Two Decades of Assistive Technologies to Empower People with Disability: a Systematic Mapping Study

For Peer Review Two Decades of Assistive Technologies to Empower People with Disability: a Systematic Mapping Study Journal: Disability and Rehabilitation: Assistive Technology Manuscript ID TIDT-05-2023-001.R1 Manuscript Type: Research Article - authors made known to the reviewers Keywords: Systematic Mapping Study, Assistive technologies, People with disabilities Note: The following files were submitted by the author for peer review, but cannot be converted to PDF. You must view these files (e.g. movies) online. TIDT_05_2023_001_LatexSources.zip URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology For Peer Review Two Decades of Assistive Technologies to Empower People with Disability: a Systematic Mapping Study J.G. Enr´ıquez, Luis M. Soria Morillo, J.A. Garc´ıa-Garc´ıa, and Juan A. ´ Alvarez-Garc´ıa Computer Languages and Systems Department. University of Seville, Spain. Escuela T´ecnica Superior de Ingenier´ıa Inform´atica, Avenida Reina Mercedes, s/n., 41012, Seville. ARTICLE HISTORY Compiled September 22, 2023 ABSTRACT Purpose: Information and Communication Technologies have transformed our lives in different social areas, facilitating interpersonal relationships thanks to technological tools. In the specific case of people with disabilities, Assistive Technologies (ATs) break down barriers and increase opportunities to become active members of the society with equal opportunities. Materials and methods: This paper presents a systematic mapping study that analyzes the current state-of-the-art of ATs proposed in the literature to support the empowering of people with disability. Specifically, this paper focuses on (1) describing a global vision of the scientific literature published in the last 20 years about ATs in the computer science field and (2) identifying research needs, gaps, and trends. Results: For this purpose, an in-depth analysis of 389 primary studies is presented. The information obtained from the mapping process is also constrained. Concretely, 35 ATs versus 22 disabilities are compared, obtaining striking peaks for some disabilities described in the discussion. Conclusions: Finally, the findings show that several areas have been covered only lightly, revealing interesting future directions and challenges for junior researchers. KEYWORDS Systematic Mapping Study; Assistive Technologies; People with disabilities 1. Introduction The enormous technological development in recent decades has led to a social revolution, coining the term known as Information Society [1]. This term refers to the prominent role that current technological innovations (i.e., software tools, techniques, or devices) have had and have in improving people’s interpersonal relationships and producing and sharing large amounts of information in a quasi-instantaneous way [2]. In this context, Information and Communication Technologies (ICTs) have transformed our lives in different areas of society (i.e., education, labor relations, or personal relationships, among others) [3], allowing and facilitating these interpersonal relationships. From a social perspective, the development of ICTs has allowed personal growth as an individual and citizen of society with equal opportunities [4]. ICTs play an essential role for people with disabilities by enhancing their personal development and independence and by assisting their daily lives [5]. For instance, ICTs enable them CONTACT J.G. Enr´ıquez. Email: [email protected] Page 1 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review to improve their quality of life by communicating independently for people who lack intelligible speech, facilitating independent mobility for people with severe motor impairments, or describing scenes for blind people. These benefits allow breaking down barriers [6] and expanding social integration [7] for people with disabilities. Nevertheless, some authors affirm that traditionally, technologies are conceived, designed and developed for use by neurotypical people, without adequately considering using them by people with disabilities [8,9]. Therefore, it is necessary to promote universal design patterns to place the user at the center of technology, not vice versa, using product design paradigms oriented to end-users. Over the last decades, a specialization of ICTs has emerged to improve the independence and autonomy of people with disabilities. These technologies, commonly known as Assistive Technologies (ATs), have become an increasingly important tool for improving the quality of their lives [10]. According to the United States legislation [11], AT is defined as “any item, piece of equipment or product system whether acquired commercially off the shelf, modified, or customized that is used to increase functional capabilities of individuals with disabilities”. The World Health Organization (WHO) states that, “more than one billion people in the world live with some form of disability, of whom nearly 200 million experience considerable difficulties in functioning” [12]. These numbers show how ATs can benefit millions of persons with disability in multiple ways: moving, seeing, communicating, eating or dressing, among others. It is relevant to highlight the importance of choosing the appropriate terminology and language to understand its real-world implications —SIGACCESS Accessible Writing Guide [13] [14]. We refer to the use of “disability” terminology. Traditionally, there are two ways to mention an individual’s disability [15,16]. The former refers to the use of a person-first language (i.e., “people with disability”), whereas the latter refers to the use of an identity-first language (i.e., “disabled person”). The semantics associated with these expressions have been discussed in recent years because of social connotations and stigmas [17,18]. Most scientific and professional authors defend the use of person-first languages, arguing the individual’s humanization, that is, the individual’s identities and experiences are emphasized instead of highlighting his/her disease or disability [19–22]. Conversely, some authors defend using the second terminology (that is, identity-first language) against the first language. In this context, some authors argue that person-first language fails to recognize diverse views on disability [15] or reduce stigma [23]. After considering these previous references, it is possible to conclude that there is no consensus in the scientific literature about the most appropriate and accepted way to use these expressions. In this context, we have decided to use the person-first language to promote human dignity and maintain this work’s scientific rigor. This study aims to discuss the state-of-the-art about ATs which are used by people with disabilities during activities of daily living. Although the accepted definitions of AT have been followed, we have analyzed them from an ICTs perspective, and given that all the authors are computer scientists, this may imply a prism that does not reflect the point of view of clinicians, although we have tried to be as exhaustive as possible. For instance, the term “mobility devices” used by a clinician would be included in the category “Robotics - Assistive Robots (humanoids, wheelchairs)” and “communication devices” would be included in the category “Human Computer Interface”. This paper analyzes the scientific literature published in the last 20 years about this topic, considering the type of work, disability, or disease on which it is focused, the technology, and the type of validation used. The results of this analysis contribute to highlight emerging trends and practices in this field. Therefore, this study aims to 2 Page 2 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review clarify the reader on the following topics: (1) what are the main applications of the AT which empower people with disabilities; (2) the scientific and technical solutions that have been proposed; and (3) evaluating the above solutions based on a classification scheme. To achieve the objectives described above, this paper presents a Systematic Mapping Study (SMS) [24,25]. An SMS is a specific form of Systematic Literature Review (SLR) [26] with a broader aim, whose results provide researchers with a global view of a specific topic. Furthermore, it allows to show up a set of research necessities and trends in the field. SMSs are typically used as a starting point for doing more work with a higher level of rigor. SMS is a formal method widely known by the computer science community for conducting systematic reviews. The main differences concerning a literature review are that a replicable, rigorous, and reliable result is obtained by applying it. This SMS will be guided by the following Research Question (RQ): “What is the state-of-the-art of ATs that have been proposed in the literature to support the empowering of people with disability?”