RT Journal Article
T1 Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning
A1 Pastor-Martín, Francisco
A1 Lin-Yang, Da-hui
A1 Gómez-de-Gabriel, Jesús Manuel
A1 García-Cerezo, Alfonso José
K1 Autómatas
AB There are physical Human–Robot Interaction (pHRI) applications where the robot has to grab the human body, such as rescue or assistive robotics. Being able to precisely estimate the grasping location when grabbing a human limb is crucial to perform a safe manipulation of the human. Computer vision methods provide pre-grasp information with strong constraints imposed by the field environments. Force-based compliant control, after grasping, limits the amount of applied strength. On the other hand, valuable tactile and proprioceptive information can be obtained from the pHRI gripper, which can be used to better know the features of the human and the contact state between the human and the robot. This paper presents a novel dataset of tactile and kinesthetic data obtained from a robot gripper that grabs a human forearm. The dataset is collected with a three-fingered gripper with two underactuated fingers and a fixed finger with a high-resolution tactile sensor. A palpation procedure is performed to record the shape of the forearm and to recognize the bones and muscles in different sections. Moreover, an application for the use of the database is included. In particular, a fusion approach is used to estimate the actual grasped forearm section using both kinesthetic and tactile information on a regression deep-learning neural network. First, tactile and kinesthetic data are trained separately with Long Short-Term Memory (LSTM) neural networks, considering the data are sequential. Then, the outputs are fed to a Fusion neural network to enhance the estimation. The experiments conducted show good results in training both sources separately, with superior performance when the fusion approach is considered.
PB MDPI
YR 2022
FD 2022-11-12
LK https://hdl.handle.net/10630/25906
UL https://hdl.handle.net/10630/25906
LA eng
NO Pastor, F.; Lin-Yang, D.-h.; Gómez-de-Gabriel, J.M.; García-Cerezo, A.J. Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning. Sensors 2022, 22, 8752. https://doi.org/10.3390/s22228752
NO This research was funded by the University of Málaga, the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, grant number RTI2018-093421-B-I00 and the European Commission, grant number BES-2016-078237. Partial funding for open access charge: Universidad de Málaga
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026