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Título: | Daily Locomotion Recognition and Prediction: A Kinematic Data-Based Machine Learning Approach |
Autor: | Figueiredo, Joana; Carvalho, Simão P.; Goncalve, D.; Moreno, Juan Camilo CSIC ORCID ; Santos, Cristina P. | Palabras clave: | Kinematic data Machine learning motion intention recognition motion transition prediction |
Fecha de publicación: | 2020 | Editor: | Inter Research | Citación: | IEEE Access 8: 33250- 33262 (2020) | Resumen: | More versatile, user-independent tools for recognizing and predicting locomotion modes (LMs) and LM transitions (LMTs) in natural gaits are still needed. This study tackles these challenges by proposing an automatic, user-independent recognition and prediction tool using easily wearable kinematic motion sensors for innovatively classifying several LMs (walking direction, level-ground walking, ascend and descend stairs, and ascend and descend ramps) and respective LMTs. We compared diverse state-of-the-art feature processing and dimensionality reduction methods and machine-learning classifiers to find an effective tool for recognition and prediction of LMs and LMTs. The comparison included kinematic patterns from 10 able-bodied subjects. The more accurate tools were achieved using min-max scaling [-1; 1] interval and “mRMR plus forward selection” algorithm for feature normalization and dimensionality reduction, respectively, and Gaussian support vector machine classifier. The developed tool was accurate in the recognition (accuracy >99% and >96%) and prediction (accuracy >99% and >93%) of daily LMs and LMTs, respectively, using exclusively kinematic data. The use of kinematic data yielded an effective recognition and prediction tool, predicting the LMs and LMTs one-step-ahead. This timely prediction is relevant for assistive devices providing personalized assistance in daily scenarios. The kinematic data-based machine learning tool innovatively addresses several LMs and LMTs while allowing the user to self-select the leading limb to perform LMTs, ensuring a natural gait. | Versión del editor: | http://dx.doi.org/10.1109/ACCESS.2020.2971552 | URI: | http://hdl.handle.net/10261/217372 | DOI: | 10.1109/ACCESS.2020.2971552 | Identificadores: | doi: 10.1109/ACCESS.2020.2971552 issn: 2169-3536 |
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Daily Locomotion Recognition.pdf | 2,52 MB | Adobe PDF | Visualizar/Abrir |
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