. Finally, the rest of the paper is organized as follows. The closest related works to this proposal are described in Section 2. The research method is presented in Section 3. The threats to the validity of this study are presented in Section 5. The SMS execution is detailed in Section 4, and results are stated in Section 6. Section 7 summarizes the conclusions and proposes a set of future research lines based on the gap detected. 2. Related work Researchers have published systematic reviews related to ATs on different aspects of theoretical proposals or technological solutions. However, to the best of our knowledge, no systematic reviews have examined ATs comprehensively. Instead, the majority of these reviews concentrate on particular types of disabilities or specific ATs. Nevertheless, the scope and weaknesses of related work since 2015 are briefly presented below. Cunha et al. [27] presented a systematic review to catalog the state-of-the-art related to the application of Virtual Reality Technologies (VRT) as a mechanism to improve treatments of people with disabilities. Authors tried determining which disabilities are treated using VRT and the effectiveness of treatments based on this technology. This review was focused on intellectual disabilities (such as Autism Spectrum Disorders and Down Syndrome). Moreover, Climent et al. [28] reviewed the literature based on techniques involved in each step of wearable sensor modality for automated lifelogging. Caspo et al. [29] performed a survey to analyze ATs based on wearable solutions applied in rehabilitation tasks for people with disabilities. Authors identified recent trends in software and hardware-based signal processing relevant to the development of these kinds of solutions. This study was focused on very specific technologies and disabilities (i.e., hearing loss and visual illnesses). Wang et al. [30] presented a survey on sensor modality-centered human activity recognition in the health care context. The major concern that these studies present was that no methodological formalism had been used to conduct this survey. Leaman et al. [31] conducted a systematic review of studies published from 2005 to 2015 to identify which are the most relevant smart wheelchair solutions that have been used. Moreover, authors proposed new research lines and open issues on this topic, considering some of the most interesting the use of biometric techniques and Brain- Machine Interfaces (BMIs). Actually, BMIs are currently one of the most popular techniques for designing and developing AT devices. In this context, Lahr et al. [32] 3 Page 3 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review conducted a survey to know the acceptance degree of BMI solutions applied to patients affected by Amyotrophic Lateral Sclerosis (ALS). However, authors did not mention what specific BMI technologies were evaluated and how these technologies have been selected for this survey. Moreover, Baldassin et al. [33] presented a systematic study on ATs related to the use of computers. Authors identified 10 proposals and analyzed how these solutions can improve the quality of life of patients with traumatic and non-traumatic spinal cord injury. Although it is an interesting topic, the authors did not follow methodological review guidelines or selection criteria, which put the validity of the study at risk. In addition, the number of primary studies analyzed was too low. This fact may be caused because the authors only focused on digital libraries related to the healthcare context (e.g., PubMed or PsycINFO) without considering digital libraries related to computer science areas. The research community had also dedicated significant efforts to (1) meet the needs of older people and people affected with dementia, and (2) propose techniques or tools based on ATs to improve the day-to-day life of these people. On the one hand, regarding people affected with some type of dementia, some systematic reviews were conducted. Ienca et al. [34] reviewed the proposals from 2000 to 2016 that use intelligent ATs with application in dementia care, focusing on Alzheimer’s disease. A systematic review related to the use of electronic AT within healthcare centers aiming to improve care for people with dementia was presented by Daly et al. [35]. These studies had been conducted following formal methods. However, they did not apply quality criteria to evaluate primary studies, which hinder to discern the impact or relevance of these papers. As mentioned above, the queries of these studies had only been executed in digital libraries related to the healthcare context. On the other hand, regarding AT to improve the autonomy of older people, Kotteritzsch et al. [36] developed a systematic review on research papers (published from 2004 to 2014) that proposed AT to improve the autonomy and day-to-day life of older people in urban areas. Authors established a classification scheme to categorize primary studies according to attributes such as technology, application context, or target user, among others. Although the target people of the study was too relevant, the number of technologies considered in the study were very limited. Moreover, some studies that put focus on ATs for improving the independence of people with cognitive disabilities or learning disabilities had been developed. Perelmutter et al. [37] presented a systematic review to analyze ATs and determine their effectiveness in improving the learning deficits of people with learning disabilities. These studies did not analyze the usefulness or application of these technologies for people with other disabilities or healthcare needs. To sum up, although some proposals similar to this study have been conducted, various relevant gaps make this research necessary: •Methodology: Unlike some related studies that lack adherence to formal methods, the current research rigorously follows formal methodologies. This ensures that our study can be easily replicated or updated, enhancing its credibility and reliability. •Digital libraries: While many previous works predominantly focus on healthcare digital libraries, the current work also includes technology and computer sciencerelated ones. By exploring a different domain, we broaden the scope of potential applications and uncover novel insights. •Scope: Existing related works often address specific questions about particular 4 Page 4 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review Definition of Research Questions Conduct Search Screening Papers Keywording using Abstracts Data Extraction and Mapping Process Scope Papers Primary Studies Classification Scheme Systematic Map Figure 1. SMS methodology steps disabilities and ATs. In contrast, this work takes a more comprehensive approach, examining the technology’s application and support for any disability or group of disabilities. This broader perspective offers a holistic understanding of the field. •Primary studies: Within the literature surveyed in this section, there is a shortage of primary studies in some related works. Conversely, our research encompasses a substantial number of primary studies covering the developments and findings from the last two decades. This comprehensive review enriches the knowledge base and strengthens the foundation of our study. In conclusion, the current research bridges these gaps in the existing literature, contributing to a more thorough and well-rounded exploration of the intersection between technology and disability support. By employing rigorous methodologies and examining a broader range of digital libraries and primary studies, this work advances the understanding and potential applications of ATs for people with disabilities. 