Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

Immersive teaching using virtual reality technology to improve ophthalmic surgical skills for medical postgraduate students

Medical education is primarily based on practical schooling and the accumulation of experience and skills, which is important for the growth and development of young ophthalmic surgeons. However, present learning and refresher methods are constrained by several factors. Nevertheless, virtual reality (VR) technology has considerably contributed to medical training worldwide, providing convenient and practical auxiliary value for the selection of students' sub-majors. Moreover, it offers previously inaccessible surgical step training, scenario simulations, and immersive evaluation exams. This paper outlines the current applications of VR immersive teaching methods for ophthalmic surgery interns.

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Purpose

Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies.

Methods

The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms.

Originality

This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014-2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse.

Findings

The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).

Visible Light Communications-Based Assistance System for the Blind and Visually Impaired: Design, Implementation, and Intensive Experimental Evaluation in a Real-Life Situation

Severe visual impairment and blindness significantly affect a person's quality of life, leading sometimes to social anxiety. Nevertheless, instead of concentrating on a person's inability, we could focus on their capacities and on their other senses, which in many cases are more developed. On the other hand, the technical evolution that we are witnessing is able to provide practical means that can reduce the effects that blindness and severe visual impairment have on a person's life. In this context, this article proposes a novel wearable solution that has the potential to significantly improve blind person's quality of life by providing personal assistance with the help of Visible Light Communications (VLC) technology. To prevent the wearable device from drawing attention and to not further emphasize the user's deficiency, the prototype has been integrated into a smart backpack that has multiple functions, from localization to obstacle detection. To demonstrate the viability of the concept, the prototype has been evaluated in a complex scenario where it is used to receive the location of a certain object and to safely travel towards it. The experimental results have: i. confirmed the prototype's ability to receive data at a Bit-Error Rate (BER) lower than 10-7; ii. established the prototype's ability to provide support for a 3 m radius around a standard 65 × 65 cm luminaire; iii. demonstrated the concept's compatibility with light dimming in the 1-99% interval while maintaining the low BER; and, most importantly, iv. proved that the use of the concept can enable a person to obtain information and guidance, enabling safer and faster way of traveling to a certain unknown location. As far as we know, this work is the first one to report the implementation and the experimental evaluation of such a concept.