Sensors' Ground Reaction Force behavior for both Normal and Parkinson subjects--A qualitative study

Area of Science:

• Biomechanics
• Biomedical Engineering
• Data Science

Background:

• Gait analysis is crucial for fall prevention, sports biomechanics, and disease detection.
• Understanding Vertical Ground Reaction Force (VGRF) is key to characterizing gait.

Purpose of the Study:

• To develop a reliable mathematical model of VGRF for normal and abnormal gait.
• To lay the groundwork for fall prevention indicators in elderly patients.

Main Methods:

• Statistical analysis of VGRF time-domain properties during walking.
• Signal segmentation to assess stationarity.
• Autoregressive modeling and linear regression for waveform fitting.
• Exploration of multi-sensor data cross-covariance and seasonality.

Main Results:

• Linear regression via Autoregressive Model successfully modeled VGRF waveforms for normal and Parkinson's disease gait using a single sensor.
• Identified seasonality in VGRF data provides important behavioral indications.

Conclusions:

• Statistical analysis of VGRF shows promise for differentiating normal and abnormal gait.
• Future work will leverage multi-sensor data for enhanced modeling and fall prevention indicators.