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

You Lei Fu, Kuei Chia Liang, Wu Song*, Jianlong Huang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Article number106870
JournalComputer Methods and Programs in Biomedicine
Publication statusPublished - 2022 Jun


  • Comfort
  • Convolutional neural network
  • Health informatics
  • Supine sitting posture
  • sEMG

ASJC Scopus subject areas

  • Software
  • Health Informatics
  • Computer Science Applications


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