Kinematic Integration Network With Enhanced Temporal Intelligence and Quality-Driven Attention for Precise Joint Angle Prediction in Exoskeleton-Based Gait Analysis
Year: 2025
Type: article
Abstract: Exoskeleton robots hold tremendous promise in aiding the rehabilitation of patients with lower limb motor dysfunction. However, their effectiveness relies on the real-time and accurate prediction of knee joint angles. This study introduces KINETIQA (Kinematic Integration Network with Enhanced Temporal Intelligence and Quality-driven Attention), a novel model that significantly pushes the boundaries of knee angle prediction, offering substantial enhancements in clinical practice. The core strength of KINETIQA lies in its Kinematic Integration Network (KIN), a sophisticated architecture seamlessly integrating state-of-the-art techniques such as Transformers, temporal convolutions, and multi-headed attention mechanisms. This powerful synergy equips KINETIQA to capture the intricate dynamics of knee movement, both in the blink of an eye and over longer durations, resulting in a notable 20% reduction in prediction error compared to existing models. In addition to accuracy, KINETIQA employs rigorous feature selection and validation methods to guarantee clinical-grade precision. These processes offer clinicians crucial insights for tailored rehabilitation, injury prevention, and movement analysis. Our model's pioneering design integrates Transformers and temporal convolutions to capture detailed temporal dynamics of knee movement, leading to markedly enhanced prediction compared to current frameworks. These positive findings indicate that the proposed approach holds significant promise for application in exoskeleton robot control. The source code for KINETIQA is publicly available on GitHub at https://github.com/LyesSaadSaoud/KINETIQA/
Source: IEEE Access
Authors: Lyes Saad Saoud, Irfan Hussain
Institution: Khalifa University of Science and Technology
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4411599713