Date of Award
May 2023
Degree Type
Thesis
Degree Name
Master of Science
Department
Computer Science
First Advisor
Rohit R Kate
Committee Members
Scott S Strath, Jun J Zhang
Keywords
energy estimation, image processing, video processing
Abstract
This research proposes a machine learning model that can estimate the energy cost of physical activities from video input. Currently, wearable sensors are commonly used for this purpose, but they have limitations in terms of practicality and accuracy. A deep learning model using three dimensional convolutional neural network (3D-CNN) architecture was used to process the video data and predict the energy cost in terms of metabolic equivalents (METs). The proposed model was evaluated on a dataset of physical activity videos and achieved an average accuracy of 71% on energy category prediction task and an root mean squared error (RMSE) of 1.14 on energy cost prediction task. The findings suggest that this approach has the potential for practical applications in physical activity surveillance, health interventions, and at-home activity monitoring.
Recommended Citation
Shrestha Chansi, Pragya, "Estimating Energy Cost of Physical Activities from Video Using 3D-CNN Networks" (2023). Theses and Dissertations. 3213.
https://dc.uwm.edu/etd/3213