A hybrid approach to product prototype usability testing based on surface EMG images and convolutional neural network classification

A multidimensional sensory evaluation model to investigate the (dis)comfort of body parts in a supine sitting position during a lunch break

“…Supine sitting is a relaxed and languid sitting position whereby one reclines backwards in a chair to rest. The experiment's prototype is based on a previous literature study [ 19 ], which demonstrated that people experience the most physical comfort when resting in a chair in a supine sitting position, by maintaining a neutral position, which is the most comfortable position and minimizes the range of muscle movement [ 13 , 15 , 18 , 21 ]. The prototype is a three-part, strip-shaped pillow that wraps around and supports the neck, underarms, waist, chest and belly like a scarf ( Fig.…”

“…Currently, the types of body parts in the perception questionnaire are determined by the study's needs, and there is no agreement or standard for the scale. The body mapping diagram, proposed by Fu et al [ 19 ] for the supine sitting posture was used in this study, with 10 body parts, including six above the chest area and four from the waist to the chest area.…”

“…In their study of car seat morphology, Wen and Yang [ 9 ] collected and processed blood pressure distribution data, using a comfort questionnaire and pressure measurement system. Pressure, vibration, posture, muscle pressure, computer-aided design (CAD), computer-aided engineering (CAE) and convolutional neural networks (CNN) are also objective research methods [ [15] , [16] , [17] , [18] , [19] ].…”

“…However, current comfort scales only provide simple degree judgements [ 19 , 20 ], whereas advanced CNN techniques can only categorize and identify, neither of which is sufficient to capture the complexity and diversity of user perceptions or to provide a broad concept for new research. They are insufficient in embodying the complexities of user perceptions and providing a diverse range of creative concepts for new designs.…”

Exercise fatigue diagnosis method based on short-time Fourier transform and convolutional neural network

“…Cui Juan et al ( Wang, 2022 ) used comprehensive health monitoring technology to diagnose exercise fatigue in aerobics, and obtained an accuracy of 85% in the fatigue diagnosis of this exercise. You-Lei et al ( Fu et al, 2022 ) used deep learning technology to identify the features of surface electromyography, which was used for the fatigue level of sitting office. Abid et al ( Minhas et al, 2022 ) collected the driver’s facial image and assessed the driver’s fatigue level to prevent the occurrence of traffic accidents.…”