Communication with autistic people through wearable sensors and cloud technology

Kashanian, Hooman; Ajami, Nasrin Bagherzadeh; Deghati, Maryam

doi:10.1109/cfis.2017.8003672

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Specifically, some of the developments have been proven to increase the safety of older adults [100], improve the mental safety of chronic patients [101] and to enhance the quality of life for autistic children via accurately recognizing their emotions [102]. Healthcare applications also help to prevent habits that may lead to health problems in the future, such as overeating [103] and excessive drinking [104].…”

Section: Emotions As Part Of Aal In Healthcarementioning

confidence: 99%

Towards Truly Affective AAL Systems

Pudāne

Petroviča

Lavendelis

et al. 2019

Lecture Notes in Computer Science

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Affective computing is a growing field of artificial intelligence. It focuses on models and strategies for detecting, obtaining, and expressing various affective states, including emotions, moods, and personality related attributes. The techniques and models developed in affective computing are applicable to various affective contexts, including Ambient Assisted Living. One of the hypotheses for the origin of emotion is that the primary purpose was to regulate social interactions. Since one of the crucial characteristics of Ambient Assisted Living systems is supporting social contact, it is unthinkable to build such systems without considering emotions. Moreover, the emotional capacity needed for Ambient Assisted Living systems exceeds simple user emotion detection and showing emotion expressions of the system. In addition, emotion generation and emotion mapping on rational thinking and behavior of a system should be considered. The chapter discusses the need and requirements for these processes in the context of various application domains of Ambient Assisted Living, i.e., healthcare, mobility, education, and social interaction.

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Section: Emotions As Part Of Aal In Healthcarementioning

confidence: 99%

Towards Truly Affective AAL Systems

Pudāne

Petroviča

Lavendelis

et al. 2019

Lecture Notes in Computer Science

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“…For example, Albraikan et al [1] proposed a real-time mobile biofeedback system that used wearable sensors to depict five basic emotions and provided the user with emotional feedback. An internet of things (IoT) architecture based on smart wearable devices and cloud technology was introduced for emotional interactions with autistic people [2]. Thus, the quest for developing a methodology that uses social and physiological signals to dynamically sense and evaluate persons' mental states has undoubtedly attracted researchers from various fields.…”

Section: Introductionmentioning

confidence: 99%

Wearable Structured Mental-Sensing-Graph Measurement

Yang

Gao

Woo

et al. 2023

IEEE Trans. Instrum. Meas.

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The emotional assessment under internet of things (IoT) architecture can support researchers to establish the relationships between human social and physiological signals and emotions. In this paper, a wearable emotion sensing system is developed under narrow band internet of things (NB-IoT) wireless communication technology. The wearable sensing device integrates social linked sensors including voice, activity, and heart rate. Using this system, a dating experiment is set up to investigate multimodal factors of perception. In particular, the multimodal data are fused in a graph structure, and this further leads to a graph convolutional neural networks model for emotion evaluation and a mentalsensing-graph intelligent interpreter. Different types of mentalsensing-graphs are fused during the training stage, and the model achieves a verification accuracy of 0.93. The intrinsic relationships among the multimodal data have been captured by the subgraphs which have star-shaped structures, and the center of the subgraphs are mostly audio node. The obtained results show that the attractiveness perception of the male participants in dating is more aligned to language communication. The results also reveal that when the male participants date highly attractive women during the experiments, a significant correlation is observed between the multimodal features and the attractiveness perception levels.

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Research Investigation and Analysis on Behavioral Analytics, Neuro Imaging, and Pervasive Sensory Algorithms and Techniques for Autism Diagnosis

Sasikala

Sureshkumar

2019

Advances in Healthcare Information Systems and Administration

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Interdisciplinary research on autism diagnosis and training for the autistic children are current requirements. Behavioral analytics are the techniques followed by pediatricians for identifying ASD in children. However, the techniques successfully accepted by the pediatricians worldwide would be useful for the parents and pediatricians only for the elementary school-aged children, and the system is not helping for early diagnose among children of age 2. Machine learning, image processing, and pervasive techniques are trending for early diagnosis of ASD. Hence, the chapter has done a research-based survey and detailed analysis on behavioral analytics algorithms, neuro imaging, and eye tracking techniques for early diagnosis. The behavioral analytics survey mainly focuses on social and meta-cognitive skills, and the input dataset has been taken from parents, trainers, teachers, and peer groups. The chapter also identifies that computing technologies with neuro-imaging and pervasive techniques are better than the behavioral skill analysis for early diagnosis.

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Communication with autistic people through wearable sensors and cloud technology

Cited by 3 publications

References 16 publications

Towards Truly Affective AAL Systems

Towards Truly Affective AAL Systems

Wearable Structured Mental-Sensing-Graph Measurement

Research Investigation and Analysis on Behavioral Analytics, Neuro Imaging, and Pervasive Sensory Algorithms and Techniques for Autism Diagnosis

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