ILAT (Software as a Service): Interactive Learning Application Tool for Autism Screening and Assessment in children with Autism Spectrum Disorder

Autism is a type of neurological disorder usually noticeable during the early stage of childhood, especially between one to three years and occurs in all social groups. The common problem experienced by the autism subjects includes lack in social interaction, poor communication skill, overexcited, unable to express their emotions. While these disorders are not fully curable, early detection can reduce the severity with proper therapy. Even though there are no appropriate medications and treatments, still we can improve the lifestyle of the autism subject through various supportive therapies. If this disorder is not detected at early stages, the severity rate may probably increase during the later stage.  Developing countries like India witness 0.2 percentage of the autism population in the overall community based on the information provided by the Rehabilitation Council of India. Express growth in the Information and Communication Technologies allows developing various assistive tools to enhance the lifestyle of the autism people. Fourth Generation Technologies like the Internet of Things, Wearable Devices, Cloud Computing, Big Data Analytics, and Artificial Intelligence, Mobile devices, Location-aware technology, Sensors, Augmented and Virtual Reality together provide a smart solution to all the sufferers. The objective of Interactive Learning Application Tool is used for Autism Screening and Assessment in children with Autism Spectrum Disorder and extended to explore the assistive technologies available to serve the community. This will enhance the social interaction, learning and communication skills in children, a tool for analysing the aggressive level, a tool for caregivers and supportive and ranking tool for psychiatrist dealing with autism subject

Smart technologies and beyond: exploring how a smart band can assist in monitoring children’s independent mobility & well-being

The problem which is being investigated through this thesis is not having a device(s) or method(s) which are appropriate for monitoring a child’s vital and tracking a child’s location. This aspect is being explored by other researchers which are yet to find a viable solution. This work focuses on providing a solution that would consider using the Internet of Things for measuring and improving children’s health. Additionally, the focus of this research is on the use of technology for health and the needs of parents who are concerned about their child’s physical health and well-being. This work also provides an insight into how technology is used during the pandemic. This thesis will be based on a mixture of quantitative and qualitative research, which will have been used to review the following areas covering key aspects and focuses of this study which are (i) Children’s Independent Mobility (ii) Physical activity for children (iii) Emotions of a child (iv) Smart Technologies and (v) Children’s smart wearables. This will allow a review of the problem in detail and how technology can help the health sector, especially for children. The deliverable of this study is to recommend a suitable smart band device that enables location tracking of the child, activity tracking as well as monitoring the health and wellbeing of the child. The research also includes an element of practical research in the form of (i) Surveys, the use of smart technology and a perspective on the solution from parents. (ii) Focus group, in the form of a survey allowing opinions and collection of information on the child and what the parents think of smart technology and how it could potentially help with their fears. (iii) Observation, which allows the collection of data from children who were given six activities to conduct while wearing the Fitbit Charge HR. The information gained from these elements will help provide guidelines for a proposed solution. In this thesis, there are three frameworks which are about (i) Research process for this study (ii) Key Performance Indicators (KPIs) which are findings from the literature review and (iii) Proposed framework for the solution, all three combined frameworks can help health professionals and many parents who want an efficient and reliable device, also deployment of technologies used in the health industry for children in support of independent mobility. Current frameworks have some considerations within the technology and medical field but were not up to date with the latest elements such as parents fears within today’s world and the advanced features of technology

Apps and wearables for monitoring physical activity and sedentary behaviour: a qualitative systematic review protocol on barriers and facilitators

International audienceObjective: Monitoring of physical activity and sedentary behaviours by mobile phone applications (apps) and wearable technology (wearables) may improve these health behaviours. This systematic review aims to synthesise the qualitative literature on the barriers and facilitators of using apps and wearables for monitoring physical activity and/or sedentary behaviour in adults.Methods: This review protocol is registered in PROSPERO (CRD42017070194). Scientific databases including CINAHL Complete, MEDLINE, PsycINFO, SPORTDiscus, Cochrane Library and Scopus will be searched for relevant studies published from 1 January 2012 to the date the searches are conducted. Studies will be included if they incorporated adults who used an app or wearable for monitoring physical activity and/or sedentary behaviour; explored the barriers and/or facilitators of using an app and/or wearable; and were published in English. Following duplicate screening of titles and abstracts, full texts of potentially eligible papers will be screened to identify studies using qualitative approaches to explore barriers and facilitators of using apps and/or wearables for monitoring physical activity and/or sedentary behaviour. Discrepancies will be resolved through consensus or by consulting a third screener. Relevant excerpts (quotes and text) from the included papers will be extracted and analysed thematically. The Critical Appraisal Skills Programme Qualitative Research Checklist will be used to appraise included studies.Conclusion: The results of this work will be useful for those intending to monitor physical activity and/or sedentary behaviour using these technologies

Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap with arXiv:1703.0352

The Internet of Hackable Things

The Internet of Things makes possible to connect each everyday object to the Internet, making computing pervasive like never before. From a security and privacy perspective, this tsunami of connectivity represents a disaster, which makes each object remotely hackable. We claim that, in order to tackle this issue, we need to address a new challenge in security: education

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

A Review of Physical Human Activity Recognition Chain Using Sensors

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, encouraging healthy behaviours, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical cases/conditions of elderly and patients is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

Using the Onitor® Track for weight loss : A mixed methods study among overweight and obese women

Funding Information: The authors thank the participants for their engagement with and significant contributions to this study. They also thank Cloudtag? for providing Onitor? Track test units, technical support (for the test unit and app) and comments on this paper. The author(s) received no financial support for the research, authorship and/or publication of this article. Publisher Copyright: © The Author(s) 2019. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Peer reviewedPublisher PD