Context-Aware Activity Recognition Systems

This paper develops a wearable system using smartwatches to estimate ground reaction force, achieving high correlation with minimal error in healthy participants.

This study evaluates the feasibility of continuous head turn monitoring using wearable sensors, finding high reliability and establishing normative data in healthy adults.

This study uses wrist-worn accelerometers to classify bradykinesia fluctuations in Parkinson's patients at the single-minute timescale using individual and group models.

This paper develops models to classify accelerometry data as human wear or delivery days, finding that a hybrid convolutional recurrent neural network performs best.

This paper develops a machine learning model to dynamically adjust activity targets for users of activity trackers to improve adherence by personalizing goals based on historical data and environmental factors.

This paper demonstrates that adding thermal sensors to wrist-worn devices improves position tracking accuracy for head locations, with potential applications in monitoring body-focused repetitive behaviors.

The paper develops a machine learning model to classify physical activity and sleep behaviors from sensor data in UK Biobank participants, achieving 87% accuracy.