3. Research method The development of this study follows the method and recommendations presented by Petersen et al. [24] for conducting an SMS. An SMS is a form of SLR but with a broader scope. The main objective of this type of study is to provide a classification of the most relevant research papers focused on a specific topic. In addition, it also seeks to identify which aspects have been better or worse researched by the scientific community to continue or propose new research lines. SMSs are defined by 5 steps (cf. Figure 1): •(i) The definition of the research questions that will guide both the execution of the process and the results, •(ii) the execution of the search that will result in the set of interesting papers related to the treated topic, •(iii) the screening of the papers, where the filtering of papers is done based on the inclusion and exclusion criteria and will result in the primary studies, •(iv) the keywording using abstracts, whose main objective is the creation of the classification scheme by reading the title and abstract of the primary studies and, finally, •(v) the data extraction and mapping process to obtain the final mapping between the classification scheme and the primary studies will lead to the discussion. Before executing each of the steps described above, it was necessary to define the following elements: the digital libraries where the queries would be executed, the keywords and queries, and finally, the inclusion and exclusion criteria to be able to screen the papers. The choice of the digital libraries in which the queries will be executed has been 5 Page 5 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review made based on the authors’ experience in the execution of other systematic reviews. Accordingly, those digital libraries that publish conferences or journals of their own, such as ACM Digital Library [38] or IEEE Explore [39], were selected. Moreover, Science Direct [40] and Scopus [41] were included. In addition, PubMed [42] has also been considered as a bibliographic source to better cover the health context; it is also considered one of the largest sources of scientific information to date. Google Scholar was discarded due to its relatively limited advanced search options and its indexing of a substantial number of sources that may lead to a higher number of less relevant results in the initial findings. First, a preliminary search was carried out to define the keywords. Three main groups were defined to search the keywords: disability, target audience, and ATs. The most relevant titles were selected, and the documents were read to obtain the most used terms. The keywords repeated with more frequency by each of the groups are shown in Table 1. Table 1. Keywords Group Keyword Disability “disabled”, “disabilities” Target Audience “person”, “people“, “user” Assistive Technologies “assistive technology” Once the keywords were defined, the queries were created so that any term from each group should appear in the title, abstract, or keywords of the paper. Besides, if the search engine allowed it, it was filtered by computer science or software engineering category, the the authors’ main research field of this contribution. Having selected the digital libraries and keywords, the queries for each library were built (cf. Table 2) to be executed as a second step of the methodology. It is important to note that queries were limited to title, abstract, and keywords to ensure that the results obtained were closely related to the paper’s subject matter. Moreover, concerning the inclusion and exclusion criteria, a total of 5 filters (F) were defined: •F1: Discard duplicated papers after executing a search in a certain digital library. •F2: Discard papers that were not written in English. •F3: Discard papers whose source, i.e., journal or conference, are not indexed within the Journal Citation Report (JCR) [43] and GII-GRIN-SCIE (GGS) Conference Rating [44] rankings respectively. These indexes were taken as a reference to set up the quality assurance criteria that will corroborate the scientific rigor of the study. •F4: Discard papers that, after reading the title and abstract, are not within the topic of this proposal, i.e., those that are not explicitly focused on the discussion or proposal of ATs or that do not address disabilities or ATs comprehensively (e.g., those related to surgical aspects and interventions in the prosthetic field, good practice manuals, or psychological/social interventions related to the disabilities considered, among others). •F5: Discard papers related to systematic reviews, comparative studies, surveys, or discussions, among others. A selection of the most relevant ones will give rise to the related work section. Finally, after applying inclusion and exclusion criteria, each primary study is analyzed by a specific researcher of this study to determine its classification considering 6 Page 6 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review Table 2. Queries by digital library Digital Library Query ACM (Title:(“disabled” OR “disabilities”) AND Title:(“person” OR “people” OR “user”) AND Title:(“assistive technology”)) OR (Abstract:(“disabled” OR “disabilities”) ANDAbstract:(“person” OR “people” OR “user”) AND Abstract:(“assistive technology”)) OR (Keyword:(“disabled” OR “disabilities”) AND Keyword:(“person” OR “people” OR “user”) AND Keyword:(“assistive technology”)) IEEE Explore (((“Document Title”:“disabled” OR “Document Title”:“disabilities” OR “Abstract”: “disabled” OR “Abstract”:“disabilities” OR “Author Keywords”:“disabled” OR “Author Keywords”: “disabilities”) AND (“Document Title”:“person” OR “Document Title”:“people” OR “Document Title”:“user” OR “Abstract”:“person” OR “Abstract”: “people” OR “Abstract”:“user” OR “Author Keywords”:“person” OR “Author Keywords”: “people” OR “Author Keywords”:“user”) AND (“Document Title”:“assistive technology” OR “Abstract”:“assistive technology” OR “Author Keywords”:“assistive technology” ))) Scopus (TITLE-ABS-KEY((“disabled“ OR “disabilities“) AND (“person“OR “people“ OR “user“) AND “assistive technology“)) AND (LIMIT-TO (SUBJAREA, “COMP”) OR LIMIT-TO (SUBJAREA, “ENGI”)) Science Direct Title, abstract, keywords: (“disabled” OR “disabilities”) AND (“person” OR “people” OR “user”) AND (“assistive technology”) PubMed ((“disabled”[Title/Abstract] OR “disabilities”[Title/Abstract]) AND (“person”[Title/Abstract] OR “people”[Title/Abstract] OR “user”[Title/Abstract])) AND (“assistive technology”[Title/Abstract]) the type of disability, technology, etc. (cf., Section 4.1). After executing this stage, it is possible that some doubts arise about the adequacy and cataloging of some primary studies. Faced with this situation, our review method proposes holding face-to-face meetings between the researchers of this study in order to minimize the biases of each investigator and avoid subjective decisions, as well as establishing consensual agreements on which primary studies are relevant and their cataloging. 