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.
Highlights
The social-economic progress experienced globally in developed and developing countries has implied numerous changes in several areas
Advances in automation and the technological revolution that computing has brought about have decreased the number of jobs that involve high or medium physical activity in favor of a greater number of office or monitoring jobs: sedentary occupations that involve the use of a video display terminal (VDT) [1]
As the sampling frequency established in the acquisition process was relatively high, spikes may appear in the sample collection of some of the sensors’ output, which could add noise to the dataset
Summary
The social-economic progress experienced globally in developed and developing countries has implied numerous changes in several areas. New leisure activities that require the use of electronic devices while sitting are on the rise, contributing to even more sedentary lifestyles than before. All these behavioral changes have increased the time that the population spends sitting [2]. According to the Spanish Society of Rheumatology (SER), 80% of the Spanish population has suffered back pain at least once and the remaining 20% has acquired chronic pain [7] These kinds of injuries are directly and indirectly associated with various diseases and they imply the third leading cause of self-perceived disability in the world [8]. In countries such as Spain and Switzerland, low-back and neck pain have become the leading causes of disability [9,10]
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