A hybrid approach to product prototype usability testing based on surface EMG images and convolutional neural network classification. (June 2022)

• Record Type: Journal Article
• Authors: Fu, You-Lei; Liang, Kuei-Chia; Song, Wu; Huang, Jianlong
• Is Part Of: Computer methods and programs in biomedicine. Volume 221(2022)
• Publication Date: 2022-06
• Subjects: sEMG -- Convolutional neural network -- Supine sitting posture -- Health informatics -- Comfort; sEMG surface electromyography -- MF median frequency -- MPF mean power frequency -- CNN convolutional neural networks -- SCM sternocleidomastoid muscle -- BN batch normalization -- CG control group -- EG experimental group -- VGG visual geometry group -- Relu rectified Linear Unit -- FC fully connected -- c¯ mean comfort level; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106870
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This study propose a real-time sEMG combined with a multi-stream CNN framework. Construction of prototype usability experiments for perceptual evaluation and sEMG measurements. Using sEMG measurements of Sternocleidomastoid to measure muscle fatigue. The CNN model proposed in this paper was able to classify the four sEMG datasets accurately with an accuracy of 0.99. The CNN classification method based on sEMG images is valid for the study of supine body comfort. Abstract: Objective: It is common for employees to complain of muscle fatigue when resting in a reclined position in an office chair. To investigate the physical factors that influence resting comfort in a supine position, a newly designed product was used as the basis for creating a prototype experiment and testing its efficacy in use. Subjective questionnaires were combined with surface EMG measurements and deep learning algorithms were used to identify body part comfort to create a hybrid approach to product usability testing. Methods: To facilitate the use of sEMG-based CNNs in human factors engineering, a subjective user assessment was first conducted using a combination of body mapping and an impact comfort scale to the screen which body parts have a significant impact effect on comfort when using the prototype. A control group (no used) and an experimental group (used) were then created and the body parts with the most significant effects were measured using sEMG methods. After pre-processing the sEMG signal, sMEG feature maps were obtained by mean power frequency (MPF) and linear regression was used to analyze the comforting effect. Finally, a CNN model is constructed and the sMEG feature maps are trained and tested. Results: The results of the experiment showed that the user's subjective assessment showed that 10 body parts had a significant effect on comfort, with the right and left sides of the neck having the highest effect on comfort (4.78). sEMG measurements were then performed on the sternocleidomastoid (SCM) of the left and right neck. Linear analysis of the measurements showed that the control group had higher SCM fatigue than the experimental group, which could also indicate that the experimental group had better comfort. The final CNN model was able to accurately classify the four datasets with an accuracy of 0.99. Conclusion: The results of the study show that the method is effective for the study of physical comfort in the supine sitting position and that it can be used to validate the comfort of similar products and to design iterations of the prototype.