A New Approach for Real-Time Center-of-Pressure Correction in Pressure Sensitive Mats Using Feedforward Neural Networks

Center-of-pressure (CoP) is a good clinical indicator in balance tests and fall-risk assessment. It can be detected using pressure sensitive mats (PSMs), which are affordable. However, these can suffer from certain non-idealities, like hysteresis, creep, etc. These effects have been assessed in literature. However, proposed algorithms have low computation speed and are complex. In this work, a completely new approach based on feed-forward neural networks (FFNNs) is proposed with the goal of correcting the CoP given by PSMs, allowing real-time correction. Its performance is compared in terms of error and computation times with a state-of-the-art model, which corrects for hysteresis and creep in the PSM. Results show that FFNN can correct for the CoP measurements, providing a good accuracy-speed balance.