- Title: Estimating Energy Expenditure During Exercise Using Wearable Sensors
- Speaker: Patrick Slade, PhD, Stanford University
- Time: Wednesday, May 4th, 2022 at 9:00 AM Pacific Time
This event has passed. View the recorded talk and additional resources below.
Estimating the calories burned during exercise is important for understanding physical activity and managing weight. Wearable tools for counting calories, such as smartwatches, provide the opportunity to make these measurements outside of a laboratory environment but have errors of 40-80%, making them ineffective. Dr. Slade and colleagues developed a new approach to estimate calories burned by using two inertial measurement units placed on the shank and thigh of one leg. Using machine learning, they are able to estimate calories once per step to rapidly capture changes in energy expenditure without physiological signal delays, as is common when making these measurements based on heart rate and respirometry. Their data-driven approach has approximately 13% error when tested with a physiologically diverse adult population during walking, running, stair climbing, and biking.
In the first half of the webinar, Dr. Slade will discuss the background and experimental work used to design and validate the Wearable System. In the tutorial during the second half of the webinar, Dr. Slade will share tips and guide you through the code so you can utilize this approach to estimate calories burned from wearable sensors in your own studies.
Slade, P., Kochenderfer, M.J., Delp, S.L., Collins, S. Sensing leg movement enhances wearable monitoring of energy expenditure. Nat Commun 12, 4312 (2021)
This webinar is offered jointly with the Restore Center, an NIH-funded Medical Rehabilitation Research Resource Network Center at Stanford University.
About Our Speaker
Patrick Slade, PhD
Patrick Slade received his Ph.D. in Mechanical Engineering from Stanford in 2021 and is now a Distinguished Postdoctoral Scholar in Bioengineering as part of the Wu Tsai Human Performance Alliance at Stanford. His research combines robotics, biomechanics, and human-centered AI to build intelligent health models and assistive devices. He explores how we can safely and effectively integrate human-robot systems so people can live healthier and more independent lives. His work focuses on solutions that are open-source and low-cost to make care accessible for underserved populations.