Machine learning for grasping recognition using wearable sensors

Autores

DOI:

https://doi.org/10.31637/epsir-2025-1205

Palavras-chave:

Grasp recognition, Hand motion analysis, Wearable sensors, Windowing, Human behavior inference, Segmentation, Traditional classifiers, F-Score

Resumo

Introduction: The objective is to evaluate the traditional classifiers for the identification of the grasp while doing different jobs, in order to obtain information that can be used in the diagnostic of the physical work requirements and job design. Methodology: The analysis considered different combinations of the data acquired from inertial and force resistive sensors: a) acceleration and resistive force sensors, b) acceleration, angular velocity and resistive force sensors c) acceleration, angular velocity, magnetic fields, and resistive force sensors. Different combinations of window and step sizes were selected with two overlap options: 50% and greater than 50%. Traditional classification models were trained: support vector machines, ensembles, Naive-Bayes algorithm. Results: Results demonstrate that the window size that presented optimal performance in the present study was 3 seconds with an overlap greater than 50%, the window size is greater than that suggested in the literature, which ranges from 0.75 to 2.25 seconds. Conclusions: The accuracy and F-score metrics for the different window-step combinations are presented, both metrics indicate that the models trained through Support Vector Machine have the best performance (90 %) with the combination of acceleration, angular velocity, and resistive force sensor.

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Biografias Autor

Graciela Rodriguez Vega, Universidad de Sonora

Industrial Engineer by the TecNM/Instituto Tecnológico de Los Mochis. Master of Science in Industrial Engineering from the TecNM/Instituto Tecnológico de Hermosillo. PhD in Information Sciences from the Universidad Autónoma de Sinaloa. She is a Full-Time Research Professor at the University of Sonora and a member of the National System of Researchers in the candidate category. She has worked in the area of Occupational Health, mainly in Ergonomics and Anthropometry, as well as in Machine Learning and Ergonomic Risk Level Prediction.

Dora Aydee Rodríguez Vega, Universidad Politécnica de Sinaloa

Dora Aydee Rodríguez Vega has a degree in Engineering, a Master of Science in Electronic Engineering with a specialisation in Image Processing and a PhD in Information Sciences. She has worked as a Full-Time Professor in the Mechatronics Engineering programme at the Polytechnic University of Sinaloa since 2005. She has a desirable PRODEP profile since 2007, is a member of the Sinaloan System of Researchers and Technologists in the Researcher category and a member of the National System of Researchers in the candidate category. Her line of research is the imitation of movements in humanoid robots, which includes the capture and interpretation of movements and their reproduction in humanoid robots.

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Publicado

2025-01-30

Como Citar

Rodriguez Vega, G., & Rodríguez Vega, D. A. (2025). Machine learning for grasping recognition using wearable sensors. European Public & Social Innovation Review, 10, 1–18. https://doi.org/10.31637/epsir-2025-1205

Edição

Vol. 10 (2025)

Secção

Investigación e Inteligencia Artificial

Licença

Direitos de Autor (c) 2025 Graciela Rodriguez Vega, Dora Aydee Rodríguez Vega

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