I build real-time, IMU-based gait analysis and haptic feedback systems that help prevent falls and restore mobility in older adults — bridging wearable hardware, signal processing, and biomechanics.
Gait analysis starts with raw inertial signals. Below is a live illustration of multi-IMU accelerometer streams — the periodic spikes mark heel-strike events my algorithms detect to estimate cadence, foot clearance, and balance.
Illustrative signals — representative of multi-IMU accelerometer data during walking.
My work integrates multiple sensing modalities into real-time systems that measure, interpret, and respond to how people move.
Multi-sensor IMU networks (Xsens DOT V2, Awinda MTw) for comprehensive, body-worn gait analysis.
Real-time obstacle detection, foot-clearance estimation, and terrain classification during walking.
Adaptive sensory cueing for gait correction and fall prevention, driven by live sensor data.
Mobile apps for on-device sensor acquisition, visualization, and analysis using the Movella DOT SDK.
MATLAB pipelines for multi-sensor synchronization, data fusion, and biomechanical feature extraction.
Translating research into practical solutions for mobility and quality of life in elderly adults.
A snapshot of the path so far — from foundations to current work.
Biorobotics & Biomechanics Lab under Dr. Babak Hejrati — real-time IMU gait analysis & haptic systems.
Published work on real-time foot clearance for fall prevention using only smartphone sensors.
Coordinating distributed Xsens networks with wireless streaming and real-time aggregation.
Hardware integration, software development, and biomechanical analysis bridging lab and practice.
Open to research collaborations, discussions on wearable sensing, and opportunities to bring gait-analysis technology into the real world.