A Real time method for evaluating and monitoring heat stress potential using wearable bio-sensors
The problem of exertional heat illness is especially prominent in working populations that perform routine physical labor. Firefighters and members of the armed forces fit this criteria particularly well due to the strenuous work associated with the job in combination with thick and heavy personal protective equipment (PPE) and the typically high ambient temperature of their working area. This is a multifaceted issue that arises from inadequate rehydration, performing high amounts of physical labor without adequate rest and caloric intake, and elevated core temperature. The goal of this research (data forthcoming) is to understand the interaction between easily accessible readings taken in real-time by wearable biosensors and known contributors to heat illness using wildland firefighters as the model population. This will be accomplished by utilizing input from a wearable biosensor to develop or improve existing predictive algorithms to estimate overall potential for heat illness.
Aim 1: Develop a regression model between easily obtained readings from a biosensor to predict full body hydration levels.
Aim 2: Investigate if the Bernard Metabolic equation can be modified to account for altitude and incorporate a modifier into the existing algorithm which accounts for how metabolic demand of an equivalent physical task varies with worker elevation
Aim 3: Develop a regression model to predict core body temperature non-invasively as a function of biosensor readings
The biosensor monitors heart rate, skin temperature, activity level, and altitude of work performed in real-time and is capable of streaming this data via Bluetooth to a nearby computer or smartphone. Future work will involve implementing these algorithms into the biosensor’s coding and implementing an alert system to notify the worker and safety monitor when they are at an elevated risk for exertional heat illness.
Reducing traumatic brain injury with smart collision detection and mitigation
The research goals of this proposal are to (1) reduce the risk of traumatic brain injury through advanced situational monitoring, musculoskeletal activation, and impact-specific force reduction; and (2) to improve potential identification of head injury risk based on multiscale brain deformation modeling. My role on the project is to develop a musculoskeletal model to understand how to minimize head angular acceleration following head or body impact. The development of auditory warning cues and muscle clench strategies utilizes kinematic musculoskeletal modeling and human subject studies to identify required auditory cues and response times as well as muscle activation parameters that best mitigate head angular acceleration during a collision. These studies will be used to improve predictions of TBI risk from impact kinematics.