iP3T: an interpretable multimodal time-series model for enhanced gait phase prediction in wearable exoskeletons

TL;DR

The iP3T model improves gait phase prediction in wearable exoskeletons by combining data from multiple sensors and using interpretable attention mechanisms.

Contribution

The novel iP3T model uses multimodal time-series data and transformer-based attention for interpretable and accurate gait phase prediction.

Findings

The iP3T model outperformed single-modality approaches in gait phase prediction.

In the post-stance phase, the model achieved an RMSE of 1.073 and an R2 of 0.985.

Multimodal data integration reduced metabolic cost during assisted treadmill walking.

Abstract

Wearable exoskeletons assist individuals with mobility impairments, enhancing their gait and quality of life. This study presents the iP3T model, designed to optimize gait phase prediction through the fusion of multimodal time-series data.

The iP3T model integrates data from stretch sensors, inertial measurement units (IMUs), and surface electromyography (sEMG) to capture comprehensive biomechanical and neuromuscular signals. The model's architecture leverages transformer-based attention mechanisms to prioritize crucial data points. A series of experiments were conducted on a treadmill with five participants to validate the model's performance.

The iP3T model consistently outperformed traditional single-modality approaches. In the post-stance phase, the model achieved an RMSE of 1.073 and an R2 of 0.985. The integration of multimodal data enhanced prediction accuracy and reduced metabolic cost during assisted treadmill walking.

The study highlights the critical role of each sensor type in providing a holistic understanding of the gait cycle. The attention mechanisms within the iP3T model contribute to its interpretability, allowing for effective optimization of sensor configurations and ultimately improving mobility and quality of life for individuals with gait impairments.

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