01735nas a2200229   4500000000100000000000100001008004100002260001200043653003100055653002000086653001700106653001600123100001900139700002900158700002900187245006800216856007900284300001000363490000600373520111200379022001401491       2023                            d  c12/202310aArtificial Neural Networks10aComputer vision10aHand Gesture10aPoint Cloud1 aCésar Osimani1 aJuan Jesus Ojeda-Castelo1 aJose A. Piedra-Fernandez00aPoint Cloud Deep Learning Solution for Hand Gesture Recognition  uhttps://www.ijimai.org/journal/sites/default/files/2023-11/ijimai8_4_7.pdf  a78-870 v83 aIn the last couple of years, there has been an increasing need for Human-Computer Interaction (HCI) systems that do not require touching the devices to control them, such as ATMs, self service kiosks in airports, terminals in public offices, among others. The use of hand gestures offers a natural alternative to achieve control without touching the devices. This paper presents a solution that allows the recognition of hand gestures by analyzing three-dimensional landmarks using deep learning. These landmarks are extracted by using a model created with machine learning techniques from a single standard RGB camera in order to define the skeleton of the hand with 21 landmarks distributed as follows: one on the wrist and four on each finger. This study proposes a deep neural network that was trained with 9 gestures receiving as input the 21 points of the hand. One of the main contributions, that considerably improves the performance, is a first layer of normalization and transformation of the landmarks. In our experimental analysis, we reach an accuracy of 99.87% recognizing of 9 hand gestures.   a1989-1660