Accurate Heuristic Terrain Prediction in Powered Lower-Limb Prostheses Using Onboard Sensors
| Autor: | Matthew Eli Carney, Hugh M. Herr, Roman Stolyarov |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
bepress|Engineering
Computer science Heuristic (computer science) medicine.medical_treatment 0206 medical engineering Biomedical Engineering Prosthetic limb Artificial Limbs Terrain bepress|Engineering|Biomedical Engineering and Bioengineering Walking 02 engineering and technology Kinematics bepress|Engineering|Biomedical Engineering and Bioengineering|Biomedical Devices and Instrumentation Prosthesis Design Prosthesis Lower limb Gait (human) Amputees Stairs Inertial measurement unit medicine Heuristics Humans Computer vision Gait engrXiv|Engineering|Biomedical Engineering and Bioengineering business.industry engrXiv|Engineering|Biomedical Engineering and Bioengineering|Biomedical Devices and Instrumentation 020601 biomedical engineering Biomechanical Phenomena medicine.anatomical_structure engrXiv|Engineering Pattern recognition (psychology) Artificial intelligence Ankle business |
| Zdroj: | Other repository |
| ISSN: | 1558-2531 0018-9294 |
| DOI: | 10.1109/tbme.2020.2994152 |
| Popis: | Objective: This study describes the development and offline validation of a heuristic algorithm for accurate prediction of ground terrain in a lower limb prosthesis. This method is based on inference of the ground terrain geometry using estimation of prosthetic limb kinematics during gait with a single integrated inertial measurement unit. Methods: We asked five subjects with below-knee amputations to traverse level ground, stairs, and ramps using a high-range-of-motion powered prosthesis while internal sensor data were remotely logged. We used these data to develop three terrain prediction algorithms. The first two employed state-of-the-art machine learning approaches, while the third was a directly tuned heuristic using thresholds on estimated prosthetic ankle joint translations and ground slope. We compared the performance of these algorithms using resubstitution error for the machine learning algorithms and overall error for the heuristic algorithm. Results: Our optimal machine learning algorithm attained a resubstitution error of $3.4\%$ using 45 features, while our heuristic method attained an overall prediction error of $2.8\%$ using only 5 features derived from estimation of ground slope and horizontal and vertical ankle joint displacement. Compared with pattern recognition, the heuristic performed better on each individual subject, and across both level and non-level strides. Conclusion and significance: These results demonstrate a method for heuristic prediction of ground terrain in a powered prosthesis. The method is more accurate, more interpretable, and less computationally expensive than machine learning methods considered state-of-the-art for intent recognition, and relies only on integrated prosthesis sensors. Finally, the method provides intuitively tunable thresholds to improve performance for specific walking conditions. |
| Databáze: | OpenAIRE |
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