Deep Learning-Assisted 3D Pressure Sensors for Control of Unmanned Aerial Vehicles

Accurately and reliably detecting and recognizing human body movements in real time, relaying appropriate commands to the machine, have substantial implications for virtual reality, remote control, and robotics applications. Nonetheless, most contemporary wearable analysis and control systems attain...

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Bibliographic Details
Main Author:	Junlai Jiang (18389117) (author)
Other Authors:	Hao Gu (837274) (author), Ruixiang Xu (9521792) (author), Jingwei Zhou (1374144) (author), Yi Gao (112914) (author), Limei Zhang (1578604) (author), Xinyue Cong (17763547) (author), Yi Jiang (40197) (author), Lijun Song (1899229) (author)
Published:	2025
Subjects:	Neuroscience Physiology Mental Health Space Science Information Systems not elsewhere classified wireless signal transmission unmanned aerial vehicle setting sensor thresholds relaying appropriate commands rapid response times poorer user experience offers innovative concepts intuitive user interface convolutional neural network contemporary wearable analysis recognition system introduces controlled flight process 30 ms ). novel control mechanism deep learning techniques deep learning recognition accuracy flight altitude 40 ms virtual reality video surveillance time display substantial implications routine usage robotic technology remote control reliably detecting recognized via practical pathways detected gestures complete gesture altitude control 5 %. 2 kpa
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Deep Learning-Assisted 3D Pressure Sensors for Control of Unmanned Aerial Vehicles

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