Publication Date

Spring 2025

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Sayma Akther

Second Advisor

Robert Chun

Third Advisor

Vatsal Bhanderi

Keywords

Human Activity Recognition, Activity Complexity, Sensor Data, Time-Series Classification, Feature Engineering, Convolutional Neural Networks, Transformer Models, Opportunity Dataset, Leave-One-User-Out, Deep Learning

Abstract

In-depth understanding of the complexity of daily human activities is crucial for building responsive health monitoring and assistive technologies. However, limited research has focused on distinguishing activities based on their involvement level, as most existing work classifies only the type of activity performed. In this thesis, we address this gap by proposing a method to classify human activities as either simple or complex using sensor data from the Opportunity dataset. We define complex activities as those involving object interactions or multiple coordinated movements (e.g., drinking from a cup, cleaning a table), and simple activities as static or low-effort postures (e.g., standing, sitting). Based on domain-specific heuristics, we label each time window and extract both time-domain and frequency-domain features. We evaluate multiple models, including a hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) and Transformers, trained on multi-sensor data across varied window lengths. To ensure robustness and generalization, we apply Leave-One-User-Out (LOUO) and Leave-One-Episode- Out (LOEO) evaluation. Our results show that the proposed approach reliably distinguishes between simple and complex activities, outperforming traditional classifiers and offering new directions for fine-grained activity recognition in real-world environments.

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Available for download on Wednesday, May 20, 2026

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