Ti3D-contact, a high-resolution and whole-body dataset of hand-object contact area based on 3D scanning method

This work compiles a high-resolution and whole-body human hand contact dataset named as Ti3D-contact. First, participantswear cotton textile gloves to grasp and manipulate different types and sizes of objects, onto which high-adhesion paint is sprayed to paint whole-body hand contact areas on the gloves. Then, the painted gloves are scannedbya 3D scanner to capture the original hand contact data in the form of texture images and 3D models. After extracting the painted areas on the obtained 3D models in the form of point cloud, the processed hand contact data recording the contact areas between hands and objects is further obtained. A coordinate conversion method is then employed byunifying the coordinates of the processed hand contact data. Through unifying the coordinate systems, the consistency of all the hand contact data is improved,which benefits the analyses of hand operation patterns. Furthermore, a method to calculate the Euclidean distance between the adjacent points in the hand contact data is applied to obtain the resolution of the dataset, demonstrating that the high average resolution for the hand contact dataset is 0.72mm. Then, the processed hand contact data is vowelized to calculate its overlap, presenting the similarity among the 3D models for repetitive actions as 90% for verifying the reliability of the hand contact data. Finally, a classifier based on a convolutional neural network (CNN) is proposed for the classification of hand movements with 95% precision using the acquired hand contact data. This shows a potential application of the hand contact data in real-world task scenarios.Ti3D-contactcomprises1872 interactions conducted by 12 healthy adults, with 10 standardized objects, recording the entire contact areas between hands and objects with a high-resolution of 0.72 mm. A diverse array of 52 prescribed grasping and manipulation actions have been completed, including the 33 most commonly used conventional grasping gestures summarized by the Feix team and the 9 basic manipulation gestures proposed by the Elliott team. Additionally, we introduce 10 new common manipulation gestures based on the classification method pioneered by the Bullock team, enhancing the realism of task execution scenarios in daily life.