Sign language recognition using wearable electronics: Implementing K-nearest neighbors with dynamic time warping and convolutional neural network algorithms

We propose a sign language recognition system based on wearable electronics and two different classification algorithms. The wearable electronics were made of a sensory glove and inertial measurement units to gather fingers, wrist, and arm/forearm movements. The classifiers were k-Nearest Neighbors with Dynamic Time Warping (that is a non-parametric method) and Convolutional Neural Networks (that is a parametric method). Ten sign-words were considered from the Italian Sign Language: cose, grazie, maestra, together with words with international meaning such as google, internet, jogging, pizza, television, twitter, and ciao. The signs were repeated one-hundred times each by seven people, five male and two females, aged 29–54 y ± 10.34 (SD). The adopted classifiers performed with an accuracy of 96.6% ± 3.4 (SD) for the k-Nearest Neighbors plus the Dynamic Time Warping and of 98.0% ± 2.0 (SD) for the Convolutional Neural Networks. Our system was made of wearable electronics among the most complete ones, and the classifiers top performed in comparison with other relevant works reported in the literature

Toward Effective Physical Human-Robot Interaction

With the fast advancement of technology, in recent years, robotics technology has significantly matured and produced robots that are able to operate in unstructured environments such as domestic environments, offices, hospitals and other human-inhabited locations. In this context, the interaction and cooperation between humans and robots has become an important and challenging aspect of robot development. Among the various kinds of possible interactions, in this Ph.D. thesis I am particularly interested in physical human-robot interaction (pHRI). In order to study how a robot can successfully engage in physical interaction with people and which factors are crucial during this kind of interaction, I investigated how humans and robots can hand over objects to each other. To study this specific interactive task I developed two robotic prototypes and conducted human-robot user studies. Although various aspects of human-robot handovers have been deeply investigated in the state of the art, during my studies I focused on three issues that have been rarely investigated so far: Human presence and motion analysis during the interaction in order to infer non-verbal communication cues and to synchronize the robot actions with the human motion; Development and evaluation of human-aware pro-active robot behaviors that enable robots to behave actively in the proximity of the human body in order to negotiate the handover location and to perform the transfer of the object; Consideration of objects grasp affordances during the handover in order to make the interaction more comfortable for the human

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning