The Laboratory for Mobile Sensing and Ubiquitous Computing at UMass conducts research on a variety of sensor systems, networking and data analytics for health and behavior modeling. Our focus is on building scalable, energy-efficient sensor systems through the use of heterogeneous sensor modalities and platforms for solving real-world problems.
Prof. Rahman received a grant from Intel Pandemic Response Technology Initiative to work on COVID-19 resurgence modeling with contactless syndromic signal sensing from crowds at public spaces in Western MA.
Prof. Rahman received NSF Smart and Connected Society planning grant on “Extreme temperature and it’s impact on human physiology, psychology and our community-level behaviors” with colleagues at UMASS, ASU and NAIST (Japan). A UMass press release on the project.
Prof. Rahman spoke to the Public Television for Western New England (WGBY) about our FluSense project. Link .
FluSense received an almost world-wide press coverage. Techcrunch , UMass , Daily Mail (UK), Hindustan Times (India), Science Node, Digital Trends, Masslive, Photonics, ibitimes (Singapore). Full List
Md Farhan Tasnim Oshim, Julian Killingback, Dave Follette, Huaishu Peng, Tauhidur Rahman. 2020. MechanoBeat: Monitoring Interactions with Everyday Objects using 3D Printed Harmonic Oscillators and Ultra-Wideband Radar. UIST '20: The 33rd Annual ACM Symposium on User Interface Software and Technology Proceedings. https://doi.org/10.1145/3379337.3415902
Mohammad Rostami, K. Sundaresan, E. Chai, S. Rangarajan, Deepak Ganesan. 2020. Redefining Passive in Backscattering with Commodity Devices. MobiCom '20: Proceedings of the 26th Annual International Conference on Mobile Computing and NetworkingApril 2020 Article 3, 13 Pages. https://doi.org/10.1145/3372224.3380880
Forsad Al Hossain, Andrew A. Lover, George A. Corey, Nicholas G. Reich, Tauhidur Rahman. 2020. FluSense: A Contactless Syndromic Surveillance Platform for Influenza-Like Illness in Hospital Waiting Areas. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4, 1, Article 1 (March 2020), 27 pages. https://doi.org/10.1145/3381014
This project develops a closed-loop embodied Intelligent Cognitive Assistant (e-ICA) to infer circadian rhythm, alertness, and stress levels and to provide personalized feedback to enhance users' cognitive ability and wellbeing in an unobtrusive and effortless manner. For details: https://www.umass.edu/newsoffice/article/umass-amherst-computer-scientists-1
