Search

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. 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. 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. In terms of technical implementations and satisfaction analysis, results found acceptable with above average score. Proposed 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. [ABSTRACT FROM AUTHOR]

Purpose: To define semi-supervised machine learning (SSML) and explore current and potential applications of this analytic strategy in rehabilitation research. Method: We conducted a scoping review using PubMed, GoogleScholar and Medline. Studies were included if they: (1) described a semi-supervised approach to apply machine learning algorithms during data analysis and (2) examined constructs encompassed by the International Classification of Functioning, Disability and Health (ICF). The first two authors reviewed identified articles and recorded study and participant characteristics. The ICF domain used in each study was also identified. Results: After combining information from the eight studies, we established that SSML was a feasible approach for analysis of complex data in rehabilitation research. We also determined that semi-supervised approaches may be more accurate than supervised machine learning approaches. Conclusions: A semi-supervised approach to machine learning has potential to enhance our understanding of complex data sets in rehabilitation science. SSML mirrors the iterative process of rehabilitation, making this approach ideal for calibrating devices, classifying activities or identifying just-in-time interventions. Rehabilitation scientists who are interested in conducting SSML should collaborate with data scientists to advance the application of this approach within our field. Semi-supervised machine learning applications may be a feasible approach for analyses of complex data sets in rehabilitation research. Semi-supervised machine learning approaches uses a combination of labelled and unlabelled data to produce accurate predictive models, thereby requiring less user-input data than other machine learning approaches (i.e., supervised, unsupervised), reducing resource cost and user-burden. Semi-supervised machine learning is an iterative process that, when applied to rehabilitation assessment and outcomes, could produce accurate personalized models for treatment. Rehabilitation researchers and data scientists should collaborate to implement semi-supervised machine learning approaches in rehabilitation research, optimizing the power of large datasets that are becoming more readily available within the field (e.g., EEG signals, sensors, smarthomes). [ABSTRACT FROM AUTHOR]

Purpose: To assess the feasibility and acceptability of the Howz smart home system for stroke survivors. Materials: Howz uses smart home technology to monitor users' activity in the home and machine learning algorithms to detect when their activity changes. This information made available to the user and a named contact (usually a family member) via the Howz app. If activity changes, an alert is then sent to the user and the named contact. Methods: Howz was installed in the homes of 19 stroke survivors who lived alone. The sensors installed; the number and nature of alerts; adverse events; any technical difficulties and how Howz was used were recorded. We also interviewed participants and their named contacts about their views and experiences of using Howz eight weeks after installation. Results: Installation was effective; no technological problems or adverse events were reported. All participants chose alerts regarding "getting up" and activity during the day and at night. There was a mean of 1.1 (sd 1.2) alerts/person. Participants found the system unobtrusive and were positive about it. They found it easy to use and would recommend it to others with stroke. The most frequently reported benefit was that Howz gave peace of mind for the users and/or the family/named contact that help would be at hand if needed. Conclusions: The Howz system was feasible and acceptable for stroke survivors. It gave users and their family an enhanced sense of security and peace of mind that help would be at hand if needed. Although most stroke survivors are elderly and often suffer physical, visual and cognitive disabilities which are often considered as barriers to using smart home technology, this project has demonstrated the well-developed smart home technology is feasible and acceptable. Users were able and willing to engage with the technology (to varying degrees) and had no concerns about invasion of privacy. The main benefit of the Howz system is an enhanced sense of security for the user and their family that help is at hand if needed. [ABSTRACT FROM AUTHOR]