Wearable Sensor Analytics
We use wearable sensor technologies to quantify athlete movement in real-world environments. Our research integrates plantar pressure systems and inertial sensors to capture performance, loading, and injury-related metrics outside the laboratory.
Why It Matters
Traditional laboratory assessments provide detailed insight into biomechanics, but often fail to capture how athletes move in real-world environments. Wearable sensor technologies enable continuous monitoring of movement, loading, and performance during training and competition.
This approach allows for more ecologically valid data collection, supporting improved performance analysis, injury risk monitoring, and evidence-based decision-making in sport.
Plantar Pressure Analysis
We utilize in-shoe pressure measurement systems to quantify foot loading patterns, center of pressure trajectories, and asymmetries during sport-specific movements.
Inertial Sensor Tracking
Inertial measurement units (IMUs) are used to capture kinematics and movement patterns in field settings, enabling analysis of acceleration, orientation, and segment motion.
Performance and Injury Monitoring
We develop approaches to link wearable-derived metrics with performance outcomes and injury risk, with a focus on identifying meaningful indicators in real-world sport contexts.
Methods & Capabilites
Our work combines wearable sensor technologies with advanced data processing and analysis techniques to quantify athlete movement in real-world environments. We deploy plantar pressure systems and inertial sensors across a range of sports to capture large-scale movement datasets.
These data are integrated with laboratory-based biomechanical assessments to validate and interpret wearable-derived metrics. We also apply data analytics and machine learning approaches to identify key features related to performance and injury risk.
Applications
Wearable sensor technologies enable scalable, field-based monitoring of athletes across training and competition. Our research supports applications in performance optimization, return-to-play assessment, and injury prevention.
We collaborate with sport organizations and industry partners to translate wearable data into actionable insights for coaches, clinicians, and athletes.
Data Analytics & Modeling
We apply advanced data analysis and modeling approaches to interpret complex biomechanical datasets collected from both laboratory and field environments. Our work focuses on identifying meaningful relationships between movement patterns, loading, and performance outcomes.
We utilize statistical modeling and machine learning techniques to extract key features from high-dimensional data, enabling the development of predictive models related to performance and injury risk. Particular emphasis is placed on interpretable approaches that provide insight into underlying biomechanical mechanisms.
By integrating wearable sensor data with laboratory-based measurements, we aim to bridge the gap between controlled experimentation and real-world athlete monitoring, supporting data-driven decision-making in sport.