dc.creator | Luna Perejón, Francisco | es |
dc.creator | Montes-Sánchez, Juan Manuel | es |
dc.creator | Durán López, Lourdes | es |
dc.creator | Vázquez Baeza, Alberto | es |
dc.creator | Beasley Bohórquez, Isabel | es |
dc.creator | Sevillano Ramos, José Luis | es |
dc.date.accessioned | 2021-10-14T11:34:59Z | |
dc.date.available | 2021-10-14T11:34:59Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Luna Perejón, F., Montes-Sánchez, J.M., Durán López, L., Vázquez Baeza, A., Beasley Bohórquez, I. y Sevillano Ramos, J.L. (2021). IoT Device for Sitting Posture Classification Using Artificial Neural Networks. Electronics, 10 (15) | |
dc.identifier.issn | 2079-9292 | es |
dc.identifier.uri | https://hdl.handle.net/11441/126584 | |
dc.description.abstract | Nowadays, the percentage of time that the population spends sitting has increased substantially
due to the use of computers as the main tool for work or leisure and the increase in jobs with a
high office workload. As a consequence, it is common to suffer musculoskeletal pain, mainly in the
back, which can lead to both temporary and chronic damage. This pain is related to holding a posture
during a prolonged period of sitting, usually in front of a computer. This work presents a IoT posture
monitoring system while sitting. The system consists of a device equipped with Force Sensitive
Resistors (FSR) that, placed on a chair seat, detects the points where the user exerts pressure when
sitting. The system is complemented with a Machine Learning model based on Artificial Neural
Networks, which was trained to recognize the neutral correct posture as well as the six most frequent
postures that involve risk of damage to the locomotor system. In this study, data was collected
from 12 participants for each of the seven positions considered, using the developed sensing device.
Several neural network models were trained and evaluated in order to improve the classification
effectiveness. Hold-Out technique was used to guide the training and evaluation process. The results
achieved a mean accuracy of 81% by means of a model consisting of two hidden layers of 128 neurons
each. These results demonstrate that is feasible to distinguish different sitting postures using few
sensors allocated in the surface of a seat, which implies lower costs and less complexity of the system. | es |
dc.format | application/pdf | es |
dc.format.extent | 15 | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Electronics, 10 (15) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Machine Learning | es |
dc.subject | IoT device | es |
dc.subject | Posture detection | es |
dc.subject | Pain prevention | es |
dc.subject | Artificial neural networks | es |
dc.title | IoT Device for Sitting Posture Classification Using Artificial Neural Networks | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.publisherversion | https://www.mdpi.com/2079-9292/10/15/1825 | es |
dc.identifier.doi | 10.3390/electronics10151825 | es |
dc.contributor.group | Universidad de Sevilla. TEP108 : Robotica y Tecnología de Computadores | es |
dc.journaltitle | Electronics | es |
dc.publication.volumen | 10 | es |
dc.publication.issue | 15 | es |