The studies of human physiology, movement biomechanics and environmental interaction are generally conducted in laboratory settings using standard lab equipment such as Electrocardiography (ECG), respiration belt, motion capture cameras and a force-plate instrumented treadmill. With recent advancements in wearable technology, research on human behavior, physiology and biomechanics in real-world environments has become much more viable and offers a means to collect real-world data from a broader range of activities. However, current wearable devices are typically a stand-alone system, each employing its own hardware and software interfaces that often vary between different systems, thus making it difficult to simultaneously integrate and instrument them on a user for comprehensive and synchronous multimodal measurements. To overcome this limitation, we propose a modular, reconfigurable, and multimodal wearable sensor network for real-time monitoring and data acquisition of different biomechanics and physiological parameters as well as environmental parameters. The system incorporates a two-tier sensor network: the first tier utilizes various wearable sensors with a microcontroller and the second tier consists of an efficient edge computing device for real-time data processing, data logging and wireless data transmission. The novel feature of the system that differentiates itself from existing wearable sensor systems is the modular and reconfigurable design in a wearable form, its scalability, easy accessibility, and integration with external computing devices. The outcomes of this research demonstrate an efficient multimodal wearable sensor network for use in many applications where human health and ambience monitoring is needed.
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Master of Science in Electrical Engineering (M.S.E.E.)
College of Engineering and Computer Science
Electrical and Computer Engineering
Length of Campus-only Access
Masters Thesis (Campus-only Access)
Devasundaram, Surendar, "Reconfigurable Multimodal Wearable Sensor Network for Holistic Human Health and Ambience Monitoring" (2021). Electronic Theses and Dissertations, 2020-. 1132.
Restricted to the UCF community until February 2027; it will then be open access.