Optimal Algorithms for Improving Pressure-Sensitive Mat Centre of Pressure Measurements

Area of Science:

• Biomechanics
• Sensor Technology
• Human Movement Analysis

Background:

• Accurate human balance measurement is crucial for analysis and training.
• Force plates are accurate but too expensive for widespread home use.
• Low-cost Pressure-Sensitive Mats (PSMs) offer a potential alternative but suffer from accuracy issues like low resolution, noise, and crosstalk.

Purpose of the Study:

• To develop novel algorithms for computing the Centre of Pressure (CoP) with enhanced accuracy using low-cost PSMs.
• To optimize PSM design and algorithms for improved balance measurement in accessible systems.

Main Methods:

• Developed a mathematical model of low-cost PSMs to determine optimal sensor geometry (track width and placement).
• Implemented algorithms utilizing footprint knowledge to further refine CoP accuracy.
• Derived a third algorithm incorporating human movement models to minimize measurement error.

Main Results:

• Optimized PSM geometries reduced mean absolute CoP error from 17.37% to 5.47% (8x8 sensor layout).
• Further optimization using footprint knowledge decreased absolute error to 3.93% (8x8 sensor layout).
• Algorithms demonstrated significant improvements in CoP measurement accuracy for low-cost PSMs.

Conclusions:

• Novel algorithms and optimized PSM designs substantially enhance the accuracy of low-cost balance measurement systems.
• These advancements pave the way for more affordable and reliable home-based balance training and analysis.
• The developed methods address key limitations of existing low-cost PSMs, improving their practical utility.