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JACIII Vol.27 No.4 pp. 655-663
doi: 10.20965/jaciii.2023.p0655
(2023)

Research Paper:

Application of Convolutional Neural Network to Gripping Comfort Evaluation Using Gripping Posture Image

Kazuki Hokari*1,† ORCID Icon, Makoto Ikarashi*2, Jonas A. Pramudita*3 ORCID Icon, Kazuya Okada*4, Masato Ito*4, and Yuji Tanabe*5

*1Department of Mechanical and Electrical Engineering, School of Engineering, Nippon Bunri University
1727 Ichigi, Oita, Oita 870-0397, Japan

Corresponding author

*2Graduate School of Science and Technology, Niigata University
8050 Ikarashi 2-no-cho, Nishi-ku, Niigata, Niigata 950-2181, Japan

*3Department of Mechanical Engineering, College of Engineering, Nihon University
1 Nakagawara, Tokusada, Tamuramachi, Koriyama, Fukushima 963-8642, Japan

*4Product Analysis Center, Panasonic Holdings Corporation
1048 Kadoma, Kadoma, Osaka 571-8686, Japan

*5Management Strategy Section, President Office, Niigata University
8050 Ikarashi 2-no-cho, Nishi-ku, Niigata-shi, Niigata 950-2181, Japan

Received:
December 7, 2022
Accepted:
April 3, 2023
Published:
July 20, 2023
Keywords:
gripping comfort, gripping posture image, CNN
Abstract

Gripping comfort evaluation was crucial for designing a product with good gripping comfort. In this study, a novel evaluation method using gripping posture image was constructed based on convolutional neural network (CNN). Human subject experiment was conducted to acquire gripping comfort scores and gripping posture images while gripping seven objects with simple shape and eleven manufactured products. The scores and the images were used as training set and validation set for CNN. Classification problem was employed to classify gripping posture images as comfort or discomfort. As a result, accuracies were 91.4% for simple shape objects and 76.2% for manufactured products. Regression problem was utilized to predict gripping comfort scores from gripping posture images while gripping cylindrical object. Gripping posture images of radial and dorsal sides in direction of hand were used to investigate effect of direction of hand on prediction accuracy. Consequently, mean absolute errors (MAE) of gripping comfort scores were 0.132 for radial side and 0.157 for dorsal side in direction of hand. In both problems, the results indicated that these evaluation methods were useful to evaluate gripping comfort. The evaluation methods help designers to evaluate products and enhance gripping comfort.

Gripping comfort evaluation based on CNN

Gripping comfort evaluation based on CNN

Cite this article as:
K. Hokari, M. Ikarashi, J. Pramudita, K. Okada, M. Ito, and Y. Tanabe, “Application of Convolutional Neural Network to Gripping Comfort Evaluation Using Gripping Posture Image,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.4, pp. 655-663, 2023.
Data files:
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