Health and Activity Monitoring of Humans and Animals via Contactless Sensing

Background

This research theme creates new smart sensing technologies and IoT systems for health assessment and assistance. The purpose of this research is to connect Data, People and Systems in a variety of areas of value to health, such as networking, pervasive computing, advanced analytics, sensor integration, privacy and security, modeling of socio-behavioral and cognitive processes. Effective solutions must satisfy a multitude of constraints arising from clinical/medical needs, barriers to change, heterogeneity of data, semantic mismatch and limitations of current cyber-physical systems and an aging population. Such solutions demand multidisciplinary teams to address issues ranging from fundamental science and engineering to medical and public health practice.

Research Thrusts

We invented a series of contactless sensing technologies including BedDot, SeatDot, FloorDot and AniDot, for the health and activity monitoring of humans and animals.

SCH: Contactless and Engagement-free Sleep Apnea Monitoring and Characterization

NIH R01 #1R01HL172291-01 (Contact PI: WenZhan Song, PI: Brad Phillips, Yuan Ke, University of Georgia)

Obstructive sleep apnea (OSA) is a major health problem and can lead to or increase the risk of cardiovascular disease, stroke, metabolic disease, daytime sleepiness, workplace errors, traffic accidents and death, if it is left undetected. Worldwide it is estimated one billion people, one in seven adults, have OSA. As sleep occurs primarily in the bedroom, monitoring sleep quality at home, instead of in sleep labs, would significantly advance the self-management, and potentially the clinical management, of OSA and other sleep disorders. Thereafter, an approach that noninvasively monitors sleep quality at home would have significant societal and health benefits. This project brings together leading researchers from informatics and health disciplines to create a contactless sensor system for OSA monitoring and characterization, which integrates advanced Artificial Intelligence (AI) and Data Science (DS) into smart sensors and home care. The key research challenges are to convert the information-rich sensor signals to clinically meaningful vital signs and behavioral patterns that are linked with OSA. The proposed research makes fundamental contributions to computer, data and biomedical science and engineering and will create the first contactless Internet of Things (IoT) system for real-time and engagement-free sleep apnea monitoring and characterization. The main technological innovation is a set of novel stream data AI/DS for sleep events and vitals monitoring: a robust signal quality control and segmentation process based on a moving-sum statistic and recursive binary segmentation; a novel factor auto-regressive recurrent neural network framework to characterize key sleep events; a new approach of monitoring vital signs and their variations based on an innovative panel data model and the structural changes in regression coefficients; and a flexible and distributional robust feature assessment method to enable out-of-distribution (OOD) generalization. The proposed interdisciplinary research takes a coordinated approach that balances theory with evidence-based analysis and systematic advances. The project will conduct empirical validation of new concepts through research prototypes, ranging from specific components to entire systems, and lead to new fundamental insights and effective usability. The research addresses issues ranging from fundamental science and engineering to medical and health practice.

Faculty / Postdoctoral Associates / Students

  • WenZhan Song
  • Yingjian Song
  • Zaid Pitafi
  • Zixuan Zeng
  • Jiayu Chen
  • Yida Zhang
  • Fangyu Li
  • Jose Clemente
  • Maria Valero
  • Zhiwei Luo
  • Sili Wang
  • Bingnan Li

Collaborators

Publications

Yingjian Song, Zaid Farooq Pitafi, Fei Dou, Jin Sun, Xiang Zhang, Bradley G. Phillips, WenZhan Song, “Self-Supervised Representation Learning and Temporal-Spectral Feature Fusion for Bed Occupancy Detection”, ACM UbiComp/IMWUT 2024

Yingjian Song, Bingnan Li, Dan Luo, Glenna S. Brewster Glasgow, Yuan Ke, Brad Phillips and Wenzhan Song, Real-time Continuous Blood Pressure Estimation with Contact-free Bedseismogram, IEEE International Conference on Communication (ICC) 2024

Zaid Farooq Pitafi, Melissa Sleda, Silvia N. J. Moreno, S. Mark Tompkins, Benjamin M. Brainard and Wenzhan Song, CageDot: Contactless Animal Activity Monitoring System to Follow Infectious Disease Progress, IEEE International Conference on Communication (ICC) 2024

Song, Yingjian, Bingnan Li, Dan Luo, Zaipeng Xie, Bradley G. Phillips, Yuan Ke, and Wenzhan Song. “Engagement-Free and Contactless Bed Occupancy and Vital Signs Monitoring.” IEEE Internet of Things Journal (2023).

