Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).The automatic emotion recognition domain brings new methods and technologies that might be used to enhance therapy of children with autism. The paper aims at the exploration of methods and tools used to recognize emotions in children. It presents a literature review study that was performed using a systematic approach and PRISMA methodology for reporting quantitative and qualitative results. Diverse observation channels and modalities are used in the analyzed studies, including facial expressions, prosody of speech, and physiological signals. Regarding representation models, the basic emotions are the most frequently recognized, especially happiness, fear, and sadness. Both single-channel and multichannel approaches are applied, with a preference for the first one. For multimodal recognition, early fusion was the most frequently applied. SVM and neural networks were the most popular for building classifiers. Qualitative analysis revealed important clues on participant group construction and the most common combinations of modalities and methods. All channels are reported to be prone to some disturbance, and as a result, information on a specific symptoms of emotions might be temporarily or permanently unavailable. The challenges of proper stimuli, labelling methods, and the creation of open datasets were also identified.Peer reviewedFinal Published versio

A physiological signal database of children with different special needs for stress recognition

This study presents a new dataset AKTIVES for evaluating the methods for stress detection and game reaction using physiological signals. We collected data from 25 children with obstetric brachial plexus injury, dyslexia, and intellectual disabilities, and typically developed children during game therapy. A wristband was used to record physiological data (blood volume pulse (BVP), electrodermal activity (EDA), and skin temperature (ST)). Furthermore, the facial expressions of children were recorded. Three experts watched the children's videos, and physiological data is labeled "Stress/No Stress" and "Reaction/No Reaction", according to the videos. The technical validation supported high-quality signals and showed consistency between the experts.Scientific and Technological Research Council of Turkey Technology and Innovation Funding Programmes Directorat

Development of a cognitive robotic system for simple surgical tasks

The introduction of robotic surgery within the operating rooms has significantly improved the quality of many surgical procedures. Recently, the research on medical robotic systems focused on increasing the level of autonomy in order to give them the possibility to carry out simple surgical actions autonomously. This paper reports on the development of technologies for introducing automation within the surgical workflow. The results have been obtained during the ongoing FP7 European funded project Intelligent Surgical Robotics (I-SUR). The main goal of the project is to demonstrate that autonomous robotic surgical systems can carry out simple surgical tasks effectively and without major intervention by surgeons. To fulfil this goal, we have developed innovative solutions (both in terms of technologies and algorithms) for the following aspects: fabrication of soft organ models starting from CT images, surgical planning and execution of movement of robot arms in contact with a deformable environment, designing a surgical interface minimizing the cognitive load of the surgeon supervising the actions, intra-operative sensing and reasoning to detect normal transitions and unexpected events. All these technologies have been integrated using a component-based software architecture to control a novel robot designed to perform the surgical actions under study. In this work we provide an overview of our system and report on preliminary results of the automatic execution of needle insertion for the cryoablation of kidney tumours

Near-constant beamwidth quadruple bandwidth double-ridged horn antenna design

Barkana, Duygun Erol/0000-0002-8929-0459WOS: 000490490300022It is well known that the beamwidths of horn antennas are inversely proportional to frequency. Previous studies point to minimum +/- 12% beamwidth stability within a maximum of 2.5:1 bandwidth (BW) ratio. To extend this BW, a design methodology for broadband double-ridged horn antenna (DRHA) is proposed and applied to 4.5-18 GHz frequency band. Firstly, a conventional DRHA is designed and studied to compare the beamwidth variation of wideband horn antennas. By explicitly addressing the shortcomings of conventional DRHA, DRHA is redesigned and near-constant beamwidth DRHA is proposed using the modifications implemented on the sidewalls. the antenna utilises ridges to extend the frequency band, unlike other wideband constant beamwidth horns. the authors show that ridged horns, whose dimensions are appropriately designed and modified, can have stable beamwidths. Then, properly positioned curved pinwalls are designed to provide considerable improvement in H-plane beamwidth constancy. Broadband double-ridged waveguide-to-coaxial adapter is also designed for 50 omega reference. the prototype is manufactured, and the measured antenna exhibits wideband characteristics with 30.9 degrees +/- 2.7 degrees half power beamwidth in H-plane over 4:1 BW ratio. This variation corresponds to +/- 8.7% beamwidth stability along with the target frequency band

A Comparative Analysis of Deep Learning Methods for Emotion Recognition using Physiological Signals for Robot-Based Intervention Studies

Robot-based intervention systems can be more pros-perous if they recognize the patients' and children's emotions and modify the interaction scenarios considering these emotions to increase their engagement. This study aims to develop an emotion recognition model that uses users' physiological signals, which is planned to be integrated into robot-based intervention studies. Deep learning methods can be used to develop emotion recognition models using physiological signals, which can be collected from different biofeedback sensors. In this study, a comparative analysis of deep learning methods for emotion recog-nition using physiological signals for robot-based intervention studies are performed. Convolutional Neural networks (CNN) and Long Short-Term Memory (LSTM) networks are employed to analyze and classify the physiological data (blood volume pulse (BVP), skin temperature (ST), and skin conductance (SC)). Furthermore, the effects of different hyperparameters (filter size, number of filters, and dropout) on the classification performance of the emotion recognition models are analyzed. It has been found that the best performance among the proposed models is the hybrid model CLS (hybrid network with Support Vector Machine (SVM) classifier), where the filter size is 3X3 with the BVP sensor (59.18%) in the pleasant-unpleasant (PU) classification. Furthermore, the hybrid model Dec-CLS contains 3 convolutional layers with 32, 16, and 8 3X3 filters, respectively, result in the highest performance (67.46%) with the BVP sensor in NU (neutral- unpleasant) classification

Finger exoskeleton for treatment of tendon injuries

A finger exoskeleton has been developed to aid treatment of tendon injuries. The exoskeleton is designed to assist flexion/extension motions of a finger within its full range, in a natural and coordinated manner, while keeping the tendon tension within acceptable limits to avoid gap formation or rupture of the suture. In addition to offering robot assisted operation modes for tendon therapies, the exoskeleton can provide quantitative measures of recovery that can help guide the physical therapy program. Usability studies have been conducted and efficacy of exoskeleton driven exercises to reduce muscle requitement levels has been demonstrated