Overview

My research focuses on developing and validating wearable inertial measurement unit (IMU)-based sensing systems for real-time gait analysis and fall prevention in elderly adults. I integrate multi-modal sensing, biomechanical analysis, and haptic feedback to create practical solutions that improve mobility and quality of life.

Key Research Areas

Wearable IMU Sensing

• Multi-sensor synchronization and calibration
• Real-time data acquisition from distributed sensors
• Xsens DOT V2 and Xsens Awinda MTw networks
• Wireless communication and data streaming

Gait Analysis

• Real-time obstacle detection during walking
• Foot clearance estimation using smartphone-based approaches
• Terrain classification and surface characterization
• Temporal-spatial gait parameters

Adaptive Haptic Feedback

• Real-time haptic cueing for gait correction
• Fall prevention through sensory augmentation
• Wearable haptic device integration
• User studies on feedback effectiveness

Signal Processing & Data Analysis

• Multi-sensor data synchronization and fusion
• MATLAB-based analysis pipelines
• Machine learning for terrain and obstacle classification
• Real-time processing on mobile platforms

Current Projects

Obstacle-Crossing Study: Multi-IMU based gait analysis during obstacle negotiation in elderly adults

Foot Clearance Estimation: Real-time smartphone-based foot height monitoring for fall prevention

Terrain Classification: Machine learning approach to classify walking surfaces using distributed IMU sensors

Mobile Applications: Android app development for real-time sensor data acquisition and on-device processing

Publications

Research outputs include peer-reviewed journal articles