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This paper aims to explore and review the potential of robotic rehabilitation as a treatment approach for Alzheimer’s disease (AD) and its impact on the health and quality of life of AD patients.

The present discourse endeavors to provide a comprehensive overview of extant scholarly inquiries that have examined the salience of inhibitory mechanisms vis-à-vis robotic interventions and their impact on patients with AD. Specifically, this review aims to explicate the contemporary state of affairs in this realm by furnishing a detailed explication of ongoing research endeavors. With the objective of elucidating the significance of inhibitory processes in robotic therapies for individuals with AD, this analysis offers a critical appraisal of extant literature that probes the intersection of cognitive mechanisms and assistive technologies. Through a meticulous analysis of diverse scholarly contributions, this review advances a nuanced understanding of the intricate interplay between inhibitory processes and robotic interventions in the context of AD.

According to the review papers, it appears that implementing robot-assisted rehabilitation can serve as a pragmatic and effective solution for enhancing the well-being and overall quality of life of patients and families engaged with AD. Besides, this new feature in the robotic area is anticipated to have a critical role in the success of this innovative approach.

Due to the nascent nature of this cutting-edge technology and the constrained configuration of the mechanized entity in question, further protracted analysis is imperative to ascertain the advantages and drawbacks of robotic rehabilitation vis-à-vis individuals afflicted with Alzheimer’s ailment.

The potential for robots to serve as indispensable assets in the provision of care for individuals afflicted with AD is significant; however, their efficacy and appropriateness for utilization by caregivers of AD patients must be subjected to further rigorous scrutiny.

This paper reviews the current robotic method and compares the current state of the art for the AD patient.

This study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques.

Autonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in various ways. Early anticipation of a particular action would give a prior benefit to the driver to successfully handle the dangers on the road. In this paper, the advancements that have taken place in the use of multi-modal machine learning for assistive driving systems are surveyed. The aim is to help elucidate the recent progress and techniques in the field while also identifying the scope for further research and improvement. The authors take an overview of context-aware driver assistance systems that alert drivers in case of maneuvers by taking advantage of multi-modal human processing to better safety and drivability.

There has been a huge improvement and investment in ADAS being a key concept for road safety. In such applications, data is processed and information is extracted from multiple data sources, thus requiring training of machine learning algorithms in a multi-modal style. The domain is fast gaining traction owing to its applications across multiple disciplines with crucial gains.

The research is focused on deep learning and computer vision-based techniques to generate a context for assistive driving and it would definitely adopt by the ADAS manufacturers.

As context-aware assistive driving would work in real-time and it would save the lives of many drivers, pedestrians.

This paper provides an understanding of context-aware deep learning frameworks for assistive driving. The research is mainly focused on deep learning and computer vision-based techniques to generate a context for assistive driving. It incorporates the latest state-of-the-art techniques using suitable driving context and the driver is alerted. Many automobile manufacturing companies and researchers would refer to this study for their enhancements.

This chapter discusses what special education means for students with visual impairments (that is, those who are blind or have low vision) including what is being done and how traditions are maintained. More specifically, this chapter explores the importance of advancing values for the diverse population of students with visual impairments, focusing on cultivation of supportive, inclusive, and collaborative educational environments that continue to stand the test of time. This chapter highlights the increasing heterogeneity of this population of students and specific instructional strategies to support the cultural and linguistic diversity of learners with visual impairments in today's classrooms. This chapter also discusses the significance of promoting core concepts that are rooted in a traditional and specialized instructional framework for students who are visually impaired.

The current predominant delivery format resulting from digitization is PDF, which is not appropriate for the blind, partially sighted and people who read on mobile devices. To meet the needs of both communities, as well as broader ones, alternative file formats are required. With the findings of the eBooks-On-Demand-Network Opening Publications for European Netizens project research, this study aims to improve access to digitized content for these communities.

In 2022, the authors conducted research on the digitization experiences of 13 EODOPEN partners at their organizations. The authors distributed the same sample of scans in English with different characteristics, and in accordance with Web content accessibility guidelines, the authors created 24 criteria to analyze their digitization workflows, output formats and optical character recognition (OCR) quality.

In this contribution, the authors present the results of a trial implementation among EODOPEN partners regarding their digitization workflows, used delivery file formats and the resulting quality of OCR results, depending on the type of digitization output file format. It was shown that partners using the OCR tool ABBYY FineReader Professional and producing scanning outputs in tagged PDF and PDF/UA formats achieved better results according to set criteria.

The trial implementations were limited to 13 project partners’ organizations only.

