# FusionTCN-Attention: A Causality-Preserving Temporal Model for Unilateral IMU-Based Gait Prediction and Cooperative Exoskeleton Control

## TL;DR

This paper introduces a new model for predicting gait using motion sensors on one leg, which could help control exoskeletons for people with walking impairments.

## Contribution

FusionTCN-Attention is a novel causality-preserving model combining dilated convolutions and attention for real-time gait prediction from unilateral IMU data.

## Key findings

- FusionTCN-Attention achieved RMSEs of 5.71° and 7.43° for hip and knee predictions.
- The model showed correlation coefficients over 0.9 and a phase lag of 14.56 ms.
- It outperformed conventional models like Seq2Seq and causal Transformers.

## Abstract

Human gait exhibits stable contralateral coupling, making healthy-side motion a viable predictor for affected-limb kinematics. Leveraging this property, this study develops FusionTCN–Attention, a causality-preserving temporal model designed to forecast contralateral hip and knee trajectories from unilateral IMU measurements. The model integrates dilated temporal convolutions with a lightweight attention mechanism to enhance feature representation while maintaining strict real-time causality. Evaluated on twenty-one subjects, the method achieves hip and knee RMSEs of 5.71° and 7.43°, correlation coefficients over 0.9, and a deterministic phase lag of 14.56 ms, consistently outperforming conventional sequence models including Seq2Seq and causal Transformers. These results demonstrate that unilateral IMU sensing supports low-latency, stable prediction, thereby establishing a control-oriented methodological basis for unilateral prediction as a necessary engineering prerequisite for future hemiparetic exoskeleton applications.