scholarly journals Research on a kind of human motion state monitoring device

2015 ◽  
Author(s):  
Jin Wang ◽  
Yanfei Liu
Keyword(s):  
Human Motion ◽  
Monitoring Device ◽  
State Monitoring ◽  
Motion State

scholarly journals Human Motion State Recognition Based on Flexible, Wearable Capacitive Pressure Sensors

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1219
Author(s):  
Qingyang Yu ◽  
Peng Zhang ◽  
Yucheng Chen
Keyword(s):  
Flexible Substrate ◽  
Recognition Rate ◽  
Pressure Sensors ◽  
Recognition System ◽  
Human Motion ◽  
Body Motion ◽  
Capacitive Pressure Sensor ◽  
Wearable Sensor ◽  
State Recognition ◽  
Motion State

Human motion state recognition technology based on flexible, wearable sensor devices has been widely applied in the fields of human–computer interaction and health monitoring. In this study, a new type of flexible capacitive pressure sensor is designed and applied to the recognition of human motion state. The electrode layers use multi-walled carbon nanotubes (MWCNTs) as conductive materials, and polydimethylsiloxane (PDMS) with microstructures is embedded in the surface as a flexible substrate. A composite film of barium titanate (BaTiO3) with a high dielectric constant and low dielectric loss and PDMS is used as the intermediate dielectric layer. The sensor has the advantages of high sensitivity (2.39 kPa−1), wide pressure range (0–120 kPa), low pressure resolution (6.8 Pa), fast response time (16 ms), fast recovery time (8 ms), lower hysteresis, and stability. The human body motion state recognition system is designed based on a multi-layer back propagation neural network, which can collect, process, and recognize the sensor signals of different motion states (sitting, standing, walking, and running). The results indicate that the overall recognition rate of the system for the human motion state reaches 94%. This proves the feasibility of the human motion state recognition system based on the flexible wearable sensor. Furthermore, the system has high application potential in the field of wearable motion detection.

scholarly journals Video-Based Contactless Heart-Rate Detection and Counting via Joint Blind Source Separation with Adaptive Noise Canceller

2019 ◽  
Vol 9 (20) ◽  
pp. 4349 ◽  
Author(s):  
Kanghyu Lee ◽  
Junmuk Lee ◽  
Changwoo Ha ◽  
Minseok Han ◽  
Hanseok Ko
Keyword(s):  
Heart Rate ◽  
Blind Source Separation ◽  
Source Separation ◽  
Ensemble Empirical Mode Decomposition ◽  
Human Motion ◽  
Indoor Environments ◽  
State Monitoring ◽  
Driver Assistance Systems ◽  
Mode Decomposition ◽  
Adaptive Noise Canceller

Driver assistance systems are a major focus of the automotive industry. Although technological functions that help drivers are improving, the monitoring of driver state functions receives less attention. In this respect, the human heart rate (HR) is one of the most important bio-signals, and it can be detected remotely using consumer-grade cameras. Based on this, a video-based driver state monitoring system using HR signals is proposed in this paper. In a practical automotive environment, monitoring the HR is very challenging due to changes in illumination, vibrations, and human motion. In order to overcome these problems, source separation strategies were employed using joint blind source separation, and feature combination was adopted to maximize HR variation. Noise-assisted data analysis was then adopted using ensemble empirical mode decomposition to extract the pure HR. Finally, power spectral density analysis was conducted in the frequency domain, and a post-processing smoothing filter was applied. The performance of the proposed approach was tested based on commonly employed metrics using the MAHNOB-HCI public dataset and compared with recently proposed competing methods. The experimental results proved that our method is robust for a variety of driving conditions based on testing using a driving dataset and static indoor environments.

scholarly journals Self-Powered, Hybrid, Multifunctional Sensor for a Human Biomechanical Monitoring Device

2021 ◽  
Vol 11 (2) ◽  
pp. 519
Author(s):  
Yeh Hsin Lu ◽  
Hsiao Han Lo ◽  
Jie Wang ◽  
Tien Hsi Lee ◽  
Yiin Kuen Fuh
Keyword(s):  
Short Term Memory ◽  
Short Circuit ◽  
Average Power ◽  
Human Motion ◽  
Open Circuit ◽  
Monitoring Device ◽  
Multiple Sources ◽  
Piezoelectric Energy ◽  
Self Powered ◽  
Multifunctional Sensor

