A Motion Capture System for Hand Movement Recognition

Graciela Rodríguez-Vega*, Dora Aydee Rodríguez-Vega, Xiomara Penelope Zaldívar-Colado, Ulises Zaldívar-Colado, Rafael Castillo-Ortega

*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)


One of the most frequently-used body regions in daily activities is the upper limbs, and many of the work-related musculoskeletal disorders occur in this area, mainly the hands. We highlight the importance of studying hand movements executed at work, and how they affect workers’ health and productivity. Data were collected from a hand-motion capture system conformed by six inertial measurement units and six resistive force sensors from hand and fingers movements. Two common hand movements were analyzed using wrist flexion-extension with a small (−15° to 15°) and medium (<−15° and >15°) range of motion and flexion-extension movement with the hand pronated-supinated. Data were classified by traditional methods. A more complex movement involving a 3-finger spherical grip was also recorded. It was found that the lectures from the six inertial sensors and the six force resistive sensors showed a pattern that facilitates the recognition of basic and more complex movements (flexion-extension and spheric handgrip) through visual analysis of the plotted data, even at different ranges of motion.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 21st Congress of the International Ergonomics Association, IEA 2021
Subtítulo de la publicación alojadaMethods and Approaches
EditoresNancy L. Black, W. Patrick Neumann, Ian Noy
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas8
ISBN (versión impresa)9783030746131
EstadoPublicada - 2022
Evento21st Congress of the International Ergonomics Association, IEA 2021 - Virtual, Online
Duración: 13 jun. 202118 jun. 2021

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen223 LNNS
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389


Conferencia21st Congress of the International Ergonomics Association, IEA 2021
CiudadVirtual, Online

Nota bibliográfica

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.


Profundice en los temas de investigación de 'A Motion Capture System for Hand Movement Recognition'. En conjunto forman una huella única.

Citar esto