Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children's sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children's chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.
Objectiv e: This study is conducted on the differences between flat and curved displays with respect to location of focused points, posture and satisfaction as well as preferred tilt angles. Background:In order to avoid physical and eye fatigue caused by misplayed sitting posture, many studies have asserted that the display requires appropriate location, size and tilt angle as well as curvature. However, most studies have focused on the work environment and the results are varied in the extent.Method: Eye height data in sitting posture were collected from 30 participants. Participants selected the most comfortable viewing angle within the range from 0°t o 12° while watching videos for both curved and flat display. Then, physical and eye fatigue and overall satisfaction were subjectively evaluated. Lateral diagram describing viewing display condition was set and used to develop linear models for expecting the preferred tilt angle.Results: Due to sitting in the natural viewing posture rather than upright, the eye height is lowered to about 4.6 centimeters, on average, for both displays showing no significant differences. In contrast, preferred angles for the two displays are significantly different and this can be interpreted that curvature vary the points focused. Two linear models as functions of sitting eye height are developed to expect preferred tilt angle for each display. Based on the result of overall satisfaction evaluation, curved display is statistically better than flat display. Conclusion:The results show that flat and curved displays are significantly different expect for the viewing posture. However, reasons for preferring curved display are not accurately factorized and the linear models are limited in the experiment condition such as size of display, distance between display and viewer and other physical environmental factors. Further studies on curved displays under more various conditions are required. Application:This study can contribute to use of the curved display in various way.
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.
This study aims to investigate the effect of size of a hand and curvature of handheld touchscreen devices on comfort when unimanually using the devices. By rated subjectively and recording EMG, comfort was measured for the use of three mock-ups of the device with different curvatures; one had flat surface and the others had curvatures of 400R and 100R for each. During the experiment, tapping, typing and dragging tasks were performed and the participants evaluated comfort subjectively and objectively. A difference among curvatures was analyzed as well as a difference among participant groups classified by size of their preferred hand. The results indicated that curvature of the handheld touchscreen devices affected neither muscle activities nor subjective comfort level. Moreover, size of hand was found to affect comfort objectively measured, but not the one subjectively rated. Overall, this study suggests that comfort measured subjectively does not consistent with comfort measured by objective data. Also, users' hand size may be more critical factor than curvature of handheld touchscreen determining comfort of touch screen use.
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