No abstract

In present study, human subject experiment including sensory evaluation was conducted to investigate gripping comfort during gripping spherical and columnar objects and to identify important factors that affect gripping comfort score. Contact pressure measurement using a pressure sensor sheet and fingers posture measurement using a motion capture system during each gripping task were then carried out to investigate the individual differences in gripping style and to reveal the differences in fingers posture according to the shapes of the test objects. As a result, significant differences in the gripping comfort score between male and female could not be found in all test objects. Additionally, the coefficients of determination of the relationship between the gripping comfort score and hand length and between the gripping comfort score and hand breadth were considered low in all test objects. Furthermore, a hierarchical cluster analysis of contact pressure distribution demonstrated that two gripping styles during gripping a cylinder existed in present study. Joint angles around x-axes of PIP joints of the middle finger and the ring fingers, around y-axis of CM joint of the thumb and around y-axes of MCP joints of the five fingers varied according to the gripping styles. Moreover, significant differences in 11 joint angles among test objects were confirmed based on the analysis result of fingers posture. This result indicates that fingers posture during gripping changes with the change of the shape of the test object. The results of present study provide information regarding important factors that should be considered or may not be considered during the evaluation and improvement of the gripping comfort of a manufactured product.

No 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.