Is this project an undergraduate, graduate, or faculty project?
Undergraduate
individual
What campus are you from?
Daytona Beach
Authors' Class Standing
Junior
Lead Presenter's Name
Liam Hunte
Faculty Mentor Name
Dr. Boetcher
Abstract
Though concurrent growth of technologies can sometimes lead to discord, often they lead to the meshing of ideas to form a new frontier of study, collaboration, and innovation. Recently, the inelastic collision of technologies has begotten a new frontier, called Medical Artificial Intelligence of Things (MAIot), edge-inferencing, encompassing the knowledge from Internet of Things (IoT) and Artificial Intelligence (AI), just targeted toward medicine using physiological data. Further, MAIoT is a discipline-focused subcategory of Artificial Intelligence of Things (AIoT) where feature detection from sensor data is done by a Machine Learning (ML) model programmed on a microprocessor unit (MPU); sequentially, inference results are communicated to an IoT network using additional software and an integrated circuit (IC) radio transceiver. In this study, Machine Vision (MV), a computer vision-based form of ML, is used to enhance camera data for a domain-specific purpose. High-power demand, specifically, from the camera, microprocessor, radio transceiver, and other sensors operating simultaneously, generates a load of approximately 7.916 Watts. This is a problem for the battery within the targeted handheld device. Therefore, a power management subsystem featuring battery HotSwap was conceived by combining hardware, software, and mechanical design to retain wireless modality, IoT connectivity, and practitioner dexterity during health monitoring.
Did this research project receive funding support from the Office of Undergraduate Research.
Yes, SURF
Design parameters of a power management system for an emerging Medical Artificial Intelligence of Things (MAIoT) technology
Though concurrent growth of technologies can sometimes lead to discord, often they lead to the meshing of ideas to form a new frontier of study, collaboration, and innovation. Recently, the inelastic collision of technologies has begotten a new frontier, called Medical Artificial Intelligence of Things (MAIot), edge-inferencing, encompassing the knowledge from Internet of Things (IoT) and Artificial Intelligence (AI), just targeted toward medicine using physiological data. Further, MAIoT is a discipline-focused subcategory of Artificial Intelligence of Things (AIoT) where feature detection from sensor data is done by a Machine Learning (ML) model programmed on a microprocessor unit (MPU); sequentially, inference results are communicated to an IoT network using additional software and an integrated circuit (IC) radio transceiver. In this study, Machine Vision (MV), a computer vision-based form of ML, is used to enhance camera data for a domain-specific purpose. High-power demand, specifically, from the camera, microprocessor, radio transceiver, and other sensors operating simultaneously, generates a load of approximately 7.916 Watts. This is a problem for the battery within the targeted handheld device. Therefore, a power management subsystem featuring battery HotSwap was conceived by combining hardware, software, and mechanical design to retain wireless modality, IoT connectivity, and practitioner dexterity during health monitoring.