Human-in-the-loop optimization of wearable device parameters using an EMG-based objective function
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| Published in: | Wearable Technologies vol. 5 (Nov 2024) |
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| Other Authors: | , , , , |
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Cambridge University Press
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| 001 | 3131715193 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2631-7176 | ||
| 024 | 7 | |a 10.1017/wtc.2024.9 |2 doi | |
| 035 | |a 3131715193 | ||
| 045 | 2 | |b d20241101 |b d20241130 | |
| 100 | 1 | |a Díaz, María Alejandra |u BruBotics, Vrije Universiteit Brussel, Brussels, 1050, Belgium; Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, 1050, Belgium | |
| 245 | 1 | |a Human-in-the-loop optimization of wearable device parameters using an EMG-based objective function | |
| 260 | |b Cambridge University Press |c Nov 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Advancements in wearable robots aim to improve user motion, motor control, and overall experience by minimizing energetic cost (EC). However, EC is challenging to measure and it is typically indirectly estimated through respiratory gas analysis. This study introduces a novel EMG-based objective function that captures individuals’ natural energetic expenditure during walking. The objective function combines information from electromyography (EMG) variables such as intensity and muscle synergies. First, we demonstrate the similarity of the proposed objective function, calculated offline, to the EC during walking. Second, we minimize and validate the EMG-based objective function using an online Bayesian optimization algorithm. The walking step frequency is chosen as the parameter to optimize in both offline and online approaches in order to simplify experiments and facilitate comparisons with related research. Compared to existing studies that use EC as the objective function, results demonstrated that the optimization of the presented objective function reduced the number of iterations and, when compared with gradient descent optimization strategies, also reduced convergence time. Moreover, the algorithm effectively converges toward an optimal step frequency near the user’s preferred frequency, positively influencing EC reduction. The good correlation between the estimated objective function and measured EC highlights its consistency and reliability. Thus, the proposed objective function could potentially optimize lower limb exoskeleton assistance and improve user performance and human–robot interaction without the need for challenging respiratory gas measurements. | |
| 653 | |a Ankle | ||
| 653 | |a Physiology | ||
| 653 | |a Convergence | ||
| 653 | |a Walking | ||
| 653 | |a Robot dynamics | ||
| 653 | |a Musculoskeletal system | ||
| 653 | |a Electromyography | ||
| 653 | |a Optimization | ||
| 653 | |a Human performance | ||
| 653 | |a Robots | ||
| 653 | |a Wearable technology | ||
| 653 | |a Algorithms | ||
| 653 | |a Metabolism | ||
| 653 | |a Gas analysis | ||
| 653 | |a Muscle function | ||
| 653 | |a Exoskeletons | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Parameters | ||
| 653 | |a Energy consumption | ||
| 653 | |a Biomechanics | ||
| 653 | |a Robot control | ||
| 653 | |a Robotics | ||
| 700 | 1 | |a De Bock, Sander |u Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, 1050, Belgium | |
| 700 | 1 | |a Beckerle, Philipp |u Institute of Autonomous Systems and Mechatronics, Department of Electrical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, 91052, Germany; Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, 91052, Germany | |
| 700 | 1 | |a Babič, Jan |u Laboratory for Neuromechanics and Biorobotics, Department of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, 1000, Slovenia; Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, 1000, Slovenia | |
| 700 | 1 | |a Verstraten, Tom |u BruBotics, Vrije Universiteit Brussel, Brussels, 1050, Belgium; Robotics and Multibody Mechanics Research Group, Vrije Universiteit Brussel and Flanders Make, Brussels, 1050, Belgium | |
| 700 | 1 | |a De Pauw, Kevin |u BruBotics, Vrije Universiteit Brussel, Brussels, 1050, Belgium; Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, 1050, Belgium | |
| 773 | 0 | |t Wearable Technologies |g vol. 5 (Nov 2024) | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3131715193/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3131715193/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3131715193/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |