Date of Award

May 2023

Degree Type


Degree Name

Master of Science


Computer Science

First Advisor

Rohit R Kate

Committee Members

Scott S Strath, Jun J Zhang


energy estimation, image processing, video processing


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.