# Tactile Sensor-Based Body Center of Pressure Estimation System Using Supervised Deep Learning Models

## TL;DR

This paper introduces a tactile sensor system using deep learning to estimate body balance, offering a low-cost alternative to expensive force plates.

## Contribution

A novel deep learning model combining CNN/ResNet and Bi-LSTM for accurate CoP estimation from tactile sensor data.

## Key findings

- ResNet-Bi-LSTM achieved ML/AP NRMSE difference of 1.3%, outperforming previous methods with 3.2–4.7%.
- The model showed consistent performance in dynamic balance tasks with the lowest RMSE.
- Tactile sensor-based systems may replace force plates for balance monitoring in gait analysis.

## Abstract

The center of pressure (CoP) is a key biomechanical indicator for assessing balance and fall risk; however, force plates, the gold standard for CoP measurement, are costly and impractical for widespread use. Low-cost alternatives such as inertial units or pressure sensors are limited by drift, sparse sensor coverage, and directional performance imbalances, with previous supervised learning approaches reporting ML-AP NRMSE differences of 3.2–4.7% using 1D time-series models on sparse sensor arrays. Therefore, we propose a tactile sensor-based CoP estimation system using deep learning models that can extract 2D spatial features from each pressure distribution image with CNN/ResNet encoders followed by a Bi-LSTM for temporal patterns. Using data from 23 healthy adults performing four balance protocols, we compared ResNet-Bi-LSTM and CNN-Bi-LSTM with baseline CNN-LSTM and Bi-LSTM models used in previous studies. Model performance was validated using leave-one-out cross-validation (LOOCV) and evaluated with RMSE, NRMSE, and R2. The ResNet-Bi-LSTM with angular features achieved the best performance, with RMSE values of 18.63 ± 4.57 mm in the mediolateral (ML) direction and 17.65 ± 3.48 mm in the anteroposterior (AP) direction, while reducing the ML/AP NRMSE difference to 1.3% compared to 3.2–4.7% in previous studies. Under dynamic protocols, ResNet-Bi-LSTM maintained the lowest RMSE across models. These findings suggest that tactile sensor-based systems may provide a cost-effective alternative to force plates and hold potential for applications in gait analysis and real-time balance monitoring. Future work will validate clinical applicability in patient populations and explore real-time implementation.