IoT Device for Sitting Posture Classification Using Artificial Neural Networks
| Autor: | Isabel Beasley-Bohórquez, Juan Manuel Montes-Sanchez, Alberto Vazquez-Baeza, José L. Sevillano-Ramos, Lourdes Duran-Lopez, Francisco Luna-Perejon |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Sevilla. TEP108 : Robotica y Tecnología de Computadores |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
TK7800-8360
Computer Networks and Communications Computer science Population Posture detection Sitting 01 natural sciences Machine Learning 03 medical and health sciences 0302 clinical medicine Pain prevention posture detection 030212 general & internal medicine Electrical and Electronic Engineering education Simulation pain prevention IoT device education.field_of_study Artificial neural network Artificial neural networks business.industry 010401 analytical chemistry Work (physics) Process (computing) Sitting posture Workload 0104 chemical sciences Hardware and Architecture Control and Systems Engineering Signal Processing Electronics Internet of Things business artificial neural networks |
| Zdroj: | Electronics, Vol 10, Iss 1825, p 1825 (2021) Electronics Volume 10 Issue 15 idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| ISSN: | 2079-9292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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