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Computational method for monitoring pauses exercises in office workers through a vision model

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Abstract

A sedentary routine at work can cause various muscular, skeletal or visual diseases, however these can be prevented with what is known as active pauses. This article is intended to illustrate how software can help reduce the risk of occupational disease due to the sedentary lifestyle of an office job, for this purpose a web application was developed under the SCRUM methodology, which makes use of the TensorFlow, Flask PoseNet model and python, for an active pause control application which is a proven practice of reducing the type of diseases already mentioned. With these tools it was possible to develop an algorithm capable of comparing two human figures; which serves to compare whether the user of the program is performing or not correctly performing the active pause exercise, with an average error squared on the order of 10−32. Finally, The application can keep track of the figure and exercises performed by the user just by using the user personal webcam and the comparison algorithm developed, leaving behind the use of tools such as Kinect.

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References

  • Chikhaoui B, Ye B, Mihailidis A (2016) Feature-level combination of skeleton joints and body parts for accurate aggressive and agitated behavior recognition. J Ambient Intell Humaniz Comput 8(6):957–976. https://doi.org/10.1007/s12652-016-0415-y

    Article  Google Scholar 

  • Deb S, Sharan A, Chaturvedi S, Arun A, Gupta A (2019) Interactive dance lessons through human body pose estimation and skeletal topographies matching. Int J Comput Intell IoT 2(4). Retrieved from SSRN: https://ssrn.com/abstract=3361142

  • Díaz X, Mardones M, Mena C, Rebolledo A, Castillo M (2011) Pausa activa como factor de cambio en actividad física en funcionarios públicos. Revista Cubana de Salud Pública 37(3):303–313

    Google Scholar 

  • Elaoud A, Barhoumi W, Zagrouba E, Agrebi B (2019) Skeleton-based comparison of throwing motion for handball players. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01301-6

    Article  Google Scholar 

  • Hou Y, Yao H, Li H, Sun X (2017) Dancing like a superstar: action guidance based on pose estimation and conditional pose alignment. In: 2017 IEEE international conference on image processing (ICIP), Beijing, pp 1312–1316. https://doi.org/10.1109/icip.2017.8296494

  • Januario LB, de Moreira FCR, Cid MM, Samani A, Madeleine P, Oliveira AB (2016) Effects of active pause pattern of surface electromyographic activity among subjects performing monotonous tasks: a systematic review. J Electromyogr Kinesiol 30:196–208. https://doi.org/10.1016/j.jelekin.2016.07.009

    Article  Google Scholar 

  • Jutinico CJM, Montenegro-Marin CE, Burgos D, Gonzalez R (2018) Natural language interface model for the evaluation of ergonomic routines in occupational health (ILENA). Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-0770-y

    Article  Google Scholar 

  • Kayembe M (2012) Human Posture Recognition and Good Posture Recommendation. (Tesis de maestría en Ciencias de la Computación), universidad de Nairobi, Nairobi

  • Kolda L, Krejcar O, Selamat A, Kuca K, Fadeyi O (2019) Multi-biometric system based on cutting-edge equipment for experimental contactless verification. Sensors 19:3709

    Article  Google Scholar 

  • Kumar A, Kumar A, Kumar Singh S, Kala R (2016) Human activity recognition in real-times environments using skeleton joints. Int J Interact Multimed Artif Intell 3(7):61. https://doi.org/10.9781/ijimai.2016.379

    Article  Google Scholar 

  • Liu Y, Xu Y, Li S (2018) 2-D human pose estimation from images based on deep learning: a review. In: 2018 2nd IEEE advanced information management, communicates, electronic and automation control conference (IMCEC), Xi’an, pp 462–465. https://doi.org/10.1109/imcec.2018.8469573

  • Martinez J, Manuel J (2018) Sistema de Visión Artificial para la Detección y Corrección de Posturas en Ejercicios realizados por Fisicoculturistas. (Tesis para Ingenieros en computación), Departamento de Ingeniería, Universidad autónoma del estado de México, Toluca

  • Oliphant TE (2006) A guide to NumPy (vol 1). Trelgol Publishing, New York

    Google Scholar 

  • Owen N, Sugiyama T, Eakin EE, Gardiner PA, Tremblay MS, Sallis JF (2011) Adults’ sedentary behavior. Am J Prev Med 41(2):189–196. https://doi.org/10.1016/j.amepre.2011.05.013

    Article  Google Scholar 

  • Rodríguez C, Dorado R (2015) ¿Por qué implementar Scrum? Revista Ontare 3(1):125–144. https://doi.org/10.21158/23823399.v3.n1.2015.1253

    Article  Google Scholar 

  • Saez Y, Baldominos A, Isasi P (2017) A comparison study of classifier algorithms for cross-person physical activity recognition. Sensors 17:66

    Article  Google Scholar 

  • Saleem N, Khattak M (2020) Deep neural networks for speech enhancement in complex-noisy environments. Int J Interact Multimed Artif Intell 6(1):84–90. https://doi.org/10.9781/ijimai.2019.06.001

  • St-Onge N, Samani A, Madeleine P (2017) Integration of active pauses and pattern of muscular activity during computer work. Ergonomics 60(9):1228–1239. https://doi.org/10.1080/00140139.2017.1303086

    Article  Google Scholar 

  • Sudin M, Abdullah S, Nasudin M (2019) Humanoid localization on robocup field using corner intersection and geometric distance estimation. Int J Interact Multimed Artif Intell IP 1:12. https://doi.org/10.9781/ijimai.2019.04.001

    Article  Google Scholar 

  • Uribe-Quevedo A, Perez-Gutierrez B (2013) Interactive pose estimation for active pauses. In: Stephanidis C (ed) HCI international 2013—Posters’ extended abstracts. HCI 2013. Communications in Computer and Information Science, vol 373. Springer, Berlin

    Google Scholar 

  • Wang Y, Cao H, Jiang X, Tang Y (2019) Recognition of dorsal hand vein based bit planes and block mutual information. Sensors 19:3718

    Article  Google Scholar 

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Correspondence to Rubén González Crespo.

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Herrera, F., Niño, R., Montenegro-Marín, C.E. et al. Computational method for monitoring pauses exercises in office workers through a vision model. J Ambient Intell Human Comput 12, 3389–3397 (2021). https://doi.org/10.1007/s12652-020-02391-3

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  • DOI: https://doi.org/10.1007/s12652-020-02391-3

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