A puzzle piece epidemic

The architectural design of a center for autistic children is discussed. This center has features for the parents and families of the autistic children. It accommodates the needs for diagnosis, treatments and therapies. There will also be training facilities to teach parents and other family members

Multimodal Data Analysis of Dyadic Interactions for an Automated Feedback System Supporting Parent Implementation of Pivotal Response Treatment

abstract: Parents fulfill a pivotal role in early childhood development of social and communication skills. In children with autism, the development of these skills can be delayed. Applied behavioral analysis (ABA) techniques have been created to aid in skill acquisition. Among these, pivotal response treatment (PRT) has been empirically shown to foster improvements. Research into PRT implementation has also shown that parents can be trained to be effective interventionists for their children. The current difficulty in PRT training is how to disseminate training to parents who need it, and how to support and motivate practitioners after training. Evaluation of the parents’ fidelity to implementation is often undertaken using video probes that depict the dyadic interaction occurring between the parent and the child during PRT sessions. These videos are time consuming for clinicians to process, and often result in only minimal feedback for the parents. Current trends in technology could be utilized to alleviate the manual cost of extracting data from the videos, affording greater opportunities for providing clinician created feedback as well as automated assessments. The naturalistic context of the video probes along with the dependence on ubiquitous recording devices creates a difficult scenario for classification tasks. The domain of the PRT video probes can be expected to have high levels of both aleatory and epistemic uncertainty. Addressing these challenges requires examination of the multimodal data along with implementation and evaluation of classification algorithms. This is explored through the use of a new dataset of PRT videos. The relationship between the parent and the clinician is important. The clinician can provide support and help build self-efficacy in addition to providing knowledge and modeling of treatment procedures. Facilitating this relationship along with automated feedback not only provides the opportunity to present expert feedback to the parent, but also allows the clinician to aid in personalizing the classification models. By utilizing a human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the classification models by providing additional labeled samples. This will allow the system to improve classification and provides a person-centered approach to extracting multimodal data from PRT video probes.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Music - A Gateway to Reaching Developmental Processes of Children with Special Needs

The scientific and technological advances made in the discoveries of how the body physiologically responds to music have opened new possibilities for the development of therapeutic archetypes to actively channel specific aspects of music to assist in the learning processes of children with special needs. The resulting protocols actively engage the brain through the use of rhythmic entrainment and can have positive outcomes when actively treating issues related to motor, speech/language, and cognition. This paper will show the connection that the structural properties of music can create in a therapeutic environment. The historical perspective of the joining of music and cognition will be briefly discussed, followed by a description of the physiological effect of music on the body. An explanation of techniques and protocols showing how the structural properties of music actively engage the brain is discussed, in addition to specific behavioral techniques utilized for behavior modification

Individually controlled noise reducing devices to improve IEQ in classrooms of primary schools

It is well-known that the indoor environmental quality (IEQ) at schools affects the health, comfort and performance of school children. Considering the need for a more effective way to improve both the IEQ in primary school classrooms and children’s satisfaction, along with the positive potential of individual control, this thesis aimed to propose a new way - individual control - to improve the IEQ in classrooms of primary schools and to increase children’s satisfaction in the Netherlands. First the main IEQ problem in classrooms as well as IEQ perceptions and preferences of the school children were identified through literature and field studies. The outcome showed that noise was the main IEQ problem in classrooms of Dutch primary schools, children could be clustered in according to their IEQ perceptions and preferences, and the reported IEQ-improving actions of the teachers could not effectively improve the IEQ for each child. As a follow-up, lab studies were performed in the SenseLab to explore the effect of background sound on children’s sound perception and performance Together with the outcome of the field studies, results suggested that individual control is a better way to improve IEQ in classrooms. Therefore, to address the main problem – noise - in classrooms, an individually controlled noisereducing device was designed, prototyped and tested with school children in the SenseLab. The results obtained from the simulations, measurements, and children’s feedback on the prototype of the device, demonstrated the feasibility of such devices in classrooms at primary schools

