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SUMMARY:Towards Smart Health Using Mobile Technologies - Yingying Chen\, R
 utgers University
DTSTART:20231107T160000Z
DTEND:20231107T170000Z
UID:TALK203734@talks.cam.ac.uk
CONTACT:Cecilia Mascolo
DESCRIPTION:Smart health is crucial in promoting individual well-being and
  long-term health outcomes. With the emerging sensing and computing power 
 in pervasive mobile devices\, smart health technologies can empower indivi
 duals to monitor their health metrics\, track their physical activity\, an
 d make informed decisions about their lifestyle\, forming a powerful syner
 gy that fosters healthier lifestyles and prevents chronic illnesses in the
  future. We find that commercial WiFi signals can be exploited as a sensin
 g modality in addition to its original communication usage\, providing a c
 ontactless and low-cost solution for smart health in non-clinical environm
 ents. This talk will first introduce a personalized fitness assistant syst
 em that utilizes WiFi signals for effective exercise monitoring and assess
 ment at a relatively coarse-grained level. The system leverages Channel St
 ate Information (CSI) measurements from existing WiFi infrastructure to pr
 ovide workout statistics and dynamic evaluations. A Deep Neural Network (D
 NN) model is employed for workout recognition and individual identificatio
 n tasks. The study investigates the impact of factors such as the sensitiv
 e region between WiFi transceivers and ambient interference on system perf
 ormance. I will then take a deeper look of estimating fine-grained vital s
 igns (e.g.\, breathing rate and heartbeats) during sleep using minute WiFi
  signal changes. Our approach demonstrates the feasibility of contactless\
 , continuous\, and fine-grained monitoring of vital signs without any addi
 tional cost. In addition\, the system can distinguish different sleep even
 ts and track sleep postures to provide insights into sleep quality. We sho
 w that vital signs can be captured using only one AP and a single WiFi dev
 ice\, which can be extended to non-sleep scenarios. Extensive experiments 
 in laboratory and nonclinical settings show comparable or better performan
 ce compared to existing sensor-based approaches. These smart health system
 s offer convenience and potential for various smart health application sce
 narios\, benefiting users in maintaining their healthy daily routines.\n\n
 ============\n\nBiography:\n\nYingying (Jennifer) Chen is a Professor and 
 Department Chair of Electrical and Computer Engineering (ECE) and Peter Ch
 erasia Endowed Faculty Scholar at Rutgers University. She is the Associate
  Director of Wireless Information Network Laboratory (WINLAB). She also le
 ads the Data Analysis and Information Security (DAISY) Lab. She is a Fello
 w of IEEE and a Fellow of National Academy of Inventors (NAI). She is also
  an ACM Distinguished Member. Her research interests include Applied Machi
 ne Learning in Mobile Computing and Sensing\, Internet of Things (IoT)\, S
 ecurity in AI/ML Systems\, Smart Healthcare\, and Deep Learning on Mobile 
 Systems. She is a pioneer in RF/WiFi sensing\, location systems\, and mobi
 le security. Before joining Rutgers\, she was a tenured professor at Steve
 ns Institute of Technology and had extensive industry experiences at Nokia
  (previously Lucent Technologies). She has published 3 books\, 4 book chap
 ters and 240+ journal articles and refereed conference papers. She is the 
 recipient of seven Best Paper Awards in top ACM and IEEE conferences. She 
 is the recipient of NSF CAREER Award and Google Faculty Research Award. Sh
 e received NJ Inventors Hall of Fame Innovator Award and is also the recip
 ient of IEEE Region 1 Technological Innovation in Academic Award. Her rese
 arch has been supported by many funding agencies including NSF\, NIH\, ARO
 \, DoD and AFRL and reported in numerous media outlets\n\nincluding MIT Te
 chnology Review\, CNN\, Fox News Channel\, Wall Street Journal\, National 
 Public Radio and IEEE Spectrum. She has been serving/served on the editori
 al boards of IEEE Transactions on Mobile Computing (TMC)\, IEEE Transactio
 ns on Wireless Communications (TWireless)\, IEEE/ACM Transactions on Netwo
 rking (ToN) and ACM Transactions on Privacy and Security (TOPS). For more 
 information\, please refer to her homepage at: http://www.winlab.rutgers.e
 du/~yychen/.
LOCATION:Online (zoom link on the page)
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