4. Execution This section describes activities 1 to 4 of the SMS, carried out until the results to be analyzed later are obtained. The first activity aims to define the research questions. In that sense, the main research question that aimed to understand the current literature on technological and software assistive proposals for empowering people with disabilities was divided into four sub-questions (cf. Table 3). Once the research questions were defined, the queries defined in Table 2 were exe- 7 Page 7 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review Table 3. Research Questions Research Question Motivation RQ1. How is the research area of AT proposals structured? What are trends concerning the publication quantity and focus of AT proposals? What are the leading publication channels for this area? Categorize by topics according to the focus of the proposals and identify where each proposal is published as a quality metric. For this purpose, international rankings are taken as reference (JCR for journals and GII-GRIN-SCIE for conferences), as well as a disability typology (Section 4.1.1) and a technological typology). The disability typology is based on two internationally recognized classifications (International Classification of Functioning, Disability and Health (ICF) [45]; and International Classification of Diseases (ICD) [46]), whereas the technological typology is based on 35 types grouped into 6 main categories as Section 4.1.2 shows. RQ2. What are the main research types and methods found in the proposals of AT? What have scientific methods been applied to evaluate and validate each study? Identify the types of research that have been carried out and the scientific method that has been used to evaluate and validate each primary study. These typologies are respectively described in Section 4.1.3 and Section 4.1.4 in detail. RQ3. What disability categories are treated using ATs? Categorize each analyzed AT proposal according to the type of disability group by the used assistive technology. In addition, this RQ aims to analyze trends in technologies that have been applied to support each disability. RQ4. What is the scientific or technological typology presented in the ATs? Categorize the ATs according to scientific or technological typology in order to analyze the temporal trends on each of the ATs (Section 4.1.2) and their evolution over time. 8 Page 8 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review confusing cases, a consensus decision was made among all the authors to include or exclude it. In this regard, the criterion for inclusion was the description of a novel technology that assists people with disability in their daily lives. In addition, evaluations of existing technologies on groups of people with disability were considered as an exclusion criterion, given that they did not provide a novel solution but an evaluation. Furthermore, it is important to emphasize that the PICO principles (Patient/Population, Intervention, Comparison, and Outcomes) were not utilized in our study, as we adopted the SMS method, which is commonly employed in software engineering research. Nevertheless, we encourage future researchers to consider employing the PICO approach in similar investigations to enhance the precision and specificity of their research questions. •Language bias: F2 proposed discarding papers that were not written in English. English’s predominance in the scientific community and its widespread use in international conferences and journals facilitated access to a diverse range of relevant studies and reduced language bias. The abundance of English-language literature enabled a comprehensive and robust analysis, leading to a more reliable and meaningful study. Nonetheless, the authors recognize the significance of exploring non-English sources in future research to understand the subject matter comprehensively. •Publication bias: In this case, no previous publications from the authors or co-authors have been cited, avoiding emphasizing some of the analyzed works. Furthermore, some experts colleagues in this area have analyzed the study to guarantee the criteria defined in Section 3. •Definition of the RQs: RQs were formed in the most comprehensive way possible concerning publications and dates. Therefore, this research was performed as completely as possible since it does not privilege particular papers. •Search strings: the quality of the selection of primary studies can be affected by the search strings (cf. Table 1) since the studies are obtained from its execution. The inaccurate definition of keywords that make up these strings may cause a significant bias in the results obtained. Two iterations of the SMS process were executed to mitigate this threat, the first to obtain preliminary results and the second to implement the complete study after refining keywords if necessary. •Mapping process: when it was unclear how to classify a paper in the different categories (type, validation, technology, and disability), the authors met and discussed until a consensus was reached. 6. Results and discussion This section aims to structurally answer the research questions posed in Section 3 and present an analysis of past and present ATs based on the previously described indicators. This analysis will provide a snapshot of the current state of ATs and show the evolution that it has undergone over the last 20 years (which allows identifying trends in this area over the next few years). 15 Page 15 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review Algorithm Framework Hardware Hardware + Software Method Methodolgy Model Software Software architecture Class 1 0 1 5 12 0 2 2 17 0 Class 2 203700313 0 Class 3 0 2 6 13 0 2 4 42 0 Class WIP 3 3 5 12 1 1 2 16 1 Q1 6 1 10 34 0 2 4 24 0 Q2 0 1 9 17 0 3 1 11 1 Q3 1 3 3 11 00070 Q4 4 1 3 24 1 0 1 17 0 WIP 212902318 0 0 5 10 15 20 25 30 35 40 45 Figure 4. Papers included in the analysis and categorized according to the quartile (Q) of the journal (JCR ranking) or class of the conference (GII-GRIN-SCIE ranking). Note: WiP stands for Work in Progress category for conference ranking. 6.1. RQ1. How is the research area of AT proposals structured? What are trends concerning the publication quantity and focus of AT proposals? What are the leading publication channels for this area? This RQ aimed to categorize by topics according to the focus of the study and identify where each study is published as a quality metric. On the one hand, Figure 4 shows the categories of the journal/conference. As mentioned in the inclusion and exclusion criteria (cf. section 3), the international rankings taken as a reference are the JCR for journals and the GII-GRIN-SCIE for conferences. The JCR ranking establishes four quartiles (i.e., Q1, Q2, Q3, and Q4, ordered by importance from highest to lowest) to rank the journals. A quartile is a unit used to measure the position of a journal. It is used to separate groups of journals in a given specialty, sorted by impact factor. Similarly, the GII-GRIN-SCIE ranking establishes four classes (i.e., Class 1, Class 2, Class 3, ordered by importance from highest to lowest, and WiP (i.e., Work in Progress)) to classify conferences. The conference class ranking is done through an algorithm —description available at [61]— that performs a series of calculations on data imported from other international rankings (i.e., The CORE 2018 Conference Rating [62], Microsoft Academic [63] and LiveSHINE (Google Simple H-Index