):  
Wireless Networks ◽  
Intrusion Detection ◽  
Wireless Network ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Network Data ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set ◽  
Network Intrusion

The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved k -means clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each participant. Furthermore, the cosine distance of multiple perspectives is introduced in the algorithm to measure the similarity between network data objects in the improved k -means clustering process, making the clustering results more reasonable and the judgment of network data behavior more accurate. The AWID, an open wireless network attack data set, is selected as the experimental data set. Its dimensionality reduces by the method of principal component analysis (PCA). Experimental results show that the improved k -means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.

Faster multi-defect detection system in shield tunnel using combination of FCN and faster RCNN

2019 ◽  
Vol 22 (13) ◽  
pp. 2907-2921 ◽  
Author(s):  
Xinwen Gao ◽  
Ming Jian ◽  
Min Hu ◽  
Mohan Tanniru ◽  
Shuaiqing Li
Keyword(s):  
Boundary Layer ◽  
Defect Detection ◽  
Detection Rate ◽  
Layer Structure ◽  
Detection System ◽  
Identification Accuracy ◽  
Shield Tunnel ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set

With the large-scale construction of urban subways, the detection of tunnel defects becomes particularly important. Due to the complexity of tunnel environment, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. This article presents a deep learning FCN-RCNN model that can detect multiple tunnel defects quickly and accurately. The algorithm uses a Faster RCNN algorithm, Adaptive Border ROI boundary layer and a three-layer structure of the FCN algorithm. The Adaptive Border ROI boundary layer is used to reduce data set redundancy and difficulties in identifying interference during data set creation. The algorithm is compared with single FCN algorithm with no Adaptive Border ROI for different defect types. The results show that our defect detection algorithm not only addresses interference due to segment patching, pipeline smears and obstruction but also the false detection rate decreases from 0.371, 0.285, 0.307 to 0.0502, respectively. Finally, corrected by cylindrical projection model, the false detection rate is further reduced from 0.0502 to 0.0190 and the identification accuracy of water leakage defects is improved.

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.

Three-States-Transition Method for Fall Detection Algorithm Using Depth Image

2019 ◽  
Vol 31 (1) ◽  
pp. 88-94 ◽  
Author(s):  
Xiangbo Kong ◽  
◽  
Zelin Meng ◽  
Lin Meng ◽  
Hiroyuki Tomiyama
Keyword(s):  
Tangent Vector ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Binary Image ◽  
Depth Image ◽  
Living Room ◽  
Elderly Persons ◽  
Data Set ◽  
Detection Systems ◽  
Transition Speed

Currently, the proportion of elderly persons is increasing all over the world, and accidents involving falls have become a serious problem especially for those who live alone. In this paper, an enhancement to our algorithm to detect such falls in an elderly person’s living room is proposed. Our previous algorithm obtains a binary image by using a depth camera and obtains an outline of the binary image by Canny edge detection. This algorithm then calculates the tangent vector angles of each outline pixels and divide them into 15° range groups. If most of the tangent angles are below 45°, a fall is detected. Traditional fall detection systems cannot detect falls towards the camera so at least two cameras are necessary in related works. To detect falls towards the camera, this study proposes the addition of a three-states-transition method to distinguish a fall state from a sitting-down one. The proposed algorithm computes the different position states and divides these states into three groups to detect the person’s current state. Futhermore, transition speed is calculated in order to differentiate sit states from fall states. This study constructes a data set that includes over 1500 images, and the experimental evaluation of the images demonstrates that our enhanced algorithm is effective for detecting the falls with only a single camera.

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.

scholarly journals Fall detection system based on BiLSTM neural network

2019 ◽  
Vol 7 (5) ◽  
pp. 01-12
Author(s):  
Biao YE ◽  
Lasheng Yu
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Detection System ◽  
Input Sequence ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Training Data ◽  
Sensor Data ◽  
Data Set ◽  
Sequence Modeling

The purpose of this article is to analyze the characteristics of human fall behavior to design a fall detection system. The existing fall detection algorithms have problems such as poor adaptability, single function and difficulty in processing large data and strong randomness. Therefore, a long-term and short-term memory recurrent neural network is used to improve the effect of falling behavior detection by exploring the internal correlation between sensor data. Firstly, the serialization representation method of sensor data, training data and detection input data is designed. The BiLSTM network has the characteristics of strong ability to sequence modeling and it is used to reduce the dimension of the data required by the fall detection model. then, the BiLSTM training algorithm for fall detection and the BiLSTM-based fall detection algorithm convert the fall detection into the classification problem of the input sequence; finally, the BiLSTM-based fall detection system was implemented on the TensorFlow platform. The detection and analysis of system were carried out using a bionic experiment data set which mimics a fall. The experimental results verify that the system can effectively improve the accuracy of fall detection to 90.47%. At the same time, it can effectively detect the behavior of Near-falling, and help to take corresponding protective measures.

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
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