Graduation Semester and Year

Spring 2024



Document Type


Degree Name

Master of Science in Computer Science


Computer Science and Engineering

First Advisor

Ming Li

Second Advisor

David Levine

Third Advisor

Abhishek Santra


Cycling presents a compelling solution for promoting personal health and environmental well-being, particularly for short-distance travel. Despite its numerous advantages, cycling uptake in the United States remains disproportionately low, primarily due to safety concerns. Traditional frameworks for assessing cyclist stress are hindered by their impracticality and inability to provide real-time evaluations. Self-report surveys and physiological measurements offer alternative approaches but suffer from limitations such as retrospective reporting biases and accessibility challenges, respectively. This thesis introduces CyclistAI, a novel smartphone-based cyclist stress assessment model that leverages context sensing. By combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) techniques, CyclistAI aims to provide real-time stress assessment for cyclists. Challenges in dataset annotation due to safety concerns are addressed through simulation-based data generation. Subsequent Domain Adaptation using Contrastive Learning bridges the simulation-to-reality gap and ensures the model’s efficacy in real-world scenarios. In-field testing of CyclistAI demonstrates promising results, showcasing its potential as a pioneering stress assessment tool. Furthermore, this thesis proposes utilizing aggregated assessment results to create stress distribution maps, facilitating informed decision-making for urban planners to enhance cycling infrastructure and promote safer, more sustainable transportation environments.


CyclistAI, Cyclist stress assessment, Cross domain contrastive learning, Deep learning, Domain adaptation, Cycling, Smartphone solution, Stress distribution heatmaps


Artificial Intelligence and Robotics | Software Engineering

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