Deep Learning-based Approaches of Intention Detection for Shared Control of an Assistive Soft Upper-Limb Exoskeleton
Creators: Sedighi, Paniz
Publisher: name: University of Alberta Library
Year: 2024

<p>In the evolving field of human-robot interaction, the use of surface electromyography (sEMG) stands out as a critical tool for assessing active muscle effort, essential for controlling upper extremity exoskeletons based on user intention and task-specific movements. Despite the strides in real time exoskeleton control, the direct prediction of body joint positions from EMG signals has not been thoroughly explored. Addressing this research gap, our study introduces a cutting-edge methodology that utilizes Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) models to create accurate exoskeleton joint trajectories, enhancing the interaction between users and assistive technologies.</p>

<p>Our innovative approach combines EMG data with Inertial Measurement Unit (IMU) signals to decode user intentions, facilitating the generation of precise movement trajectories for a 3-DoF cable-driven upper-limb exoskeleton. By incorporating an attention mechanism within a sequence-to-sequence encoder-decoder architecture, our model dynamically prioritizes critical EMG features and historical angular positions. This not only improves prediction accuracy but also introduces a level of personalization and adaptability previously unseen in robotic rehabilitation devices, catering to the unique requirements of individuals with upper limb impairments.</p>

<p>Personalization is paramount in the myoelectric control of robotic exoskeletons to accurately interpret and adapt to each user&#39;s distinct muscle activity patterns and movement intentions, ultimately minimizing the risk of injury and enhancing user comfort. To this end, our research leverages a Model Agnostic Meta-Learning (MAML) framework for personalizing a soft upper limb exoskeleton for industrial applications. This innovative framework, centered around an attention-based CNN-LSTM model, efficiently predicts future angular positions from EMG and IMU data with minimal input, showcasing the model&#39;s capability for rapid online adaptation to new tasks, movements, and users.</p>

<p>The integration of data from three EMG channels and three degrees-of-freedom (DoF) joint angles enables our model to control a pneumatic cable-driven upper-limb exoskeleton effectively, providing tailored assistance to users across various tasks. Our experiments demonstrate the model&#39;s robust performance in detecting changes in speed, payload, and electrode placement, significantly reducing EMG levels during task execution by offering as-needed exoskeleton support.</p>

<p>The implementation of the attention mechanism within our model marks a significant advancement in enhancing the control systems of assistive technologies. It not only compensates for sensor unreliability but also tailors the system&#39;s response to individual user signals, allowing for the adaptive prediction of complex movement trajectories. This level of dynamic learning and responsiveness signifies a breakthrough in achieving personalized rehabilitation and assistance.</p>

<p>Our findings reveal a notable decrease in user effort during load-bearing tasks, alongside an increase in the torque exerted by the exoskeleton, all while maintaining superior accuracy. These results underscore the effectiveness of our approach in offering personalized, efficient, and safe myoelectric control of robotic exoskeletons. A comparison with traditional methods further underscores the superior adaptability provided by the meta-learning approach, positioning our study at the forefront of personalized myoelectric control system development. The work in the thesis has the potential to be used to improve the quality of life for individuals with mobility impairments by offering more natural and intuitive control over assistive devices, thereby facilitating their daily activities and accelerating rehabilitation processes</p>

<p>In conclusion, this thesis presents a comprehensive framework that combines CNN-LSTM, attention mechanisms, and MAML for the personalization of upper-limb exoskeleton control. By addressing the challenges of real time adaptability and user-specific customization, our work paves the way for future advancements in assistive robotics, potentially revolutionizing how we approach rehabilitation, injury prevention, and human-robot interaction in industrial and clinical settings.</p>