Vocal-friend: internet of social-things framework to aid verbal communication.

PURPOSE: Deficits in social verbal communication in individuals with Social Communication Disorder (SCD) is of concern and SCD in the human community is prevalent in large population throughout the globe. Deficits in verbal social communication are prevalent in a large population. This paper aimed to propose internet connected multi-system architecture which is capable to support verbal communication in a social environment for individuals with social communication deficits. MATERIAL AND METHODS: Implementation methodology was included with corpus collection for specific communication, deep learning based machine training for intelligent communication, and implementation of the trained algorithm on internet connected electronic multiple social communication devices. The implemented system is smart enough to initiate and maintain two types of communication; the first type includes communication between multiple individuals on the remote location and the second type includes communication with the individual present in the physical listening range. RESULTS: The system was investigated in terms of its algorithmic parameters and found 97% to 100% in terms of training and testing accuracy with negligible mean squared error. Vocal-Friend analysed results based on audio-bot simulative conditions provide more than 91% accuracy, interaction rate and fallback rate. On the basis of the satisfaction analysis, above average results were noticed. CONCLUSION: In terms of technical implementations and satisfaction analysis, results found acceptable with above average score.IMPLICATION FOR REHABILITATIONProposed framework is easy to use by caregivers with even having little knowledge.Support individual with deficit to learn social verbal communication skill to survive in society.Aiding parents, caregivers and professionals to understand the communication needs of individuals with communication deficits.Since technology is also grooming in the domain of rehabilitation, so this system could be used in various future applications such as social robots, social virtual assistants etc.

Deep Neural Network-based Handheld Diagnosis System for Autism Spectrum Disorder.

OBJECTIVE: The aim of the present work was to propose and implement deep neural network (DNN)-based handheld diagnosis system for more accurate diagnosis and severity assessment of individuals with autism spectrum disorder (ASD). METHODS: Initially, the learning of the proposed system for ASD diagnosis was performed by implementing DNN algorithms such as a convolutional neural network (CNN) and long short-term memory (LSTM), and multilayer perceptron (MLP) with DSM-V based acquired dataset. The performance of the DNN algorithms was analyzed based on parameters viz. accuracy, loss, mean squared error (MSE), precision, recall, and area under the curve (AUC) during the training and validation process. Later, the optimum DNN algorithm, among the tested algorithms, was implemented on handheld diagnosis system (HDS) and the performance of HDS was analyzed. The stability of proposed DNN-based HDS was validated with the dataset group of 20 ASD and 20 typically developed (TD) individuals. RESULTS: It was observed during comparative analysis that LSTM resulted better in ASD diagnosis as compared to other artificial intelligence (AI) algorithms such as CNN and MLP since LSTM showed stabilized results achieving maximum accuracy in less consumption of epochs with minimum MSE and loss. Further, the LSTM based proposed HDS for ASD achieved optimum results with 100% accuracy in reference to DSM-V, which was validated statistically using a group of ASD and TD individuals. CONCLUSION: The use of advanced AI algorithms could play an important role in the diagnosis of ASD in today's era. Since the proposed LSTM based HDS for ASD and determination of its severity provided accurate results with maximum accuracy with reference to DSM-V criteria, the proposed HDS could be the best alternative to the manual diagnosis system for diagnosis of ASD.

IoT based assistive companion for hypersensitive individuals (ACHI) with autism spectrum disorder.

OBJECTIVE: Today, most of the individuals with Autism Spectrum Disorders (ASD) have atypical sensory behaviors. The main aim of this study is to propose an assistive intervention for supporting the overloaded sensory responses in hypersensitive individuals with ASD. METHODS: The vision, auditory, smell, and physical balance related multi-sensors based hardware prototype, namely Assistive Companion for Hypersensitive Individuals (ACHI) has been designed for individuals with ASD. The proposed ACHI prototype is an assistive-technology based companion for hypersensitive individuals with ASD which is able to 'fetch/detect the sensory information using electronic sensors', 'making the decision using fuzzy logic on the basis of fetched sensory information' and then, 'transmit the generated information over the internet through the Internet of Things (IoT)', and also able for 'generating alerts to caregivers'. The proposed design is also capable of providing audio & video feedback to calm down individuals with ASD. RESULTS: After testing, it is observed that 93% percent of the caregivers rated the proposed ACHI intervention on the scale of above average. The remarkable reduction in hyperactive states related triggering incidents in ASD has been found with the use of ACHI. CONCLUSION: The present work and the proposed prototype can identify and control the sensory overload triggers in ASD and it can guide the caregiver or clinicians to optimize the responsible surrounding causes of explosive behavior in ASD and would help the individuals with ASD to become calm.