Fangyu Li; Maria Valero; Jose Clemente; Zion Tse; WenZhan Song; Smart Sleep Monitoring System via Passively Sensing Human Vibration Signals; IEEE sensors journal, 21 (13), 2021.

Maria Valero; Jose Clemente; Fangyu Li; WenZhan Song; Health and sleep nursing assistant for real-time, contactless, and non-invasive monitoring; Pervasive and Mobile Computing, Volume 75 , 2021.

Jose Clemente; Maria Valero; Fangyu Li; Chengliang Wang; WenZhan Song; Helena: Real-time Contact-free Monitoring of Sleep Activities and Events around the Bed, 18th IEEE Conference on Pervasive Computing and Communications (PerCom 2020), 2020. [Mark Weiser Best Paper Award]

Fangyu Li; Jose Clemente; Maria Valero; Zion Tse; Sheng Li; WenZhan Song Smart Home Monitoring System via Footstep Induced Vibrations Journal Article IEEE Systems Journal, 2019.

Jose Clemente; Fangyu Li; Maria Valero; WenZhan Song; Smart Seismic Sensing for Indoor Fall Detection, Location and Notification. IEEE Journal of Biomedical and Health Informatics, 2019

Fangyu Li; WenZhan Song; Changwei Li; Aiying Yang; Non-Harmonic Analysis based Instantaneous Heart Rate Estimation from Photoplethysmography. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2019

Fangyu Li; Jose Clemente; WenZhan Song; Non-intrusive and Non-contact Sleep Monitoring with Seismometer. IEEE GlobalSIP. 2018

Chengliang Wang; Yayun Peng; Debraj De; Wen-Zhan Song. DPHK Real-Time Distributed Predicted Data Collecting based on activity pattern Knowledge mined from trajectories in Smart Environment. Frontiers of Computer Science, 2015.

Chengliang Wang; Qian Zheng; Yayun Peng; Debraj De; Wen-Zhan Song. Distributed Abnormal Activity Detection in Smart Environments. Special issue on “Smart Learning with Sensor Network Technologies”, International Journal of Distributed Sensor Networks, Hindawi Publishing Corporation, 2014.

Chengliang Wang; Debraj De; Wen-Zhan Song. Trajectory Mining from Anonymous Binary Motion Sensors in Smart Environment. Elsevier Journal of Knowledge-Based Systems (KnoSys), 2012.

Debraj De; Wen-Zhan Song; Mingsen Xu; Cheng-Liang Wang; Diane Cook; Xiaoming Huo. FindingHuMo: Real-Time Tracking of Motion Trajectories from Anonymous Binary Sensing in Smart Environments. The 32nd International Conference on Distributed Computing Systems (ICDCS12), 2012.

Debraj De; Shaojie Tang; Wen-Zhan Song; Diane Cook; Sajal K Das. ActiSen: Activity-aware Sensor Network in Smart Environments. Journal of Pervasive and Mobile Computing (PMC), 2012.

Debraj De; Wen-Zhan Song; Shaojie Tang; Diane Cook. EAR: An Energy and Activity Aware Routing Protocol for Wireless Sensor Networks in Smart Environments. The Computer Journal, 2012.

Diane Cook; Wen-Zhan Song. Ambient intelligence and wearable computing: Sensors on the body, in the home, and beyond. Journal of Ambient Intelligence and Smart Environments (JAISE), 1 (2), 2009.

Shao-Jie Tang; Debraj De; Wen-Zhan Song; Diane Cook; Sajal Das. ActSee: Activity-Aware Radio Duty-Cycling for Sensor Networks in Smart Environments. The 8th IEEE International Conference on Networked Sensing Systems (IEEE INSS), 2011.