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Measuring Outcomes and Impact Related to Assistive Technology and Accessibility for Disability

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A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 16375

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Special Issue Editors

Dr. Rochelle Mendonca

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Guest Editor

Department of Rehabilitation and Regenerative Medicine, Columbia University Medical Center, New York, NY, USA
Interests: accessibility; assistive technology; innovative measurement approaches; rehabilitation robotics; disability outcomes

Dr. Qussai Obiedat

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Guest Editor

Department of Rehabilitation Sciences, Jordan University of Science and Technology, Irbid, Jordan
Interests: assistive technology; accessibility; universal design; measurements; outcome measures

Prof. Dr. Roger O. Smith

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Guest Editor

School of Rehabilitation Sciences & Technology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
Interests: accessible design; assistive technology; universal design; bifocals and falling; outcome measurement and disability

Special Issue Information

Dear Colleagues,

Assistive Technology (AT) is a key enabler for people with disabilities across the lifespan in all areas of life. It has the potential to improve their functional ability, enable and enhance their participation and inclusion in all domains of life, and promote their well-being. Globally, the need for AT is growing rapidly alongside the rise in noncommunicable diseases and an ageing population. The WHO estimates that only 1 in 10 people globally have access to the AT they need, with more than 2.5 billion people needing one or more assistive products. It is estimated that by 2050 more than 3.5 billion people will need at least one assistive product, with many older people needing two or more.

Without access to AT products, people with disabilities are often socially isolated and the impact of their disability is felt not only at the individual level but also at the level of family, community, and country. To address the large and growing unmet need for AT, several international efforts have been made to enhance access and use to AT, especially by the WHO and the United Nations (UN). Additionally, AT is commonly used with accessibility and universal design (UD) approaches. However, there is a paucity in research targeting AT, accessibility, and UD innovative strategies for measuring and collecting access, impact, and outcome data.

The aim of this Special Issue is to collect original research, comprehensive reviews, and advanced theoretical approaches regarding the measurement of outcomes and impact related to assistive technology and accessibility for disability.

The topics of interest for this Special Issue include, but are not limited to, the following:

Advances in impact and outcome measurements for AT;
Impact and outcome studies using innovative measurement for assistive technology;
Measuring access to AT;
Training related to AT;
The application of AI for measuring outcomes of AT;
The application of AI for measuring accessibility and outcomes of universal design strategies;
Innovative tools and strategies for measuring accessibility and/or universal design;
Innovative papers in measurement topics including in robotics, personal communication, smart apps, vision and hearing systems, information strategies, mobility, transportation, work-systems, personal care, and home health;
Innovative methodology for improving cross-cultural implementation of AT and UD measures;
Innovative methods for improving the reliability and validity of AT and UD measures.

Dr. Rochelle Mendonca
Dr. Qussai Obiedat
Prof. Dr. Roger O. Smith
Guest Editors

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Keywords

assistive technology
disability
accessibility
measurement
outcomes
universal design

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Published Papers (8 papers)

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Research

28 pages, 1786 KB

Open AccessArticle

Measuring Assistive Technology Outcomes via AI-Based Kinematic Modeling of Individualized Routine Learning in Elite Boccia Athletes with Severe Cerebral Palsy: A Longitudinal Case Series

by Se-Won Park and Young-Kyun Ha

Bioengineering 2026, 13(3), 261; https://doi.org/10.3390/bioengineering13030261 - 25 Feb 2026

Viewed by 606

Abstract

Objectives: This longitudinal single-case series evaluated an AI-based routine-learning system as assistive technology (AT) for elite Boccia athletes with severe Cerebral Palsy (CP). The study aimed to provide an innovative outcome measurement approach for individualized monitoring by integrating performance scores and longitudinal kinematic variability indicators. Methods: Three national-level players performed 694 throws over eight weeks. To ensure technical credibility, trials were rated through a consensus-based assessment by a panel of two experts, serving as ground truth for AI modeling. The system utilized a Bidirectional Long Short-Term Memory (Bi-LSTM) architecture to extract 29 kinematic features and perform regression-based scoring, providing real-time augmented feedback. Results: High-baseline tasks maintained stable scores (7–9), while intermediate tasks showed significant score increases, reflecting motor learning transitions. The model achieved a Mean Squared Error of 1.14 and a Mean Absolute Error of 1.13, demonstrating high alignment with expert standards. Training demonstrated stable convergence, with loss reducing from 7.45 to 1.19. Notably, for the most severely impaired athlete, the AI system detected a 4.69% reduction in kinematic variability despite stagnant performance scores. This provides empirical evidence of movement stabilization within the cognitive stage that traditional observation might overlook. Conclusions: The Bi-LSTM system enabled accurate tracking of performance and motor variability, revealing distinct learning curves based on task difficulty. These findings demonstrate the feasibility of AI-enabled motion analysis as an AT for outcome measurement, supporting data-driven coaching where conventional evaluation is constrained by the rarity and severity of disabilities. Full article

