Intelligent Elbow Exoskeleton Control: A Neural Network-Based Framework for Optimized Performance

Abstract

Elbow exoskeletons have emerged as promising technologies in the field of wearable robotics, offering assistance and support for tasks involving elbow flexion and extension. Musculoskeletal disorders associated with the elbow are prevalent in occupational environments, leading to work-related injuries and discomfort. Active elbow exoskeletons with integrated sensors, actuators, and control boards have been proposed to mitigate these issues by reducing joint strain and supporting repetitive tasks. The design and control of elbow exoskeletons are essential to ensure effective assistance, user comfort, and operational safety. Key design considerations include joint alignment, adaptability to real-world tasks, and intuitive user interaction to enhance usability and acceptance. Although current control strategies have made significant progress, they still require improvements in terms of user adaptability, feedback responsiveness, robustness, energy efficiency, and dynamic assistance. This study introduces a comprehensive methodological framework to optimise control strategies in the ExoElbow. The primary focus is on adapting assistive responses to individual user needs through real-time adjustments using advanced neural network architectures. Neural networks enable the system to learn from user inputs, adapt to feedback, model dynamic behaviours, and personalise assistance strategies. Convolutional Neural Networks are used to extract spatial features from sensor data, providing insights into user movement patterns and environmental cues while supporting energy-efficient computation. Recurrent Neural Networks are employed to capture temporal dynamics, enabling predictive assistance and smooth adaptation to varying task demands, which are key for real-time, user-centred control. Together, these models support intuitive human-machine interaction, such as brain-machine interfaces, significantly enhancing the usability and responsiveness of the system. The proposed control system dynamically adjusts assistive torque levels by continuously monitoring and analysing sensory inputs, thereby optimising user experience while reducing discomfort and strain. Validation strategies, including simulation and real-world experimentation, will be used to assess performance and user satisfaction. By addressing the limitations in adaptability, intuitive interaction, and energy efficiency found in existing approaches, this research lays the foundation for smarter, more responsive assistive technologies in active industrial exoskeletons.

Keywords: Intelligent Industrial Exoskeleton, Human-Machine Interface, Adaptive Control, Neural Networks

DOI: 10.54941/ahfe1006723