Detecting, locating and recognising human touches in social robots with contact microphones

There are many situations in our daily life where touch gestures during natural human–human interaction take place: meeting people (shaking hands), personal relationships (caresses), moments of celebration or sadness (hugs), etc. Considering that robots are expected to form part of our daily life in the future, they should be endowed with the capacity of recognising these touch gestures and the part of its body that has been touched since the gesture’s meaning may differ. Therefore, this work presents a learning system for both purposes: detect and recognise the type of touch gesture (stroke, tickle, tap and slap) and its localisation. The interpretation of the meaning of the gesture is out of the scope of this paper. Different technologies have been applied to perceive touch by a social robot, commonly using a large number of sensors. Instead, our approach uses 3 contact microphones installed inside some parts of the robot. The audio signals generated when the user touches the robot are sensed by the contact microphones and processed using Machine Learning techniques. We acquired information from sensors installed in two social robots, Maggie and Mini (both developed by the RoboticsLab at the Carlos III University of Madrid), and a real-time version of the whole system has been deployed in the robot Mini. The system allows the robot to sense if it has been touched or not, to recognise the kind of touch gesture, and its approximate location. The main advantage of using contact microphones as touch sensors is that by using just one, it is possible to “cover” a whole solid part of the robot. Besides, the sensors are unaffected by ambient noises, such as human voice, TV, music etc. Nevertheless, the fact of using several contact microphones makes possible that a touch gesture is detected by all of them, and each may recognise a different gesture at the same time. The results show that this system is robust against this phenomenon. Moreover, the accuracy obtained for both robots is about 86%.The research leading to these results has received funding from the projects: ‘‘Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES)’’, funded by the Spanish "Ministerio de Ciencia, Innovación y Universidades, Spain" and from RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by ‘"Programas de Actividades I+D en la Comunidad de Madrid’" and cofunded by Structural Funds of the EU, Slovak Republic.Publicad

Sign Language Recognition for Static and Dynamic Gestures

Humans are called social animals, which makes communication a very important part of humans. Humans use shoes and non-verbal forms of language for communication purposes, but not all humans can give oral speech. Hearing impaired and mute people. Sign language became consequently advanced for them and nevertheless impairs communication. Therefore, this paper proposes a system that uses streams to use CNN networks for the classification of alphabets and numbers. Alphabet and number gestures are static gestures in Indian sign language, and CNN is used because it provides very good results for image classification. Use hand-masked (skin segmented) images for model training. For dynamic hand gestures, the system uses the LSTM network for classification tasks. LSTMs are known for their accurate prediction of time zone distributed data. This paper presents different types of hand gestures, namely two models for static and dynamic prediction, CNN and LSTM

Automatic recognition of Arabic alphabets sign language using deep learning

Technological advancements are helping people with special needs overcome many communications’ obstacles. Deep learning and computer vision models are innovative leaps nowadays in facilitating unprecedented tasks in human interactions. The Arabic language is always a rich research area. In this paper, different deep learning models were applied to test the accuracy and efficiency obtained in automatic Arabic sign language recognition. In this paper, we provide a novel framework for the automatic detection of Arabic sign language, based on transfer learning applied on popular deep learning models for image processing. Specifically, by training AlexNet, VGGNet and GoogleNet/Inception models, along with testing the efficiency of shallow learning approaches based on support vector machine (SVM) and nearest neighbors algorithms as baselines. As a result, we propose a novel approach for the automatic recognition of Arabic alphabets in sign language based on VGGNet architecture which outperformed the other trained models. The proposed model is set to present promising results in recognizing Arabic sign language with an accuracy score of 97%. The suggested models are tested against a recent fully-labeled dataset of Arabic sign language images. The dataset contains 54,049 images, which is considered the first large and comprehensive real dataset of Arabic sign language to the furthest we know

Detecting Surface Interactions via a Wearable Microphone to Improve Augmented Reality Text Entry

This thesis investigates whether we can detect and distinguish between surface interaction events such as tapping or swiping using a wearable mic from a surface. Also, what are the advantages of new text entry methods such as tapping with two fingers simultaneously to enter capital letters and punctuation? For this purpose, we conducted a remote study to collect audio and video of three different ways people might interact with a surface. We also built a CNN classifier to detect taps. Our results show that we can detect and distinguish between surface interaction events such as tap or swipe via a wearable mic on the user\u27s head