Estimator) [64]. Conferences classified as WiP are those for which insufficient information is available for classification into classes 1, 2, or 3, but are recognized by the ranking. As can be seen in Figure 4, the case of software architecture cannot be considered as a representative, since the number of related works found during the bibliographic search has been very small. Regarding the software and framework typology, the most significant number of primary studies are in Class 3 and WiP. This is justified since many of the papers categorized in these typologies have had little formal validation, in many cases necessary for journals with a high impact factor or at top-level conferences. The opposite is true for the model, methodology, and algorithm typologies. In these cases, the largest concentration of papers is found in first-level conferences (class 1) and journals, mainly in Q1. Finally, the hardware+software and hardware typologies deserve special mention since the concentration of related work in the field 16 Page 16 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 4 624 64 15 48 13 22 1 5 22 9 4 29 3 3 25 102 41 63 1 06 Mental, behavioural or neurodevelopmental disorders 08 Diseases of the nervous system 08A0x Movement disorders 09 Diseases of the visual system 10 Diseases of the ear or mastoid process 15 Diseases of the musculoskeletal system or connective tissue 20 Developmental anomalies 21 Symptoms, signs or clinical findings, not elsewhere classified 22 Injury, poisoning or certain other consequences of external causes 6A00 Disorders of intellectual development 6A01 Developmental speech or language disorders 6A02 Autism spectrum disorder 6A03 Developmental learning disorder 6A04 Developmental motor coordination disorder 6D71 Mild neurocognitive disorder 6D8x Neurocognitive disorders - Dementia 8A2x Disorders with neurocognitive impairment as a major feature 8B6x Motor neuron diseases or related disorders 8D2x Cerebral palsy Multiple/Cross disability 6D80 Dementia due to Alzheimer disease Figure 5. Number of papers under analysis associated with each type of disability of AT has been noteworthy. Both categories contain more than 50% of the identified works, so their analysis is quite representative. Since these are the most widespread typologies in the literature, their publication in high-impact journals (Q1) and conferences, regardless of their classification, is significantly high, as can be seen in Figure 4. On the other hand, Figure 5 shows the proportion of papers related to the different types of disabilities identified in this analysis, following ICD-11 categorization as explained in Section 4.1.1, for which relevant contributions have been found in the bibliography. To these disabilities, as previously described, the category “Cross” has been added for those solutions that apply transversely to several disabilities. It should be said that the sum of the studies associated with each disability is greater than the ones considered in this analysis, given that during the analysis of the papers, up to 3 types of disability were assigned to each paper. The same occurs in Figure 6 (up to 2 types of technology per paper). The decision to make these multiple assignments was due to the difficulty of grouping jobs into a single disability or technology category. To avoid losing information and biasing the results due to difficulties during the validation process, this solution was applied. As it can be seen in Figure 5, data is unbalanced towards three specific disabilities: Motor neuron diseases or related disorders (8B6x), Cross (54), and Diseases of the visual system (09), with a total of 102, 63 and 64 papers classified in these categories respectively. These categories are related to the highest concentration of people with disability in the categories 8B6x and 09, where 42 [65] and 285 million [66] of people in the world present these disabilities. In this sense, the efforts of the scientific community and companies are especially focused on these disabilities. However, after analyzing the results of our study, there are disabilities with a high incidence in the population that have been less considered by the scientific community taking into account our selection process for primary studies of our systematic review. For example, the number 17 Page 17 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 16 3 13 6 13 3 9 5 32 27 11 41 14 2 4 19 18 15 6 29 5 51 7 8 7 87 10 16 3 8 10 19 13 19 12 24 Computer Vision Computer Vision - Augmented reality Computer Vision - Environment description Computer vision - Machine Learning Computer Vision - Obstacle avoidance Computer Vision - Sign language recognition Computer Vision - Virtual reality Human Computer Interface - Balance detection… Human Computer Interface - EEG… Human Computer Interface - Eye tracking Human Computer Interface - Morse Human Computer Interface - Motion recognition Human Computer Interface - Myography Human Computer Interface - Neurostimulation Human Computer Interface - Object detection Human Computer Interface - Tongue motion input Human Computer Interface - Touch/keyboard Human Computer Interface - Touch/keyboard/haptic Human Computer Interface - Voice recognition… Human Computer Interface - Voice recognition… Output device - Vibrations Robotics - Assistant robot (humanoids, wheelchairs) Robotics - Assisted rehabilitation Robotics - Exoskeleton / Prothesis Robotics - Software control system Software - Application (app or desktop isolated) Software - Environment description Software - Navigation support technology… Software - Robotic process automation Software - Serious games Software - Simulator Software - Smart homes Software - Text-to-speech synthesis Software - WEB and Cloud technology Software - WEB technology Wearable - Smart-watch, step device, etc. Figure 6. Number of papers under analysis associated with each type of technology of people living with dementia worldwide is currently estimated at 50 million [67] according to WHO. However, we have only found 4 primary studies which describe ATs focused on people with dementia (6D8x Neurocognitive disorders - Dementia; 6D80 Dementia due to Alzheimer disease) as shown in Figure 9 and Table of supplemental material. Concerning the “Cross” typology, it was also to be expected that the number of primary studies would be high, as a large number of the solutions proposed are crosscutting and have therefore been considered in this category. Among the other categories, Diseases of the musculoskeletal system or connective tissue (15) and Cerebral palsy (8D2x) stand out with an also considerable number of contributions. Finally, Figure 6 shows an analysis similar to the previous one except that, instead of disabilities, it considers the categorization of developed AT. AT refers to the different technologies considered in the classification and identified during the primary studies analysis. For the classification, 36 different technologies have been identified, grouped into 6 macro-categories. In this case, the greatest amount of work is again distributed into 3 categories: Application (S-APP), Assistant robot (S-AR), and Motion recognition (HCI-MR). Again, this is related to the prevalence of data on disabilities worldwide. Application-based solutions are often targeted at visual disabilities, while assistive robots and motion recognition are largely associated with disabilities related to motor system disorders. Therefore, mentioning the same justification as in the previous case, the most significant interest of the research community (considering published papers) is focused on these areas. The information illustrated in Figure 6, however, lacks an essential factor in making a perspective analysis of the proposed solutions: the time factor. The technologies mentioned above