Context-Aware Activity Recognition Systems

Edge-First Ground Reaction Force Estimation with Consumer Smartwatches

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

Continuous Monitoring of Head Turns: Compliance, Kinematics, and Reliability of Wearable Sensing

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

Rapid dynamic naturalistic monitoring of bradykinesia in Parkinson's disease using a wrist-worn accelerometer

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

Statistical and machine learning models for classification of human wear and delivery days in accelerometry data

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

A Neural Network Based Algorithm for Dynamically Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers

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.

Thermal sensors improve wrist-worn position tracking

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.

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants

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