This research paper can be a valuable contribution to the field of massive digitization practices, particularly in terms of improving the accessibility of the output delivery file formats.

Breast cancer is the most diagnosed type of cancer in the world, and mastectomies to remove tumors are still common. An external breast prosthesis (EBP) can be used to minimize the asymmetry, due to the ablation. Some governments do not cover costs of that assistive technology, and women end up using socks and fabric pockets filled with seeds, to simulate the volume lost in the surgery. This study aims to offer to those women a decent solution, ergonomic, but still affordable.

The authors interviewed 20 mastectomized Brazilian women, listened to their relate and 3D scanned them, to give rise to personalized external lightweight breast prostheses. The authors used free software for computer-aided design and computer-aided manufacturing, and low-cost 3D printers. From the strategy of bespoke products, this study generalized the method, to conceive mass customized prostheses, in a compromise solution that reduces personalization, conserving the best features of design.

This study achieved a method to manufacture ergonomic, bespoke external breast prostheses, using low-cost technology. Previous literature made them using expensive scanners, software and printers.

The authors validated this method during pandemic, which restricted the number of patients the authors could have access to. This impacted authors’ possibility to work on matching the color of the final product and real skin. The authors understood, though, that precision of color, in the final product, is challenging, because of the peculiar aspects of human skin.

Using the method the authors proposed, personalized external breast prostheses can be manufactured using low-cost resources, democratizing better quality of life for more breast cancer survivors.

This paper aims to optimize the stiffness coefficient of the elastic element for a passive waist assistive exoskeleton (WAE). There is a tradeoff between stiffness coefficient of elastic element of the exoskeleton and work efficiency of the wearer, because elastic element affects original bending motion of the wearer and the force requirement of erector spinae is compensated by the other muscles. However, there is no accepted conclusion on how to determine the proper stiffness coefficient, especially with respected to the effort of groups of muscles, not only erector spinae.

In this study, a consumption indicator based on muscle fatigue of seven muscles is proposed and a Bayesian-based human-in-the-loop optimization approach is adopted to optimize the stiffness coefficient. Pneumatic artificial muscles are used to replace the mechanical elastic part to adjust the assistive force automatically. The proposed optimization method is verified by the way of load-lifting experiments with three different conditions: without exoskeleton, with fixed air pressure and with optimized air pressure. Six subjects participated in the experiment and each experiment is performed in different day.

Compared with No-Exo condition and static assistance condition, the parameter-optimized waist exoskeleton averagely reduces muscle fatigue of the six subjects by 45.30 ± 29.14% and 30.94 ± 30.29%, respectively. The experimental results indicate that the proposed method is effective to reduce muscle fatigue during stoop lifting task.

This paper provides a novel cost function construction method based on muscle fatigue and muscle synergy for passive WAE stiffness optimization.

The purpose of this study is to critically reflect on visually impaired customers’ technology assistance needs and the perceptions of existing technologies’ performance in the contexts of hospitality and tourism.

Following a qualitative approach, this study used in-depth semistructured interviews with 19 participants with visual impairments.

Positive and negative sides of technology-assisted experiences in the hotel, restaurant, and travel domains were summarized, and room for improvement was discussed to enhance the quality of life and travel experience of visually impaired customers.

Findings from this study offer actionable implications and future directions to technicians and managers to make hospitality and travel experiences more inclusive.

This timely reflection addresses a critical situation by offering original ideas and calling for more discussion of under-represented groups with visual impairments.

Persons with disabilities (PwD) are generally less likely to be employed than the general population. The paper aims to investigate whether access to assistive technologies (AT) impacts labour force participation in the disabled population.

The study utilises the nationally representative survey on disability in India conducted in 2018 through multi-stage sampling by the Ministry of Statistics and Programme Implementation (MOSPI), Government of India. The instrumental variable (IV) approach has been employed to infer causality between AT and labour market participation.

The study found that the labour force participation rate (LFPR) in India amongst the disabled population was 29% in the age group of 15–65 years in 2017–2018, as compared to 52% in the general population. Around two-thirds of the PwD respondents who were advised to acquire aid appliances acquired them, implying limited access to AT. The probability of LFPR in disabled population increases by 26.6% with access to AT particularly in urban areas.

Persistent issues such as lack of adequate amenities, poor literacy and a lack of vocational skills need to be addressed to improve labour market outcomes for the disabled population in rural areas.

Despite its role in promoting distributive justice and inclusive development, research on equity gaps in access to AT and its impact on labour market outcomes is scant. This is the first paper that provides empirical evidence on the impact of access to AT on LFPR in the context of low- and middle-income countries.