IoT Device for Sitting Posture Classification Using Artificial Neural Networks

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, wh...

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Bibliographic Details
Published inElectronics (Basel) Vol. 10; no. 15; p. 1825
Main Authors Luna-Perejón, Francisco, Montes-Sánchez, Juan Manuel, Durán-López, Lourdes, Vazquez-Baeza, Alberto, Beasley-Bohórquez, Isabel, Sevillano-Ramos, José L.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2021
Subjects
Accuracy
Algorithms
Artificial neural networks
Back pain
Classification
Damage
Datasets
Deep learning
Employment
Evaluation
GDP
Gross Domestic Product
Internet of Things
Machine learning
Neural networks
Pain
Population
Posture
Resistors
Sensors
Workload
Switzerland
Spain
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics10151825