scholarly journals Low-Cost Multisensor Integrated System for Online Walking Gait Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lingyun Yan ◽  
Guowu Wei ◽  
Zheqi Hu ◽  
Haohua Xiu ◽  
Yuyang Wei ◽  
...  
Keyword(s):  
Detection System ◽  
Low Cost ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Integrated System ◽  
Heel Strike ◽  
Detection Accuracy ◽  
Time Error ◽  
Kinematic Data ◽  
Compact Size

A three-dimensional motion capture system is a useful tool for analysing gait patterns during walking or exercising, and it is frequently applied in biomechanical studies. However, most of them are expensive. This study designs a low-cost gait detection system with high accuracy and reliability that is an alternative method/equipment in the gait detection field to the most widely used commercial system, the virtual user concept (Vicon) system. The proposed system integrates mass-produced low-cost sensors/chips in a compact size to collect kinematic data. Furthermore, an x86 mini personal computer (PC) running at 100 Hz classifies motion data in real-time. To guarantee gait detection accuracy, the embedded gait detection algorithm adopts a multilayer perceptron (MLP) model and a rule-based calibration filter to classify kinematic data into five distinct gait events: heel-strike, foot-flat, heel-off, toe-off, and initial-swing. To evaluate performance, volunteers are requested to walk on the treadmill at a regular walking speed of 4.2 km/h while kinematic data are recorded by a low-cost system and a Vicon system simultaneously. The gait detection accuracy and relative time error are estimated by comparing the classified gait events in the study with the Vicon system as a reference. The results show that the proposed system obtains a high accuracy of 99.66% with a smaller time error (32 ms), demonstrating that it performs similarly to the Vicon system in the gait detection field.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

scholarly journals AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 771-777 ◽  
Author(s):  
Guoqing Zhou ◽  
Xiang Zhou ◽  
Tao Yue ◽  
Yilong Liu
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Threshold Method ◽  
Dsp Implementation ◽  
Svm Algorithm

This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.

scholarly journals Reputation Management Using Honeypots for Intrusion Detection in the Internet of Things

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 415 ◽  
Author(s):  
Zeeshan Ali Khan ◽  
Ubaid Abbasi
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Detection System ◽  
Mac Protocol ◽  
Low Cost ◽  
Reputation Management ◽  
The Internet ◽  
Detection Accuracy ◽  
Extra Energy ◽  
Networking Protocols

Internet of Things (IoT) networks consist of tiny devices with limited processing resources and restricted energy budget. These devices are connected to the world-wide web (www) using networking protocols. Considering their resource limitations, they are vulnerable to security attacks by numerous entities on the Internet. The classical security solutions cannot be directly implemented on top of these devices for this reason. However, an Intrusion Detection System (IDS) is a classical way to protect these devices by using low-cost solutions. IDS monitors the network by introducing various metrics, and potential intruders are identified, which are quarantined by the firewall. One such metric is reputation management, which monitors the behavior of the IoT networks. However, this technique may still result in detection error that can be optimized by combining this solution with honeypots. Therefore, our aim is to add some honeypots in the network by distributing them homogeneously as well as randomly. These honeypots will team up with possible maliciously behaving nodes and will monitor their behavior. As per the simulation results, this technique reduces the error rate within the existing IDS for the IoT; however, it costs some extra energy. This trade-off between energy consumption and detection accuracy is studied by considering standard routing and MAC protocol for the IoT network.

scholarly journals Sliding wire detection system of electric lock screw tool based on motor characteristics

2021 ◽  
Vol 2137 (1) ◽  
pp. 012009
Author(s):  
Ning Zhang ◽  
Yinxin Yan ◽  
Houcheng Yang ◽  
Zhangsi Yu
Keyword(s):  
Real Time ◽  
Detection System ◽  
Low Cost ◽  
High Sensitivity ◽  
High Accuracy ◽  
Normal Operation ◽  
Automatic Assembly ◽  
The Real ◽  
Current Change ◽  
Real Time Detection

Abstract This paper presents a sliding wire detection system of electric screw locking tool based on the characteristics of motor. The system can judge whether the screw has sliding wire through the current change of motor during normal operation, and realize the real-time detection and alarm of sliding wire. The system has the advantages of high sensitivity, low cost and high accuracy. It can be widely used in automatic assembly and other fields.

An Off-Resonance Synchronous Vibration Based Method for Rotor System Damage Detections

2005 ◽  
Author(s):  
Huageng Luo ◽  
Hector Rodriguez ◽  
Darren Hallman ◽  
Dennis Corbly
Keyword(s):  
Final Section ◽  
State Of The Art ◽  
Detection System ◽  
Detection Algorithm ◽  
Integrated System ◽  
Test Results ◽  
Test Rig ◽  
Test Setup ◽  
First Order ◽  
Vibration Detection

This paper presents a methodology of detecting rotor imbalances, such as mass imbalance and crack-induced imbalance, using shaft synchronous vibrations. A vibration detection algorithm is derived based on the first order nonresonant synchronous vibration response. A detection system is integrated by using state-of-the-art commercial analysis equipment. A laboratory rotor test rig with controlled mass imbalances was used to verify the integrated system. The system is then deployed to an engine sub-assembly test setup. Four specimens were used in the subassembly test and the test results are reported in the final section.

