Designing of smart chair for monitoring of sitting posture using convolutional neural networks

Abstract

Purpose Sitting in a chair is a typical act of modern people. Prolonged sitting and sitting with improper postures can lead to musculoskeletal disorders. Thus, there is a need for a sitting posture classification monitoring system that can predict a sitting posture. The purpose of this paper is to develop a system for classifying children’s sitting postures for the formation of correct postural habits. Design/methodology/approach For the data analysis, a pressure sensor of film type was installed on the seat of the chair, and image data of the postu.re were collected. A total of 26 children participated in the experiment and collected image data for a total of seven postures. The authors used convolutional neural networks (CNN) algorithm consisting of seven layers. In addition, to compare the accuracy of classification, artificial neural networks (ANN) technique, one of the machine learning techniques, was used. Findings The CNN algorithm was used for the sitting position classification and the average accuracy obtained by tenfold cross validation was 97.5 percent. The authors confirmed that classification accuracy through CNN algorithm is superior to conventional machine learning algorithms such as ANN and DNN. Through this study, we confirmed the applicability of the CNN-based algorithm that can be applied to the smart chair to support the correct posture in children. Originality/value This study successfully performed the posture classification of children using CNN technique, which has not been used in related studies. In addition, by focusing on children, we have expanded the scope of the related research area and expected to contribute to the early postural habits of children.

Ergonomics workstation environment toward organisational competitiveness

“…When sitting down, an individual should make sure that the hips maintain an 'S' shape according to the spine curvature. This position can prevent the pressure on the cartilage and the main function of the hip [33]. Chairs in the context of this study relate to workers' perceptions on ergonomic chairs and the fact that it can influence an individual's work performance by avoiding fatigue and tension.…”

“…Studies have shown that there is a relationship between work that requires 95% of sitting time and the pain in the neck and shoulders [32]. An ergonomic chair design not only enables workers to complete their tasks but also helps to expedite the execution of these tasks [33]. When sitting down, an individual should make sure that the hips maintain an 'S' shape according to the spine curvature.…”

Instrumented Office Chair With Low-Cost Plastic Optical Fiber Sensors for Posture Control and Work Conditions Optimization

“…Most of the available technologies to monitor posture are based on pressure maps obtained by the incorporation of electronic sensors at different points of the chair [5][6][7][8][9][10]. Nevertheless, electronic sensors exhibit limitations, one of the biggest being the use of electrical signals at the point of measurement, which makes them susceptible to catastrophic failure if in contact with liquids (in case of an accident or even cleaning), increases the risk of short circuit and/or electric fire, and even user safety.…”

Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image

“…At present, there are three main ways of sitting posture recognition, which are based on machine vision [ 2 , 3 , 4 , 5 ], wearable motion sensors [ 6 , 7 , 8 , 9 ] and external pressure sensors [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Although machine vision technology has achieved great success in the field of posture recognition [ 26 ], it is difficult to work normally in situations with many obstacles.…”

“…Ahmad et al [ 13 ] used a 4 × 4 matrix pressure sensor to classify four types of sitting postures by decision tree algorithm, obtaining about 80% experimental classification accuracy. Kim et al [ 24 ] used an 8 × 8 matrix pressure sensor and convolutional neural network algorithm to classify five types of sitting postures, obtaining 95.3% experimental classification accuracy. The above sitting posture recognition algorithms did not predict the position of the hip and extracted the pressure features according to the position of the hip, but directly selected the pressure value of the fixed position of the seat as features for sitting posture recognition.…”