Robust displacement estimation from filament-based soft sensors using a parallel attention-enhanced LSTM for rehabilitation monitoring.

Accurate time-series prediction from soft sensor signals is essential for sensor-driven rehabilitation systems, yet remains challenging due to sensor noise, nonlinear dynamics, and complex temporal dependencies. In particular, filament-based tactile sensors exhibit long-term drift, hysteresis, and redundant signal components that degrade the performance of conventional recurrent models. To address these limitations, this paper proposes a novel Parallel Attention-Enhanced Long Short-Term Memory (PA-LSTM) architecture for robust displacement prediction from soft sensor data. The proposed model integrates an LSTM-based temporal encoder with a parallel dense embedding pathway and a Bahdanau-style attention mechanism, enabling adaptive weighting of informative time steps while suppressing noise and irrelevant signal fluctuations. By jointly capturing short-term dynamics and global contextual features, PA-LSTM enhances temporal feature selection and representation learning under noisy sensing conditions. The model is evaluated using pressure-displacement data collected from filament-based tactile sensors in a rehabilitation-oriented experimental setup. Extensive experiments demonstrate that PA-LSTM consistently outperforms standard LSTM, GRU, CNN-LSTM, and attention-only baselines. Specifically, the proposed approach achieves an RMSE of 0.047, an MAE of 0.028, and an R² score of 0.963, indicating substantial improvements in prediction accuracy and robustness. These results confirm that PA-LSTM effectively models complex soft-sensor dynamics and is well-suited for real-time displacement estimation in wearable rehabilitation and soft robotic sensing applications.