782 research outputs found

    On the Recognition of Emotion from Physiological Data

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    This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

    Recognition of Emotion from Speech: A Review

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    Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

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    Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported

    Evaluation and Fault Classification for Service Robot during Sit- to-Stand Movement through Center of Mass

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    Many service robots have been developed to assist patients with sit-to-stand movement (STS). However, little research has focused on users’ negative psychological changes during the STS movement when assisted by a robot. The STS movement accompanied with a negative psychological change is defined as a fault. The main purpose of this study was to propose a method of conveying faults to a service robot through the center of mass (CoM). Experiments on the STS movement were executed five times with 10 healthy subjects under four conditions: two self-performed STSs with seat heights of 43 and 62 cm, and two robot-assisted STSs with a seat height of 43 cm and end-effector speeds of 2 and 5 s. Time series data on the CoM were measured with high-speed camera system. A classifier was designed according to the data on the CoM in the frequency domain. The results showed that the proposed classifier had a high probability of discriminating fault classes from others. Then, the vertical ground reaction force (vGRF) under the same experimental conditions was used to cross-check the experimental results. It was concluded that faults in the assistance of service robots can be detected from the CoP-related items

    EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.

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    Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs

    Xylo-Bot: A Therapeutic Robot-Based Music Platform for Children with Autism

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    Children with Autism Spectrum Disorder (ASD) experience deficits in verbal and nonverbal communication skills, including motor control, emotional facial expressions, and eye gaze / joint attention. This Ph.D. dissertation focuses on studying the feasibility and effectiveness of using a social robot, called NAO, and a toy music instrument, xylophone, at modeling and improving the social responses and behaviors of children with ASD. In our investigation, we designed an autonomous social interactive music teaching system to fulfill this mission. A novel modular robot-music teaching system consisting of three modules is presented. Module 1 provides an autonomous self-awareness positioning system for the robot to localize the instrument and make a micro adjustment for the arm joints to play the note bars properly. Module 2 allows the robot to be able to play any customized song per user’s request. This design provides an opportunity to translate songs into C-major or a-minor with a set of hexadecimal numbers without music experience. After the music score converted robot should be able to play it immediately. Module 3 is designed for providing real-life music teaching experience for the users. Two key features of this module are a) music detection and b) smart scoring and feedback . Short-time Fourier transform and Levenshtein distance are adapted to fulfill the design requirements, which allow the robot to understand music and provide a proper dosage of practice and oral feedback to users. A new instrument has designed to present better emotions from music due to the limitation of the original xylophone. This new programmable xylophone can provide a more extensive frequency range of notes, easily switch between the Major and Minor keys, extensively easy to control, and have fun with it as an advanced music instrument. Because our initial intention has been to study emotion in children with autism, an automated method for emotion classification in children using electrodermal activity (EDA) signals. The time-frequency analysis of the acquired raw EDAs provides a feature space based on which different emotions can be recognized. To this end, the complex Morlet (C-Morlet) wavelet function is applied to the recorded EDA signals. The dataset used in this research includes a set of multimodal recordings of social and communicative behavior as well as EDA recordings of 100 children younger than 30 months old. The dataset is annotated by two experts to extract the time sequence corresponding to three primary emotions, including “Joy”, “Boredom”, and “Acceptance”. Various experiments are conducted on the annotated EDA signals to classify emotions using a support vector machine (SVM) classifier. The quantitative results show that emotion classification performance remarkably improves compared to other methods when the proposed wavelet-based features are used. By using this emotion classification, emotion engagement during sessions, and feelings between different music can be detected after data analysis. NAO music education platform will be thought-about as a decent tool to facilitate improving fine motor control, turn-taking skills, and social activities engagement. Most of the ASD youngsters began to develop the strike movement within the two initial intervention sessions; some even mastered the motor ability throughout the early events. More than half of the subjects could dominate proper turn-taking after few sessions. Music teaching is a good example for accomplishing social skill tasks by taking advantage of customized songs selected by individuals. According to researcher and video annotator, majority of the subjects showed high level of engagement for all music game activities, especially with the free play mode. Based on the conversation and music performance with NAO, subjects showed strong interest in challenging the robot with a friendly way

    Recent Developments in Smart Healthcare

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    Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

    Human-Centric Machine Vision

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    Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans
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