Md Shahab Uddin

This project involved developing a framework to synchronize treadmill belt speed signals during perturbed postural balance experiments, addressing key challenges in biomechanics. Accurate alignment of biomechanical signals, such as belt speed and displacement, is essential to understanding human balance recovery mechanisms.

Using MATLAB, I applied cross-correlation techniques to align belt speed signals with a reference template created by averaging data from multiple trials. Advanced filtering methods were used to process raw data and preserve critical variations caused by perturbations. Removing low correlation values and optimizing peak detection enhanced the accuracy of synchronization.

The project successfully synchronized 10 perturbation events with minimal lag variation, demonstrating alignment consistency and reliability for analyzing postural control dynamics. This method improves the precision of balance studies and supports applications in fall prevention and rehabilitation.

Through this work, I gained expertise in signal processing, MATLAB programming, and biomechanics while building a strong foundation in data analysis and problem-solving for biomechanical research.

I explored how older adults react to sudden balance disturbances and how these neuromuscular responses relate to their ability to maintain stability. Using an instrumented treadmill, I applied controlled backward perturbations at two speeds (slow and fast) and recorded surface EMG signals from 11 lower limb muscles. I analyzed muscle activation timing, amplitude, and detection rates, alongside center of pressure (CoP) metrics to assess balance performance.

Key findings include:

Posterior muscles (e.g., soleus, gastrocnemius, hamstrings) showed the most consistent and robust responses, especially during fast perturbations.

Faster perturbations triggered earlier and stronger muscle activity, indicating adaptive scaling of neuromuscular responses.

Modest correlations between EMG parameters and CoP displacement suggest that reflexive muscle activity contributes to balance recovery, though individual coordination strategies vary.

This research enhances our understanding of age-related changes in postural control and supports the use of EMG-based assessments in fall prevention strategies for older adults