are well-established in the scientific field, and for many years they have produced many remarkable results. However, the emerging technologies in recent years, for example, computer vision or machine learning techniques, are relegated to the background because of their short past history. For this reason, the following 18 Page 18 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review answers to the research questions will consider, in addition to the number of primary studies, the time component, thus being able to identify technological trends (and declines). The macro-category Human-Computer Interface presents most of the proposed solutions, being Computer Vision, the one with the lowest concentration. Although it could be assumed that people with vision impairments need AT from Computer Vision, the reality is that they almost need more to facilitate their interaction with the environment or computers via HCI. Therefore, it is not surprising that more applicationoriented solutions or Assistant Robots surpass the computer vision category in the number of items. According to the leading conferences for this area, we find the following four terms of their periodicity, the number of papers they present, and the researchers who participate. We indicate the number of articles evaluated in brackets before applying the filters discussed in Section 3: International ACM SIGACCESS Conference on Computers and Accessibility - ASSETS (87), Conference on Human Factors in Computing Systems - CHI (65), International Web for All Conference - W4A (40), IEEE Engineering in Medicine and Biology - EMBS (15). These conferences are highly recommended for researchers working with AT to improve the lives of people with disabilities. Concerning the journals, as explained above, only those indexed in the Journal Citation Report (JCR) are included in this study; however, we consider highly recommendable the journal ACM Transactions on Accessible Computing (TACCESS), which, although not on this list, has a subject matter closely related to this work. Another channel for dissemination and advancement of the state-of-the-art are competitions. In this sense, CYBATHLON [68] should be highlighted as it aims to break down barriers between the public, people with disabilities, and technology developers and promote the inclusion of people with disabilities. 6.2. RQ2. What are the main research types and methods found in the assistive proposals? What have scientific methods been applied to evaluate and validate each study? This RQ aimed to identify the types of research carried out and the scientific method used to evaluate and validate each primary study. To check the maturity of a technology, a good indicator is the analysis of the results and the conditions under which those results have been evaluated. After discussing the results of RQ1 and considering the high volume of primary studies published in conferences and journals, the research community has generated a high volume of AT-based proposals practical to improve the autonomy and independence of people with disabilities. Below, the research typology of these proposals and how they have been validated according to the category of the journal or conference is analyzed. On the one hand, Figure 7 shows an analysis of validation type, representing the validation carried out in the works analyzed, being, from more to less exhaustive: experiment, case study, proof of concept, and without validation. Contribution type refers to the typology of the proposed solution, which, in essence, determines the solution generated by the primary study (see Section 4.1.4). This analysis shows that journals classified as Q1 have the highest proportion of seriously validated papers. That is, experiments, case studies and, to a lesser extent, proofs of concept are very significantly present in this category. In contrast, lower-level conferences (class 3) present a higher percentage of papers not validated or validated by proofs of concept. 19 Page 19 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 0 5 10 15 20 25 30 Case study Experiment Non validation Proof-of-concept Class 1 Class 2 Class 3 Class WIP Q1 Q2 Q3 Q4 WIP Figure 7. Number of papers grouped by type of validation in each category of journal or conference Computer Vision Computer Vision - Augmented reality Computer Vision - Environment description Computer vision - Machine Learning Computer Vision - Obstacle avoidance Computer Vision - Sign language recognition Computer Vision - Virtual reality Human Computer Interface - Balance detection Human Computer Interface - EEG Human Computer Interface - Eye tracking Human Computer Interface - Morse Human Computer Interface - Motion recognition Human Computer Interface - Myography Human Computer Interface - Neurostimulation Human Computer Interface - Object detection Human Computer Interface - Tongue motion input Human Computer Interface - Touch/keyboard Human Computer Interface - Touch/keyboard/haptic Human Computer Interface - Voice recognition (non-vocal input) Human Computer Interface - Voice recognition (vocal input) Output device - Vibrations Robotics - Assistant robot (humanoids, wheelchairs) Robotics - Assisted rehabilitation Robotics - Exoskeleton / Prothesis Robotics - Software control system Software - Application (app or desktop isolated) Software - Environment description Software - Navigation support technology (pedestrian) Software - Robotic process automation Software - Serious games Software - Simulator Software - Smart homes Software - Text-to-speech synthesis Software - WEB and Cloud technology Software - WEB technology Wearable - Smart-watch, step device, etc. Algorithm 10 002 11 01 11 11 0 0 1 0 1 0 3 1 2002 30 100010001 Framework 00 100 00 00 10 20 0 0 0 1 0 0 0 0 0000 40 210120030 Hardware 2 0 0 1 0 0 0 0 1 2 0 3 2 0 1 9 4 3 0 2 2 13 140 21 000000009 Hardware + Software 5 1 3 1 6 1 6 4 19 16 625 11 2 3 8 9 8 2 6 3 28 444 23 1 6122811011 Method 00 000 10 00 00 00 0 0 0 0 1 0 0 0 0000 00 000000000 Methodolgy 00 000 10 00 10 00 0 0 0 0 1 0 0 1 1002 51 000001112 Model 10 000 00 02 10 10 0 0 0 0 0 1 0 0 2100 50 001032222 Software 72 946 03 19 84 12 1 0 0 2 5 3 6 19 0 7214 51 7 9269712 14 9 2 Software architecture 00 000 00 00 00 00 0 0 0 0 0 0 0 0 0001 10 000000200 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Computer Vision Computer Vision - Environment description Computer Vision - Obstacle avoidance Computer Vision - Virtual reality Human Computer Interface - EEG Human Computer Interface - Morse Human Computer Interface - Myography Human Computer Interface - Object detection Human Computer Interface - Touch/keyboard Human Computer Interface - Voice recognition (non-vocal input) Output device - Vibrations Robotics - Assisted rehabilitation Robotics - Software control system Software - Environment description Software - Robotic process automation Software - Simulator Software - Text-to-speech synthesis Software - WEB technology Algorithm Framework Hardware Hardware + Software Method Methodolgy Model Software Software architecture Figure 8. Papers grouped by technology belonging to each of the classification categories 20 Page 20 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review On the other hand, Figure 8 illustrates the analyzed works grouped by technology belonging to each type. This fact will answer the question of what types of technology are used in the field of AT and how they are classified. According to the results obtained and presented in the previous figure, the most widespread typology is software and hardware, and the combination of both ones. This means that software and hardware works prevail over models, methodologies, and algorithms. This concentration of works in these categories is perfectly visualized in the technologies Voice recognition with non-vocal input (HCI-VRNV), Text-to-speech synthesis (S-TTS), and Environment description (S-ED), where 80% of the works considered belong to the category “software”. 