This project explores whether consciously guided breathing strategies can enhance locomotor–respiratory coupling (LRC) during physical activity. It systematically evaluates the impact of prescribed step-to-breath ratios, such as 2:1 and 3:2, alongside distinct ventilatory patterns, including diaphragmatic and thoracic breathing, through the acquisition and analysis of synchronized multimodal biomechanical and physiological data.

The findings aim to inform performance optimization and provide non-invasive strategies for motor and respiratory rehabilitation.

This project investigated how voluntary modulation of breathing affected locomotor–respiratory coupling during walking and running. Controlled experiments were conducted using multimodal gait and respiratory monitoring systems.

Synchronization and variability were assessed using statistical, nonlinear, and model-based methods to develop non-invasive performance optimization and rehabilitation strategies.

This project developed an innovative methodology for diagnosing neurodegenerative diseases using multimodal gait time-series analysis. The approach enabled detection of subtle motor impairments and classification of daily activities.

Statistical, nonlinear, and machine learning techniques were applied to raw time-series data using an unsupervised framework, increasing scalability and reducing preprocessing requirements.

This project developed a methodology for diagnosing abnormal gait through time-series analysis of lower limb kinematics. Data were acquired using IMUs, video tracking, and Kinect-based systems during treadmill walking.

Nonlinear similarity measures including dynamic time warping, cross-correlation, covariance metrics, and Recurrence Quantification Analysis were applied to detect abnormal gait patterns.

The research aimed to provide a scalable, non-invasive diagnostic tool for clinical and rehabilitation applications.

This project investigated how joint mechanical clearance influences the performance of a human-wearable energy harvesting system that converts knee motion into electrical energy.

Differential equation modeling and response surface methodology were applied to analyze biomechanical and electromechanical system dynamics.