- The M-Lab is now MOSAIC Lab!As of August 2021, Prof. Christian Poellabauer has joined the faculty of the Knight Foundation School of Computing and Information Sciences and the Mobile Computing Lab (M-Lab) at the University of Notre Dame is now the MOSAIC (Mobile Sensing and Analytics) Lab at FIU! Follow these pages to learn about continuing and new projects in the area of opportunistic and participatory sensing, smart health applications, […]
- Negative Effects of COVID-19 Stay-at-Home Mandates on Physical Intervention OutcomesThe COVID-19 pandemic has been difficult for everyone, especially those in high-risk populations. Due to state-wide stay-at-home mandates, it was especially difficult for individuals with Parkinson’s Disease. Although the COVID-19 stay-at-home mandate was intended to help protect individuals at high-risk from coming into contact with the virus, it also prevented individuals with PD from receiving recommended structured and supervised exercise interventions. The presented work was […]
- MobiHealth’s Best Paper AwardThe Best Paper Award was received by John Templeton, Christian Poellabauer, and Sandra Schneider for their paper entitled ‘Design Of A Mobile-Based Neurological Assessment Tool For Aging Populations’ as part of EAI MobiHealth 2020. This paper assesses the usability of a neurocognitive assessment application by individuals with Parkinson’s Disease (PD) and proposes a design that focuses on the user interface, specifically on testing instructions, layouts, and subsequent user interactions.
The MOSAIC (Mobile Sensing and Analytics) Lab at Florida International University (FIU) investigates how mobile, wireless, and wearable computers and sensors can be used to address many pressing problems in areas such as healthcare, conservation, transportation, education, and more. Our lab studies a variety of sensing challenges, such as how to design participatory and opportunistic data collections at large scales and over long period of times, how to fuse many different sensing modalities, how to use context information to increase the sensing efficiency and the quality of the collected data, and how to do all that in a secure and privacy-conserving manner. From an analytics perspective, we use the collected data to obtain insights into user behavior and to provide opportunities for the early detection of various diseases and to monitor disease progression or patient recovery. We also investigate how to use the insights obtained from the sensor data to to develop new solutions in areas such as smart cities, smart transportation, and wireless communications. Our work also investigates how to develop machine and deep learning techniques that can provide timely decisions and outputs on resource-constrained and distributed systems.