scholarly journals Perceptual fields reveal previously hidden dynamics of human visual motion sensitivity

2015 ◽  
Vol 114 (3) ◽  
pp. 1360-1363
Author(s):  
Andrew Isaac Meso ◽  
Sandrine Chemla
Keyword(s):  
Spatial Scale ◽  
Motion Detection ◽  
Visual Motion ◽  
Fundamental Property ◽  
Neural Correlates ◽  
Human Motion ◽  
Human Vision ◽  
Reverse Correlation ◽  
Motion Sensitivity ◽  
Human Motion Detection

Motion sensitivity is a fundamental property of human vision. Although its neural correlates are normally only directly accessible with neurophysiological approaches, Neri (Neri P. J Neurosci 34: 8449–8491, 2014) proposed psychophysical reverse correlation to derive perceptual fields, revealing previously unseen dynamics of human motion detection. In this Neuro Forum, these key findings are discussed, putting them into broader context and pointing out possible implications of spatial scale considerations on the interpretation of the findings and dynamic model proposed.

scholarly journals Flexible and wearable strain sensor based on electrospun carbon sponge/polydimethylsiloxane composite for human motion detection

RSC Advances ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 4186-4193
Author(s):  
He Gong ◽  
Chuan Cai ◽  
Hongjun Gu ◽  
Qiushi Jiang ◽  
Daming Zhang ◽  
...  
Keyword(s):  
Motion Detection ◽  
Strain Sensor ◽  
Human Motion ◽  
Gauge Factor ◽  
Human Motion Detection ◽  
Tensile Strains

Electrospun carbon sponge was used to measure tensile strains with a high gauge factor.

scholarly journals Multi-Target Intense Human Motion Analysis and Detection Using Channel State Information

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3379 ◽  
Author(s):  
Jialin Liu ◽  
Lei Wang ◽  
Jian Fang ◽  
Linlin Guo ◽  
Bingxian Lu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Motion Detection ◽  
School Bullying ◽  
Human Motion ◽  
Line Of Sight ◽  
Human Motion Analysis ◽  
Channel State ◽  
Detection Scheme ◽  
State Information ◽  
Human Motion Detection

Intense human motion, such as hitting, kicking, and falling, in some particular scenes indicates the occurrence of abnormal events like violence and school bullying. Camera-based human motion detection is an effective way to analyze human behavior and detect intense human motion. However, even if the camera is properly deployed, it will still generate blind spots. Moreover, camera-based methods cannot be used in places such as restrooms and dressing rooms due to privacy issues. In this paper, we propose a multi-target intense human motion detection scheme using commercial Wi-Fi infrastructures. Compared with human daily activities, intense human motion usually has the characteristics of intensity, rapid change, irregularity, large amplitude, and continuity. We studied the changing pattern of Channel State Information (CSI) influenced by intense human motion, and extracted features in the pattern by conducting a large number of experiments. Considering occlusion exists in some complex scenarios, we distinguished the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions in the case of obstacles appearing between the transmitter and the receiver, which further improves the overall performance. We implemented the intense human motion detection system using single commercial Wi-Fi devices, and evaluated it in real indoor environments. The experimental results show that our system can achieve intense human motion detection rate of 90%.

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