Diamond turning fabrication of an ultra-compact endoscope

2016 ◽  
Vol 5 (4) ◽  
Author(s):  
Chih-Yu Huang ◽  
Rongguang Liang
Keyword(s):  
Optical System ◽  
Low Cost ◽  
High Accuracy ◽  
Diamond Turning ◽  
Fabrication Process ◽  
Stray Light ◽  
Assembly Process ◽  
Interference Fringes ◽  
Compact Size ◽  
Endoscope System

AbstractIn this paper, we propose a technique by integrating mechanical mounts into lens elements to fulfill a self-aligned and self-assembled optical system. To prove this concept, we designed, fabricated, and tested an ultra-compact endoscope that adopts this technique. By taking advantages of the specially designed fixture and observing the interference fringes between the lens and fixture, we developed a method to minimize decenter and tilt between the two surfaces of the endoscope lens during the diamond turning fabrication process. The integrated mechanical mounts provide an easy assembly process for the endoscope system while maintaining high accuracy in system alignment. With the application of heat shrink tube as the endoscope system holder and to block stray light, the proposed endoscope system has the advantages of low cost, compact size, and high imaging quality.

Research on High-Density Network Intrusion Features Detection Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 1054-1057
Author(s):  
Tai Fu Lv
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Systems ◽  
Feature Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
High Density ◽  
Detection Accuracy ◽  
Network Intrusion ◽  
The Neural Network

Research on high-density network intrusion features problems, which improves the detection accuracy. For high-density network, an intrusion feature detection system based on intelligent expert systems and neural networks in is proposed. First, use expert systems for known high-density network intrusion detection. Use the neural network expert system to detect those which cannot find the unknown high-density network intrusion. Finally test results using neural network expert system rule library to be updated. Experimental results show that this method can effectively detect high-density network intrusion features, which ensures the security of the network and achieves satisfactory results.

scholarly journals Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4335
Author(s):  
Goran Šeketa ◽  
Lovro Pavlaković ◽  
Dominik Džaja ◽  
Igor Lacković ◽  
Ratko Magjarević
Keyword(s):  
Large Scale ◽  
Detection System ◽  
Low Cost ◽  
Fall Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Data Segmentation ◽  
Post Impact

Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.

scholarly journals An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1777
Author(s):  
Ana Serrano-Mamolar ◽  
Miguel Arevalillo-Herráez ◽  
Guillermo Chicote-Huete ◽  
Jesus G. G. Boticario
Keyword(s):  
Real World ◽  
Markov Models ◽  
Low Cost ◽  
High Accuracy ◽  
Physiological Signals ◽  
Detection Accuracy ◽  
Related Information ◽  
Additional Control ◽  
Affect Detection ◽  
Potential Use

Previous research has proven the strong influence of emotions on student engagement and motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but there is no standard method for predicting students’ affects. However, physiological signals have been widely used in educational contexts. Some physiological signals have shown a high accuracy in detecting emotions because they reflect spontaneous affect-related information, which is fresh and does not require additional control or interpretation. Most proposed works use measuring equipment for which applicability in real-world scenarios is limited because of its high cost and intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using both inter-subject and intra-subject models, we present an experimental study that aims to explore the potential application of Hidden Markov Models (HMM) to predict the concentration state from 4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin temperature. We also study the effect of combining these four signals and analyse their potential use in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high accuracy can be achieved with three of the signals when using HMM-based intra-subject models. However, inter-subject models, which are meant to obtain subject-independent approaches for affect detection, fail at the same task.

scholarly journals A Rapid Gradation Detection System for Earth and Stone Materials Based on Digital Image

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shu Yu ◽  
Yanfeng Wen ◽  
Zuyu Chen ◽  
Guoying Zhang ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Digital Image ◽  
Detection System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Dam Construction ◽  
Construction Site ◽  
Construction Sites ◽  
Edge Detection Algorithm ◽  
Stone Materials ◽  
Transport Vehicle

Gradation has an important influence on the mechanical properties of earth and stone materials after compaction, and a reasonable gradation is the key to ensure compaction quality. In this paper, a set of earth and stone materials gradation detection system based on digital image processing was proposed, which was suitable for detection in the construction sites. The system obtained images which were taken from multiple directions of materials transport vehicle, used the thresholding method to segment the images, detected particle contours through edge detection algorithm of Canny to realize the image recognition of particle size, and drew the gradation curve of earth and rock materials finally. It was verified by selecting limestone aggregates of different gradations, and the results showed high accuracy. The system can realize on-site detection of the earthwork gradation rapidly and accurately at a dam construction site.

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