Emotion-aware voice interfaces based on speech signal processing

Voice interfaces (VIs) will become increasingly widespread in current daily lives as AI techniques progress. VIs can be incorporated into smart devices like smartphones, as well as integrated into autos, home automation systems, computer operating systems, and home appliances, among other things. Current speech interfaces, however, are unaware of users’ emotional states and hence cannot support real communication. To overcome these limitations, it is necessary to implement emotional awareness in future VIs. This thesis focuses on how speech signal processing (SSP) and speech emotion recognition (SER) can enable VIs to gain emotional awareness. Following an explanation of what emotion is and how neural networks are implemented, this thesis presents the results of several user studies and surveys. Emotions are complicated, and they are typically characterized using category and dimensional models. They can be expressed verbally or nonverbally. Although existing voice interfaces are unaware of users’ emotional states and cannot support natural conversations, it is possible to perceive users’ emotions by speech based on SSP in future VIs. One section of this thesis, based on SSP, investigates mental restorative effects on humans and their measures from speech signals. SSP is less intrusive and more accessible than traditional measures such as attention scales or response tests, and it can provide a reliable assessment for attention and mental restoration. SSP can be implemented into future VIs and utilized in future HCI user research. The thesis then moves on to present a novel attention neural network based on sparse correlation features. The detection accuracy of emotions in the continuous speech was demonstrated in a user study utilizing recordings from a real classroom. In this section, a promising result will be shown. In SER research, it is unknown if existing emotion detection methods detect acted emotions or the genuine emotion of the speaker. Another section of this thesis is concerned with humans’ ability to act on their emotions. In a user study, participants were instructed to imitate five fundamental emotions. The results revealed that they struggled with this task; nevertheless, certain emotions were easier to replicate than others. A further study concern is how VIs should respond to users’ emotions if SER techniques are implemented in VIs and can recognize users’ emotions. The thesis includes research on ways for dealing with the emotions of users. In a user study, users were instructed to make sad, angry, and terrified VI avatars happy and were asked if they would like to be treated the same way if the situation were reversed. According to the results, the majority of participants tended to respond to these unpleasant emotions with neutral emotion, but there is a difference among genders in emotion selection. For a human-centered design approach, it is important to understand what the users’ preferences for future VIs are. In three distinct cultures, a questionnaire-based survey on users’ attitudes and preferences for emotion-aware VIs was conducted. It was discovered that there are almost no gender differences. Cluster analysis found that there are three fundamental user types that exist in all cultures: Enthusiasts, Pragmatists, and Sceptics. As a result, future VI development should consider diverse sorts of consumers. In conclusion, future VIs systems should be designed for various sorts of users as well as be able to detect the users’ disguised or actual emotions using SER and SSP technologies. Furthermore, many other applications, such as restorative effects assessments, can be included in the VIs system

Pan European Voice Conference - PEVOC 11

The Pan European VOice Conference (PEVOC) was born in 1995 and therefore in 2015 it celebrates the 20th anniversary of its establishment: an important milestone that clearly expresses the strength and interest of the scientific community for the topics of this conference. The most significant themes of PEVOC are singing pedagogy and art, but also occupational voice disorders, neurology, rehabilitation, image and video analysis. PEVOC takes place in different European cities every two years (www.pevoc.org). The PEVOC 11 conference includes a symposium of the Collegium Medicorum Theatri (www.comet collegium.com

Multisensory learning in adaptive interactive systems

The main purpose of my work is to investigate multisensory perceptual learning and sensory integration in the design and development of adaptive user interfaces for educational purposes. To this aim, starting from renewed understanding from neuroscience and cognitive science on multisensory perceptual learning and sensory integration, I developed a theoretical computational model for designing multimodal learning technologies that take into account these results. Main theoretical foundations of my research are multisensory perceptual learning theories and the research on sensory processing and integration, embodied cognition theories, computational models of non-verbal and emotion communication in full-body movement, and human-computer interaction models. Finally, a computational model was applied in two case studies, based on two EU ICT-H2020 Projects, "weDRAW" and "TELMI", on which I worked during the PhD