(This article belongs to the Special Issue Measuring Outcomes and Impact Related to Assistive Technology and Accessibility for Disability)

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23 pages, 711 KB

Open AccessArticle

Examining the Acceptance and Use of AI-Based Assistive Technology Among University Students with Visual Disability: The Moderating Role of Physical Self-Esteem

by Sameer M. Alnajdi, Mostafa A. Salem and Ibrahim A. Elshaer

Bioengineering 2025, 12(10), 1095; https://doi.org/10.3390/bioengineering12101095 - 11 Oct 2025

Cited by 2 | Viewed by 2380

Abstract

AI-based assistive technologies (AIATs) are increasingly recognised as essential tools to enhance accessibility, independence, and inclusion for visually impaired students in higher education. However, limited evidence exists regarding the determinants of their acceptance and use, particularly in terms of psychosocial factors. This study aimed to extend the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating physical self-esteem (PSE) as a moderator and behavioural intention (BI) as a mediator within a single model. Data were collected through a validated questionnaire administered to 395 visually impaired undergraduates across five Saudi universities. Constructs included effort expectancy (EE), performance expectancy (PE), facilitating conditions (FCs), social influence (SI), BI, and PSE. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used for analysis. Results showed that PE and SI significantly predicted both BI and adoption, while EE strongly predicted BI but not AIAT adoption; FC had no significant influence on either outcome. BI positively affected AIAT adoption and mediated the effects of PE, EE, and SI, but not FC. Moderation analysis indicated that PSE strengthened the influence of PE, EE, and SI on BI and adoption. These findings underscore the significance of psychological factors, particularly self-esteem, in promoting the adoption of AIAT and offer guidance for developing inclusive educational strategies. Full article

(This article belongs to the Special Issue Measuring Outcomes and Impact Related to Assistive Technology and Accessibility for Disability)

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19 pages, 650 KB

Open AccessArticle

Measuring the Impact of Large Language Models on Academic Success and Quality of Life Among Students with Visual Disability: An Assistive Technology Perspective

by Ibrahim A. Elshaer, Sameer M. AlNajdi and Mostafa A. Salem

Bioengineering 2025, 12(10), 1056; https://doi.org/10.3390/bioengineering12101056 - 30 Sep 2025

Cited by 5 | Viewed by 1990

Abstract

In the rapid digital era, artificial intelligence (AI) tools have progressively arisen to shape the education environment. In this context, large language models (LLMs) (i.e., ChatGPT vs. 4.0 and Gemini vs. 2.5) have emerged as powerful applications for academic inclusion. This paper investigated how using and trusting LLMs can impact the academic success and quality of life (QoL) of visually impaired university students. Quantitative research was conducted, obtaining data from 385 visually impaired university students through a structured survey design. Partial Least Squares Structural Equation Modelling (PLS-SEM) was implemented to test the study hypotheses. The findings revealed that trust in LLMs can significantly predict LLM usage, which in turn can improve QoL. While LLM usage failed to directly support the academic success of disabled students, but its impact was mediated through QoL, suggesting that enhancements in well-being can contribute to higher academic success. The results highlighted the importance of promoting trust in AI applications, along with developing an accessible, inclusive, and student-centred digital environment. The study offers practical contributions for educators and policymakers, shedding light on the importance of LLM applications for both the QoL and academic success of visually impaired university students. Full article

(This article belongs to the Special Issue Measuring Outcomes and Impact Related to Assistive Technology and Accessibility for Disability)

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18 pages, 1015 KB

Open AccessArticle

Edge-Driven Disability Detection and Outcome Measurement in IoMT Healthcare for Assistive Technology

by Malak Alamri, Khalid Haseeb, Mamoona Humayun, Menwa Alshammeri, Ghadah Naif Alwakid and Naeem Ramzan