For personal and daily activities, it is highly desirable to collect energy from multiple sources, not only for charging personal electronics but also for charging devices that may in the future sense and transmit information for healthcare and biomedical applications. In particular, hybridization of triboelectric and piezoelectric energy-harvesting generators with lightweight components and relatively simple structures have shown promise in self-powered sensors. Here, we present a self-powered multifunctional sensor (SPMS) based on hybridization with a novel design of a piezoelectrically curved spacer that functions concurrently with a zigzag shaped triboelectric harvester for a human biomechanical monitoring device. The optimized SPMS had an open-circuit voltage (VOC) of 103 V, short-circuit current (ISC) of 302 µA, load of 100 kΩ, and maximum average power output of 38 mW under the operational processes of compression/deformation/touch/release. To maximize the new sensor’s usage as a gait sensor that can detect and monitor human motion characteristics in rehabilitation circumstances, the deep learning long short-term memory (LSTM) model was developed with an accuracy of the personal sequence gait SPMS signal recognition of 81.8%.

Analysis on the Selection of Low Temperature Resistant Materials for Electric Equipment State Monitoring Device in Cold Areas

2020 ◽  
Vol 842 ◽  
pp. 251-256
Author(s):  
Hong Da Zhang ◽  
Yue Wang ◽  
Li Wei Liu ◽  
Hai Tao Feng ◽  
Lin Li ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Low Temperature ◽  
High Rate ◽  
Insulation Material ◽  
Monitoring Device ◽  
State Monitoring ◽  
Steady State Method ◽  
Influence Of Temperature On ◽  
Electric Equipment ◽  
Equipment State

As the important device to perceive the operating state of grid, the condition monitoring device of electric equipment solves such problems as the high rate of false alarm and failure of device caused by the low temperature in winter of cold areas. This paper measures the thermal conductivity of such three kinds of materials as asbestos, rubber and nano porous aerogels in the temperature range of -55°C to +80°C by steady state method, and analyzes the influence of temperature on the thermal conductivity of three materials. According to the result of experiment, the thermal conductivity of nano porous aerogels is the lowest and is affected least by temperature among three kinds of materials. Nano porous aerogels apply to state monitoring device operated in cold areas; and rubber can also be used as thermal insulation material when the operating temperature is generally higher than -20°C.

scholarly journals Capture of 3D Human Motion Pose in Virtual Reality Based on Video Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Qiang Fu ◽  
Xingui Zhang ◽  
Jinxiu Xu ◽  
Haimin Zhang
Keyword(s):  
Virtual Reality ◽  
Fuzzy Logic ◽  
Human Body ◽  
Estimation Algorithm ◽  
Human Motion ◽  
Body Motion ◽  
Classification Model ◽  
Accuracy Rate ◽  
Motion State ◽  
Adaptive Adjustment

Motion pose capture technology can effectively solve the problem of difficulty in defining character motion in the process of 3D animation production and greatly reduce the workload of character motion control, thereby improving the efficiency of animation development and the fidelity of character motion. Motion gesture capture technology is widely used in virtual reality systems, virtual training grounds, and real-time tracking of the motion trajectories of general objects. This paper proposes an attitude estimation algorithm adapted to be embedded. The previous centralized Kalman filter is divided into two-step Kalman filtering. According to the different characteristics of the sensors, they are processed separately to isolate the cross-influence between sensors. An adaptive adjustment method based on fuzzy logic is proposed. The acceleration, angular velocity, and geomagnetic field strength of the environment are used as the input of fuzzy logic to judge the motion state of the carrier and then adjust the covariance matrix of the filter. The adaptive adjustment of the sensor is converted to the recognition of the motion state. For the study of human motion posture capture, this paper designs a verification experiment based on the existing robotic arm in the laboratory. The experiment shows that the studied motion posture capture method has better performance. The human body motion gesture is designed for capturing experiments, and the capture results show that the obtained pose angle information can better restore the human body motion. A visual model of human motion posture capture was established, and after comparing and analyzing with the real situation, it was found that the simulation approach reproduced the motion process of human motion well. For the research of human motion recognition, this paper designs a two-classification model and human daily behaviors for experiments. Experiments show that the accuracy of the two-category human motion gesture capture and recognition has achieved good results. The experimental effect of SVC on the recognition of two classifications is excellent. In the case of using all optimization algorithms, the accuracy rate is higher than 90%, and the final recognition accuracy rate is also higher than 90%. In terms of recognition time, the time required for human motion gesture capture and recognition is less than 2 s.

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