Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals

TL;DR

This study develops a deep learning-based system combining IMU and sEMG sensors for accurate, real-time motion classification in ankle exoskeletons, demonstrating high accuracy, transfer learning capabilities, and robustness to sensor failures.

Contribution

It introduces a novel sensor fusion framework with CNNs and LSTMs for motion prediction, including transfer learning for new users and robustness to sensor failures.

Findings

CNNs achieved 96.5% accuracy in motion classification.

Transfer learning enabled accurate new subject classification with minimal data.

System remained reliable despite sensor signal loss.

Abstract

Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility and reduce fall risks, particularly among the aging population. The efficacy of these devices relies on accurate real-time prediction of the user's intended movements through sensor-based inputs. This paper presents a novel motion prediction framework that integrates three Inertial Measurement Units (IMUs) and eight surface Electromyography (sEMG) sensors to capture both kinematic and muscular activity data. A comprehensive set of activities, representative of everyday movements in barrier-free environments, was recorded for the purpose. Our findings reveal that Convolutional Neural Networks (CNNs) slightly outperform Long Short-Term Memory (LSTM) networks on a dataset of five motion tasks, achieving classification accuracies of $96.5 \pm 0.8 \%$ and $87.5 \pm 2.9 \%$, respectively. Furthermore, we demonstrate the system's proficiency in transfer learning, enabling accurate motion classification for new subjects using just ten samples per class for finetuning. The robustness of the model is demonstrated by its resilience to sensor failures resulting in absent signals, maintaining reliable performance in real-world scenarios. These results underscore the potential of deep learning algorithms to enhance the functionality and safety of ankle exoskeletons, ultimately improving their usability in daily life.