Bioengineering 2025, 12(10), 1013; https://doi.org/10.3390/bioengineering12101013 - 23 Sep 2025

Cited by 1 | Viewed by 840

Abstract

The integration of edge computing (EC) and Internet of Medical Things (IoMT) technologies facilitates the development of adaptive healthcare systems that significantly improve the accessibility and monitoring of individuals with disabilities. By enabling real-time disease identification and reducing response times, this architecture supports personalized healthcare solutions for those with chronic conditions or mobility impairments. The inclusion of untrusted devices leads to communication delays and enhances the security risks for medical applications. Therefore, this research presents a Trust-Driven Disability-Detection Model Using Secured Random Forest Classification (TTDD-SRF) to address the issues while monitoring real-time health records. It also increases the detection of abnormal movement patterns to highlight the indication of disability using edge-driven communication. The TTDD-SRF model improves the classification accuracy of abnormal motion detection while ensuring data reliability through trust scores computed at the edge level. Such a paradigm decreases the ratio of false positives and enhances decision-making accuracy in coping with health-related applications, mainly the detection of patients’ disabilities. The experimental analysis of the proposed TTDD-SRF model indicates improved performance in terms of network throughput by 48%, system resilience by 42%, device integrity by 49%, and energy consumption by 45% while highlighting the potential of medical systems using edge technologies, advancing assistive technology for healthcare accessibility. Full article

(This article belongs to the Special Issue Measuring Outcomes and Impact Related to Assistive Technology and Accessibility for Disability)

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16 pages, 394 KB

Open AccessArticle

Technology-Enabled Cognitive Strategy Intervention for Secondary Stroke Prevention: A Feasibility Study

by Timothy Dionne, Jessica D. Richardson, Davin Quinn, Karen Luo and Suzanne Perea Burns

Bioengineering 2025, 12(7), 778; https://doi.org/10.3390/bioengineering12070778 - 18 Jul 2025

Viewed by 1532

Abstract

Background: People with post-stroke cognitive impairment (PSCI) are at increased risk of recurrent stroke, dementia, and accelerated cognitive decline. Objective: To examine the feasibility, safety, acceptability, and suitability of a virtually-delivered vascular risk reduction intervention that integrates tailored cognitive strategy training for people with executive function (EF) impairments post-stroke. Methods: This case series included eight participants who completed up to ten virtual sessions focused on vascular risk reduction and metacognitive strategy training. Sessions averaged 40 min over a 4–5-week period. Results: The intervention was found to be feasible, safe, and acceptable. The recruitment rate was 66.7%, and the retention rate was 87.5% (7 of 8 completed the training). No serious adverse events were reported. Most participants demonstrated improvements on the Canadian Occupational Performance Measure (COPM), with mean performance and satisfaction change scores of 1.22 ± 0.87 and 1.18 ± 0.83, respectively. Conclusions: This technology-enabled intervention was feasible and acceptable for individuals with post-stroke EF impairments. Virtual delivery was a key factor in its accessibility and success. The results are promising for improving self-management of vascular risk factors, warranting further study in larger trials. Full article

(This article belongs to the Special Issue Measuring Outcomes and Impact Related to Assistive Technology and Accessibility for Disability)

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19 pages, 259 KB

Open AccessEditor’s ChoiceArticle

Understanding the Impact of Assistive Technology on Users’ Lives in England: A Capability Approach

by Rebecca Joskow, Dilisha Patel, Anna Landre, Kate Mattick, Catherine Holloway, Jamie Danemayer and Victoria Austin

Bioengineering 2025, 12(7), 750; https://doi.org/10.3390/bioengineering12070750 - 9 Jul 2025

