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

According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.N/

Inertial Measurement Unit-Based Gait Event Detection in Healthy and Neurological Cohorts: A Walk in the Dark

A deep learning (DL)-based network is developed to determine gait events from IMU data from a shank- or foot-worn device. The DL network takes as input the raw IMU data and predicts for each time step the probability that it corresponds to an initial or final contact. The algorithm is validated for walking at different self-selected speeds across multiple neurological diseases and both in clinical research settings and the habitual environment. The algorithms shows a high detection rate for initial and final contacts, and a small time error when compared to reference events obtained with an optical motion capture system or pressure insoles. Based on the excellent performance, it is concluded that the DL algorithm is well suited for continuous long-term monitoring of gait in the habitual environment

Connected healthcare: Improving patient care using digital health technologies

Now more than ever, traditional healthcare models are being overhauled with digital technologies of Healthcare 4.0 being increasingly adopted. Worldwide, digital devices are improving every stage of the patient care pathway. For one, sensors are being used to monitor patient metrics 24/7, permitting swift diagnosis and interventions. At the treatment stage, 3D printers are currently being investigated for the concept of personalised medicine by allowing patients access to on-demand, customisable therapeutics. Robots are also being explored for treatment, by empowering precision surgery or targeted drug delivery. Within medical logistics, drones are being leveraged to deliver critical treatments to remote areas, collect samples, and even provide emergency aid. To enable seamless integration within healthcare, the Internet of Things technology is being exploited to form closed-loop systems that remotely communicate with one another. This review outlines the most promising healthcare technologies and devices, their strengths, drawbacks, and scopes for clinical adoption

Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design

Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

Egocentric Vision-based Action Recognition: A survey

[EN] The egocentric action recognition EAR field has recently increased its popularity due to the affordable and lightweight wearable cameras available nowadays such as GoPro and similars. Therefore, the amount of egocentric data generated has increased, triggering the interest in the understanding of egocentric videos. More specifically, the recognition of actions in egocentric videos has gained popularity due to the challenge that it poses: the wild movement of the camera and the lack of context make it hard to recognise actions with a performance similar to that of third-person vision solutions. This has ignited the research interest on the field and, nowadays, many public datasets and competitions can be found in both the machine learning and the computer vision communities. In this survey, we aim to analyse the literature on egocentric vision methods and algorithms. For that, we propose a taxonomy to divide the literature into various categories with subcategories, contributing a more fine-grained classification of the available methods. We also provide a review of the zero-shot approaches used by the EAR community, a methodology that could help to transfer EAR algorithms to real-world applications. Finally, we summarise the datasets used by researchers in the literature.We gratefully acknowledge the support of the Basque Govern-ment's Department of Education for the predoctoral funding of the first author. This work has been supported by the Spanish Government under the FuturAAL-Context project (RTI2018-101045-B-C21) and by the Basque Government under the Deustek project (IT-1078-16-D)