Fabrication of a wrinkled structure made of wearable polyacrylonitrile/polyurethane composite fibers with elastic sensing properties suitable for human movement detection

2020 ◽  
Vol 41 (9) ◽  
pp. 3491-3500
Author(s):  
Wuji Lu ◽  
Wenxing Chen ◽  
Wangyang Lu
Keyword(s):  
Human Movement ◽  
Movement Detection ◽  
Composite Fibers ◽  
Sensing Properties ◽  
Polyurethane Composite ◽  
Wrinkled Structure

High conductive free-written thermoplastic polyurethane composite fibers utilized as weight-strain sensors

2020 ◽  
Vol 189 ◽  
pp. 108011 ◽  
Author(s):  
Shijie Zhang ◽  
Zuoli He ◽  
Gengheng Zhou ◽  
Byung-Mun Jung ◽  
Tae-Hoon Kim ◽  
...  
Keyword(s):  
Thermoplastic Polyurethane ◽  
Strain Sensors ◽  
Composite Fibers ◽  
Polyurethane Composite

scholarly journals Compositional Effects of Large Graphene Oxide Sheets on the Spinnability and Properties of Polyurethane Composite Fibers

2016 ◽  
Vol 3 (5) ◽  
pp. 1500672 ◽  
Author(s):  
Shayan Seyedin ◽  
Joselito M. Razal ◽  
Peter C. Innis ◽  
Rouhollah Jalili ◽  
Gordon G. Wallace
Keyword(s):  
Graphene Oxide ◽  
Composite Fibers ◽  
Polyurethane Composite ◽  
Compositional Effects ◽  
Graphene Oxide Sheets

Electrospun V2O5 composite fibers: Synthesis, characterization and ammonia sensing properties

2013 ◽  
Vol 548 ◽  
pp. 689-694 ◽  
Author(s):  
V. Modafferi ◽  
S. Trocino ◽  
A. Donato ◽  
G. Panzera ◽  
G. Neri
Keyword(s):  
Composite Fibers ◽  
Sensing Properties ◽  
Ammonia Sensing

Carbon nanotube-reinforced polyurethane composite fibers

2006 ◽  
Vol 66 (15) ◽  
pp. 3029-3034 ◽  
Author(s):  
Wei Chen ◽  
Xiaoming Tao ◽  
Yuyang Liu
Keyword(s):  
Carbon Nanotube ◽  
Composite Fibers ◽  
Polyurethane Composite

scholarly journals Real Life Human Movement Realization in Multimodal Group Communication Using Depth Map Information and Machine Learning

2020 ◽  
Vol 2 (2) ◽  
pp. 93-101
Author(s):  
Dr. Ranganathan G.
Keyword(s):  
Data Gathering ◽  
Real Life ◽  
Depth Map ◽  
Human Movement ◽  
Recognition System ◽  
Research Field ◽  
Movement Detection ◽  
The Real ◽  
Human Movements ◽  
Almost All

The latest advancements in the evolution of depth map information’s has paved way for interesting works like object recognition sign detection and human movement detection etc. The real life human movement detection or their activity identification is very challenging and tiresome. Since the real life activities of the humans could be of much interest in almost all areas, the subject of identifying the human activities has gained significance and has become a most popular research field. Identifying the human movements /activities in the public places like airport, railways stations, hospital, home for aged become very essential due to the several benefits incurred form the human movement recognition system such as surveillance camera, monitoring devices etc. since the changes in the space and the time parameters can provide an effective way of presenting the movements, yet in the case of natural color vision, as the flatness is depicted in almost all portions of images. So the work laid out in the paper in order to identify the human movement in the real life employs the space and the time depth particulars (Spatial-Temporal depth details –STDD) and the random forest in the final stage for movement classification. The technology put forth utilize the Kinect sensors to collecting the information’s in the data gathering stage. The mechanism laid out to identify the human movements is test with the MATLAB using the Berkley and the Cornell datasets. The mechanism proposed through the acquired results proves to deliver a better performance compared to the human movements captured using the normal video frames.

Processable Thermally Conductive Polyurethane Composite Fibers

2018 ◽  
Vol 304 (3) ◽  
pp. 1800542 ◽  
Author(s):  
Syamak Farajikhah ◽  
Rebecca Amber ◽  
Sepidar Sayyar ◽  
Sajjad Shafei ◽  
Cormac D. Fay ◽  
...  
Keyword(s):  
Composite Fibers ◽  
Polyurethane Composite ◽  
Thermally Conductive

scholarly journals Human Movement Detection using Recurrent Convolutional Neural Networks

2019 ◽  
Vol 8 (12S) ◽  
pp. 348-351
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Human Movement ◽  
Sensor Data ◽  
Light Weight ◽  
Movement Detection ◽  
Relative Coordinates ◽  
Video Input ◽  
Background Image ◽  
Over Time

Human Movement detection is vital in Tele-presence Robots, Animations, Games and Robotic movements. By using Traditional methods with the help of sensor suits it is difficult to find and interpret the movements. As it includes so much sensor data which is difficult to interpret, find the action and send to long distances. It is also very expensive and bulky too. Image processing and computer vision provides a solution to detect and interpret Human movement based on R-CNN approach. It is cheap, easy and light weight algorithm. It takes the video input and divides it in to frames, then it is Human body is separated for the background image. This paper mainly focused on skeleton, its major points and its relative positions in successive picture frames. A set of frames (Video) is given as input to the model, so that the model compares the coordinates of the successive frames and estimates the movement. First, the human is identified and separated from the rest of the image by drawing a bounding box around the human by using CNN (Convolution neural networks), then by applying R-CNN human is segmented and converted to skeleton. From the shape of the skeleton we can identify whether the skeleton is that of a human or not. Comparing the relative coordinates of skeletons extracted from frames photographed over time gives the movement of the human and its direction.

scholarly journals Super resolution passive radars based on 802.11ax Wi‐Fi signals for human movement detection

2021 ◽  
Author(s):  
Hasan Can Yildirim ◽  
Jean‐François Determe ◽  
Laurent Storrer ◽  
François Rottenberg ◽  
Philippe De Doncker ◽  
...  
Keyword(s):  
Super Resolution ◽  
Human Movement ◽  
Movement Detection

Fabrication of Conductive Polypyrrole/Polyurethane Composite Fibers for Large Strain Sensing

2012 ◽  
Vol 482-484 ◽  
pp. 1142-1145 ◽  
Author(s):  
Xiao Lin Zhang ◽  
Zong Yi Qin ◽  
Long Chen
Keyword(s):  
Large Strain ◽  
Fiber Surface ◽  
Smart Device ◽  
Strain Sensing ◽  
Composite Fibers ◽  
Resistance Response ◽  
Polyurethane Composite ◽  
Sensing Material ◽  
Device Applications ◽  
Conductive Polypyrrole

A kind of flexible, conductive polypyrrole–coated polyurethane (PPy/PU) fibers was fabricated by controlled chemical polymerization and its strain sensing ability was evaluated. The as-prepared fibers possessed high conductivity with a maximum value of 10-1 (Ω•cm)-1, and highly elastic nature of the PU matrix. It is further found that dense PPy layer was covered uniformly onto PU fiber surface, and an interpenetrating interface and strong hydrogen bonding interaction could be observed, which greatly benefited their high structural stability. More importantly, the composite fibers exhibited a wide strain deformation range up to 250% and high strain sensitivity of over 20 (at the large strain of 50%), and good reversible resistance response on cyclic force loading, which would open a high opportunity for fabricating strain sensing material in large volume for future smart device applications.

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