Cited by 6 | Viewed by 3266

Abstract

This study presents an analysis of England’s 2023 national assessment of assistive technology (AT) access and use, with a particular focus on the qualitative impact of AT as described by users. It aims to address limitations in conventional AT impact assessments, which often prioritize clinical outcomes or user satisfaction, by offering a deeper account of how impact is experienced in everyday life. Drawing on data from a nationally representative survey of 7000 disabled adults and children, as well as six focus group discussions and 28 semi-structured interviews with stakeholders across the WHO 5Ps framework (People, Providers, Personnel, Policy, and Products), the study applies Amartya Sen and Martha Nussbaum’s Capability Approach to explore these experiences. Using inductive thematic analysis, we identify three main domains of user-reported impact: Functions and Activities (e.g., mobility, communication, vision, leisure, daily routines, and cognitive support), Outcomes (e.g., autonomy, quality of life, safety, social participation, wellbeing, and work and learning), and Lived Experience (e.g., access barriers, essentiality, identity and emotional connection, peace of mind, and sense of control and confidence). These findings offer a more user-centered understanding of AT impact and can inform the development of future measurement tools, research design, and government-led interventions to improve AT provision. Full article

(This article belongs to the Special Issue Measuring Outcomes and Impact Related to Assistive Technology and Accessibility for Disability)

11 pages, 1602 KB

Open AccessArticle

Evaluating Assistive Technology Outcomes in Boccia Athletes with Disabilities Using AI-Based Kinematic Analysis

by Wann-Yun Shieh, Yan-Ying Ju, Shiu-Yuan Yang, I-Chun Chen and Hsin-Yi Kathy Cheng

Bioengineering 2025, 12(7), 684; https://doi.org/10.3390/bioengineering12070684 - 23 Jun 2025

Cited by 2 | Viewed by 1690

Abstract

This study explores how artificial intelligence (AI) can support the evaluation of assistive technology outcomes in adaptive sports, focusing on elite boccia athletes with disabilities. Using a multi-stage motion analysis framework, we integrated OpenPose, ViTPose, and Lifting to estimate seated joint kinematics with greater precision. Match footage from 12 athletes at the 2018 Asia-Pacific Boccia Open was analyzed across five biomechanical phases: preparation, acceleration, peak, release, and follow-through. AI-enhanced 2D and 3D pose estimation methods were applied to assess throwing strategies and motor variability. ViTPose outperformed OpenPose in joint detection accuracy (F1-score: 85% vs. 79.5%), while Lifting improved 3D estimation by reducing joint position error by 16%. Principal Component Analysis revealed greater movement consistency in overhand throws compared to underhand techniques. The proposed pipeline provides an interpretable and scalable method for measuring performance, motor control, and strategy-specific movement outcomes in boccia, offering practical applications for evidence-based coaching, athlete classification, and the design of inclusive assistive sport technologies. Full article

(This article belongs to the Special Issue Measuring Outcomes and Impact Related to Assistive Technology and Accessibility for Disability)

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20 pages, 2016 KB

Open AccessArticle

Exploring Growth-Stage Variations in Home Use of Positioning and Mobility Assistive Technology for Children with GMFCS IV Cerebral Palsy: Parental Insights and Challenges

by Hsin-Yi Kathy Cheng, Shun-Yin Hu, Yan-Ying Ju and Yu-Chun Yu

Bioengineering 2025, 12(3), 241; https://doi.org/10.3390/bioengineering12030241 - 26 Feb 2025

Cited by 2 | Viewed by 2985

Abstract

This study examines how the use of postural and mobility devices evolves in home environments for children with GMFCS IV cerebral palsy, focusing on parents’ perspectives on benefits, outcomes, and challenges. As children grow, changes in muscle strength, motor function, and daily activity demands necessitate adjustments in assistive devices to maintain mobility and postural support. Data from 10 parents, collected through descriptive statistics and qualitative interviews, covered device types, usage patterns, and family impacts across developmental stages from preschool to adulthood. Device needs shift significantly with growth, transitioning from early gait trainers and postural support devices to advanced mobility devices, such as power wheelchairs, which become essential in adulthood. Parents reported positive outcomes, including improved emotional well-being, social participation, and independent mobility, alongside reduced caregiving burdens. However, challenges persist, including financial constraints, frequent device replacements, and limited training for users and caregivers. These insights highlight the need for more adaptable device designs and enhanced family-centered support programs to better assist caregivers in managing device transitions. This study addresses a gap by exploring the real-world outcomes of home-based device use, providing data and parental insights to inform device design, clinical practices, and family-centered support programs. Future research should focus on enhancing device functionality, customization, and accessibility to improve quality of life and promote greater independence for individuals with cerebral palsy. Full article

(This article belongs to the Special Issue Measuring Outcomes and Impact Related to Assistive Technology and Accessibility for Disability)

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