Ph.D. Research Proposal Exam: Yuqian Hu

Tuesday, April 13, 2021
12:00 p.m.
Zoom meeting: https://umd.zoom.us/j/2902504036
Maria Hoo
301 405 3681
mch@umd.edu

ANNOUNCEMENT: Remote Ph.D. Research Proposal Exam

 

Name: Yuqian Hu

 

Committee:

Professor K. J. Ray Liu (Chair)

Professor Min Wu

Professor Furong Huang


Date/time: Tuesday, April 13, 2021, 12:00pm-14:00pm

 

Location: Zoom meeting: https://umd.zoom.us/j/2902504036

 

Title: Wireless Sensing for Activity Monitoring and Detection

 

 

Abstract:

As people spend more time indoors nowadays, understanding their indoor activities, from body-level activities such as fall to micro motions like keystroke, becomes essential in modern smart homes and health care. However, existing wearable and vision-based activity detection systems suffer from their obtrusiveness and privacy invasion. Inspired by the fact that radio frequency (RF) signals contain the information of the propagation environments, wireless sensing brings the promise of monitoring indoor activities in a non-obtrusive way without invading user privacy. Thanks to the rapid development of internet-of-things (IoT) technologies, wireless sensing has offered plenty of emerging wireless IoT applications, especially by leveraging ambient radios, e.g., WiFi, LTE, and millimeter-wave (mmWave). In the preliminary works, we first implemented a novel WiFi-based environment-independent fall detection system by extracting the speed information inherently associated with human falls. Extensive experiments demonstrate the efficiency and robustness of our fall detector in different time-varying environments. Moreover, by exploring the high directionality of mmWave, we designed a novel signal processing pipeline to recognize keystrokes and developed a universal virtual keyboard system that enables precise concurrent keystrokes recognition on any user-customized layouts. For the proposed research, we will further investigate the problem of using commercial radios to detect the proximity of motion and recognize activities to extend the scope of the existing RF-based activity monitoring and detection.

Audience: Graduate  Faculty 

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