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

IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-07-20 DOI:10.20965/jaciii.2023.p0655
Kazuki Hokari, Makoto Ikarashi, J. A. Pramudita, Kazuya Okada, Masato Ito, Yuji Tanabe
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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.
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卷积神经网络在夹持姿态图像夹持舒适性评价中的应用
抓握舒适性评价是设计抓握舒适性好的产品的关键。在本研究中,基于卷积神经网络(CNN)构建了一种新的基于抓握姿势图像的评估方法。通过人体被试实验,获得7种形状简单的物体和11种制成品的抓握舒适度评分和抓握姿势图像。将得分和图像作为CNN的训练集和验证集。采用分类问题对抓握姿势图像进行舒适和不舒适的分类。结果,简单形状物体的精度为91.4%,制成品的精度为76.2%。利用回归问题预测抓取圆柱形物体时抓取姿态图像的抓取舒适度得分。采用手方向的桡侧和背侧抓握姿态图像,研究手方向对预测精度的影响。结果表明,手握舒适性评分的平均绝对误差(MAE)为0.132(桡侧)和0.157(背侧)。在这两个问题中,结果表明这些评价方法对评价抓握舒适性是有用的。该评价方法有助于设计者对产品进行评价,增强抓握舒适性。
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来源期刊
CiteScore
1.50
自引率
14.30%
发文量
89
期刊介绍: JACIII focuses on advanced computational intelligence and intelligent informatics. The topics include, but are not limited to; Fuzzy logic, Fuzzy control, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Adaptation and Learning Systems, Distributed Intelligent Systems, Network systems, Multi-media, Human interface, Biologically inspired evolutionary systems, Artificial life, Chaos, Complex systems, Fractals, Robotics, Medical applications, Pattern recognition, Virtual reality, Wavelet analysis, Scientific applications, Industrial applications, and Artistic applications.
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