GCSP research stipend | Spring 2024

Enhancing Human Activity Recognition through 3D Modeling and Deep Learning: Advancement of Wearable Robotics through Time-series and Video Data Analysis

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This study explores the potential of enhancing wearable robotics by analyzing walking activity data from a number of subjects. The research team employs sophisticated algorithms to recognize distinct human activities, aiming to bridge the gap between human motion and robotic assistance. By decoding patterns within walking data, the project seeks to contribute significantly to the development of more intuitive and responsive wearable robotic systems.

Clarification of technical terms:

  • Wearable robotics refers to technology worn by users that augments their abilities.
  • Human activity recognition involves identifying specific movements or activities through data analysis.

Student researcher

Aishani Pathak

Computer science

Hometown: Seattle, Washington, United States

Graduation date: Spring 2026