Study on Position Estimation of Human Body and Objects Indoors Using Wireless LAN

2020 ◽  
Vol 2020 (0) ◽  
pp. C-6-1
Author(s):  
Nobuharu MIMURA ◽  
Ryusei OTAKI
Keyword(s):  
Human Body ◽  
Wireless Lan ◽  
Position Estimation

Reducing the Effect of a Human Body for Position Estimation using iBeacon

2020 ◽  
Author(s):  
Takayuki Miyazaki ◽  
Takehiro Makita ◽  
Kenichi Takahashi ◽  
Takao Kawamura ◽  
Kazunori Sugahara
Keyword(s):  
Human Body ◽  
Position Estimation

Modelling the Effect of Human Body around User on Signal Strength and Accuracy of Indoor Positioning

2020 ◽  
Vol 12 (7) ◽  
Author(s):  
Firdaus Firdaus ◽  
◽  
Noor Azurati Ahmad ◽  
Shamsul Sahibuddin ◽  
Rudzidatul Akmam Dziyauddin ◽  
...  
Keyword(s):  
Human Body ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Line Of Sight ◽  
Minimal Cost ◽  
The People ◽  
Indoor Positioning System ◽  
Presence Of People ◽  
Non Line Of Sight

WLAN indoor positioning system (IPS) has high accurate of position estimation and minimal cost. However, environmental conditions such as the people presence effect (PPE) greatly influence WLAN signal and it will decrease the accuracy. This research modelled the effect of people around user on signal strength and the accuracy. We have modelled the human body around user effects by proposed a general equation of decrease in signal strength as function of position, distance, and number of people. Signal strength decreased from 5 dBm to 1 dBm when people in line of sight (LOS) position, and start from 0.5 dBm to 0.3 dBm when people in non-line of sight (NLOS) position. The system accuracy decreases due to the presence of people. When the system is in NLOS case, the presence of people causes a decrease in accuracy from 33% to 57%. Then the accuracy decrease from 273% to 334% in LOS case.

scholarly journals The modified method for position estimation of human body joints in video sequences

2019 ◽  
Vol 24 (6) ◽  
pp. 53-59
Author(s):  
Denys Volodymyrovych Soldatov ◽  
Anton Yuriiovych Varfolomieiev
Keyword(s):  
Human Body ◽  
Position Estimation ◽  
Video Sequences ◽  
Modified Method ◽  
Body Joints

SAR Calculation in Human Body Close to Wireless LAN Router

2019 ◽  
Author(s):  
Kosuke Nishino ◽  
Kazuyuki Saito ◽  
Tomoaki Nagaoka ◽  
Soichi Watanabe ◽  
Masaharu Takahashi
Keyword(s):  
Human Body ◽  
Wireless Lan

scholarly journals Volleyball Action Extraction and Dynamic Recognition Based on Gait Tactile Sensor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Limin Qi
Keyword(s):  
Human Body ◽  
Difference Method ◽  
Body Position ◽  
Capture Rate ◽  
Detection Algorithm ◽  
Human Motion ◽  
Position Estimation ◽  
Test Method ◽  
Frame Difference ◽  
Background Segmentation

At present, the industry research of volleyball technology is relatively in-depth, and the analysis of the muscle strength characteristics and coordination of the jumping ball is less, which is not conducive to the control of technical movements. This study used a wireless portable surface EMG tester (16 lines) to analyze the EMG of the main muscle groups in athletes’ volleyball and conducted a video synchronization test method to find the position of the human body. Therefore, a background-based frame difference method is proposed to detect the position and obtain the precise position of the human body. Experiments show that the background-based three-frame difference method effectively eliminates the “hole” effect of the original three-frame difference method and provides an accurate and complete framework for identifying the human body. Adjust the recognition frame according to the proportion of the human body in the image, and use the predefined parameters of the severe frame to perform forward/volleyball background segmentation. The novelty of this document lies in the completion of the complete human body placement of the above three tasks, precapture/background segmentation, and an improved human body position estimation algorithm to extract the human body pose from the video. First, locate the human body in each frame of the video, and then, perform the process of estimating the position of the graphic model based on the color and texture of the unit. After recognizing the gesture of each image in the video, the recognition result will be displayed. Experiments show that after detecting the position of the human body, the predefined frame setting process of the tomb is carried out in two steps, which improves the automation of the human body image detection algorithm, effectively extracts the human motion video, and increases the motion capture rate by more than 30%, to provide a useful reference for the improvement of college volleyball players’ movement skills and training competitions.

Human body position estimation system using electric field resonance coupling

ICCAS 2010 ◽  
2010 ◽  
Author(s):  
Shimon Ajisaka ◽  
Sousuke Nakamura ◽  
Kiyoaki Takiguchi ◽  
Akira Hirose ◽  
Hideki Hashimoto
Keyword(s):  
Electric Field ◽  
Human Body ◽  
Body Position ◽  
Position Estimation ◽  
Resonance Coupling ◽  
Estimation System
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