6.3. RQ3. What disability categories are treated using assistive proposals? This RQ aimed to categorize each analyzed proposal according to the type of disability. In this sense, the categories of disability for which ATs have provided solutions and have made significant progress in the state-of-the-art have been identified. To this end, the primary studies have been analyzed from a timeless perspective, through the correlation between technologies and disabilities, and from a temporal point of view, through the evolution of work on AT over the last 2 decades. Figure 6.4 displays the number of analyzed papers tied to a specific technology and a specific disability. The point height indicates the number of papers at each intersection so that those technologies applied mostly to a specific disability will have a higher value than those technologies that have not been applied to that disability. Four main peaks can be seen. The first and highest appears with the technology assistant robots (R-AR) on the disability “degeneration of the lower motor neurons” (8B6x). Most of the works grouped in this category propose assistive solutions (at home and outdoors, such as the use of motorized wheelchairs) for people with reduced mobility. For this reason, this association between technology and disability is not strange, reaching a total of 23 related papers. Moreover, also related to the disability “degeneration of the lower motor neurons” (8B6x), we can see in the same figure that Human-Computer Interface - EEG (HCI-EEG), Human-Computer Interface - Eye tracking (HCI-ET) and Application (S-APP), where the contributions to this disability have also been relevant. Specifically, the number of contributions understudy with the previous technologies applied to this disability is 21, 15, and 12, respectively. Although with a lower number of contributions, the Application Technologies (S-APP) also offer solutions for the following disabilities: Diseases of the visual system (09) with 12 related papers and Cerebral palsy (8D2X) with 9 studies. In addition, assistant robot (AR) technology also makes significant contributions to the following disabilities: Cross disabilities with 10 related papers and Diseases of the musculoskeletal system or connective tissue (15) with 13 related studies. Continuing the above analysis, in Figure 9 a temporal vision is offered that will enable the analysis of the trends identified through the study of the evolution in the number of related primary studies in the different technologies and applied to the identified disabilities. The figure shows how the technology applied to Motor neuron diseases or related disorders (8B6x) has been increasing over the last few years, reaching 10 considered primary studies in 2018. Although this increase is particularly significant, for most disabilities it can be seen how the number of related papers has grown steadily over the last decade. Contrary to the previous trend, there are disabilities where the impact of technology is not particularly significant. For instance, 21 Page 21 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 1994 1995 1997 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 06 Mental, behavioural or neurodevelopmental disorders 100000000001000020000000 08 Diseases of the nervous system 100000000001000000101011 08A0x Movement disorders 000002210003120103304200 09 Diseases of the visual system 001010002103474663381634 10 Diseases of the ear or mastoid process 000000001001200010121033 15 Diseases of the musculoskeletal system or connective tissue 000111121311300246142337 20 Developmental anomalies 000000000002211000112201 21 Symptoms, signs or clinical findings, not elsewhere classified 000000000030100230332212 22 Injury, poisoning or certain other consequences of external causes 000000000000000000000001 6A00 Disorders of intellectual development 000000000002000000110001 6A01 Developmental speech or language disorders 000010004030003121001112 6A02 Autism spectrum disorder 000000001000020100003110 6A03 Developmental learning disorder 000000000000000010002100 6A04 Developmental motor coordination disorder 000000300114000206111315 6D71 Mild neurocognitive disorder 000000010000000000010001 6D8x Neurocognitive disorders - Dementia 000000000000010000200000 8A2x Disorders with neurocognitive impairment as a major feature 000200200107000010132051 8B6x Motor neuron diseases or related disorders 00032127334545463778910 6 3 8D2x Cerebral palsy 000014041202121222034315 Multiple/Cross disability 012000012435144125575533 6D80 Dementia due to Alzheimer disease 000000000000000000000000 0 2 4 6 8 10 12 Figure 9. Analysis of the temporal trend of the papers understudy on each of the disabilities present in the classification on “Diseases of the nervous system” (08), “Mental, behavioral or neurodevelopmental disorders” (06), and “Movement disorders” (8A0x). As it can be seen in Figure 9, the presence of related jobs in these disabilities is not particularly significant. Special mention should be made of “Symptoms, signs or clinical findings, not elsewhere classified” (21). This category groups together pathologies not included in any of the previous classifications and, therefore, they are usually associated with rare diseases. 6.4. RQ4. What is the scientific or technological typology presented in the assistance proposals? This RQ aimed to categorize the assistance proposals according to scientific or technological typology. In this paper, this RQ is perhaps the most ambitious of all as it seeks to outline the future of ATs. Based on the processed works and their technological and temporal categorization, Figure 10 shows the technological evolution in this area of research. Both figures have been generated from the same information, but through the two visualizations, it is possible to appreciate nuances for the elaboration of the trend hypotheses. In Figure 10, the temporal evolution of the identified technologies is projected. Although it appears that all technologies have evolved, increasing the number of identified works, some have done so more clearly. For example, applications (S-APP), Human- Computer Interface - Tongue motion input (HCI-T) or Human-Computer Interface - Voice recognition (HCI-VRNV and HCI-VRV) have shown an average year-on-year growth of more than 40% since 2012. Special mention should be made of computer vision-related technologies where, in virtually all subcategories, the overall number of 22 Page 22 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 1994 1995 1997 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Wearable - Smart-watch, step device, etc. 001000000003122300201000 Software - WEB technology 000000002200211020213012 Software - WEB and Cloud technology 000010001000130102011010 Software - Text-to-speech synthesis 100010000002121200311112 Software - Smart homes 000000100003000011110001 Software - Simulator 000000000000010201002011 Software - Serious games 000000000000010100000000 Software - Navigation support technology (pedestrian) 000000010000101000551101 Software - Environment description 000000101000011120011000 Software - Application (app or desktop isolated) 0011100212111 2 6 5 1 2 5 7 8 7 7 4 8 Robotics - Software control system 001000000010000000011110 Robotics - Exoskeleton / Prothesis 000000001000100011011110 Robotics - Assisted rehabilitation 000000100002000010002001 Robotics - Assistant robot (humanoids, wheelchairs) 000011122025300324344337 Output device - Vibrations 000000000000100002010000 Human Computer Interface - Voice recognition (vocal input) 002010102100230023210311 Human Computer Interface - Voice recognition (non-vocal input) 000000011110002000000000 Human Computer Interface - Touch/keyboard/haptic 000002210110100101010004 Human Computer Interface - Touch/keyboard 000001001210000114111021 Human Computer Interface - Tongue motion input 000000000122100310011302 Human Computer Interface - Object detection 000010000000001010010000 Human Computer Interface - Neurostimulation 000000000000000100100000 Human Computer Interface - Myography 000100020010010101113110 Human Computer Interface - Motion recognition 000001101201101234342721 Human Computer Interface - Morse 000102210202100000000000 Human Computer Interface - Eye tracking 000000010101031112122443 Human Computer Interface - EEG 000301130102002122424022 Human Computer Interface - Balance detection 000001000000000102000010 Computer Vision - Virtual reality 010000002011000010020000 Computer Vision - Sign language recognition 000000000010000010000010 Computer Vision - Obstacle avoidance 000000000010100010141100 Computer vision - Machine Learning 000000000100000000021001 Computer Vision - Environment description 000000000001100121210110 Computer Vision - Augmented reality 000000000000000100000110 Computer Vision 000000001011010320022201 0 10 20 30 40 50 60 Figure 10. Linear chart of the temporal trend of the papers under study on each of the technologies present in the classification 23 Page 23 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review Computer Vision Computer Vision - Augmented reality Computer Vision - Environment description Computer vision - Machine Learning Computer Vision - Obstacle avoidance Computer Vision - Sign language recognition Computer Vision - Virtual reality Human Computer Interface - Balance detection Human Computer Interface - EEG Human Computer Interface - Eye tracking Human Computer Interface - Morse Human Computer Interface - Motion recognition Human Computer Interface - Myography Human Computer Interface - Neurostimulation Human Computer Interface - Object detection Human Computer Interface - Tongue motion input Human Computer Interface - Touch/keyboard Human Computer Interface - Touch/keyboard/haptic Human Computer Interface - Voice recognition (non-vocal input) Human Computer Interface - Voice recognition (vocal input) Output device - Vibrations Robotics - Assistant robot (humanoids, wheelchairs) Robotics - Assisted rehabilitation Robotics - Exoskeleton / Prothesis Robotics - Software control system Software - Application (app or desktop isolated) Software - Environment description Software - Navigation support technology (pedestrian) Software - Robotic process automation Software - Serious games Software - Simulator Software - Smart homes Software - Text-to-speech synthesis Software - WEB and Cloud technology Software - WEB technology Wearable - Smart-watch, step device, etc. 06 Mental, behavioural or neurodevelopmental disorders 00000000000100100 0 0 00 000001 000010010 08 Diseases of the nervous system 10000000200010010 0 0 00 000010 000010000 08A0x Movement disorders 00000000420320000 5 0 10 720120 112101210 09 Diseases of the visual system 41704020100400301 5 0 50 000012 4 410008647 10 Diseases of the ear or mastoid process 00000200000100100 0 0 31 000070 000001112 15 Diseases of the musculoskeletal system or connective tissue 20001000742420076 1 2 30 13 24050 000010001 20 Developmental anomalies 00000010000000000 0 0 10 000071 200000211 21 Symptoms, signs or clinical findings, not elsewhere classified 10011000110230022 0 0 10 702120 000320001 22 Injury, poisoning or certain other consequences of external causes 00000000100000000 0 0 00 000000 000000000 6A00 Disorders of intellectual development 00000010000000000 0 0 00 000040 200000000 6A01 Developmental speech or language disorders 01120000010010000 1 3 40 101082 001002000 6A02 Autism spectrum disorder 01000010000000002 0 0 00 000060 002000001 6A03 Developmental learning disorder 00000000000000000 0 0 00 000010 001001101 6A04 Developmental motor coordination disorder 20100004261310034 1 0 23 610011 001150000 6D71 Mild neurocognitive disorder 10000000000000000 0 0 00 000020 100010100 6D8x Neurocognitive disorders - Dementia 00000000000100000 0 0 00 000020 000120000 8A2x Disorders with neurocognitive impairment as a major feature 10001000542210013 0 0 11 310070 100030100 8B6x Motor neuron diseases or related disorders 2111300221 15 4852082 1 2 60 23 23512 0 213340414 8D2x Cerebral palsy 60102001473820073 1 0 00 510290 101000101 Multiple/Cross disability 30122140222810020 3 0 70 10 11111 1 300481334 0 20 40 60 80 100 120 Computer Vision Computer Vision - Augmented reality Computer Vision - Environment description Computer vision - Machine Learni ng Computer Vision - Obstacle avoidance Computer Vision - Sign language recognition Computer Vision - Virtual reality Human Computer Interface - Balance detection Human Computer Interface - EEG Human Computer Interface - Eye tracking Human Computer Interface - Morse Human Computer Interface - Motion recognition Human Computer Interface - Myography Human Computer Interface - Neurostimulation Human Computer Interface - Object detection Human Computer Interface - Tongue motion input Human Computer Interface - Touch/keyboard Human Computer Interface - Touch/keyboard/haptic Human Computer Interface - Voice recognition (non-vocal input) Human Computer Interface - Voice recognition (vocal input) Output device - Vibrations Robotics - Assistant robot (humanoids, wheelchairs) Robotics - Assisted rehabilitation Robotics - Exoskeleton / Prothesis Robotics - Software control system Software - Application (app or desktop isolated) Software - Environment description Software - Navigation support technology (pedestrian) Software - Robotic process automation Software - Serious games Software - Simulator Software - Smart homes Software - Text-to-speech synthesis Software - WEB and Cloud technology Software - WEB technology Wearable - Smart-watch, step device, etc. 06 Mental, behavioural or neurodevelopmental disorders 08 Diseases of the nervous system 08A0x Movement disorders 09 Diseases of the visual system 10 Diseases of the ear or mastoid process 15 Diseases of the musculoskeletal system or connective tissue 20 Developmental anomalies 21 Symptoms, signs or clinical findings, not elsewhere classified 22 Injury, poisoning or certain other consequences of external causes 6A00 Disorders of intellectual development 6A01 Developmental speech or language disorders 6A02 Autism spectrum disorder 6A03 Developmental learning disorder 6A04 Developmental motor coordination disorder 6D71 Mild neurocognitive disorder 6D8x Neurocognitive disorders - Dementia 8A2x Disorders with neurocognitive impairment as a major feature 8B6x Motor neuron diseases or related disorders 8D2x Cerebral palsy Multiple/Cross disability Figure 11. Cross table with the primary studies under study corresponding to each disability in each of the technological categories described related jobs has increased. This is in line with the global rise of this technology not only in the healthcare field but also in different contexts such as safety, autonomous driving, and disease diagnosis among others using Deep Learning techniques. In 2008 and 2016, despite the evolution of each technology individually, two very pronounced peaks can be observed. Moreover, these peaks occur independently of technology, so their explanation does not seem to lie in the development of disruptive technology or/in the evolution of individual technologies. If the historical context is analyzed, in 2008 the decision 742/2008/EC of the European Commission [69] takes place through which the use of ATs in domestic environments and the use of these for the assistance of people with disability and dependent elderly people is regulated. In addition, in July 2008 the AppStore [70] for iOS and in October 2008 the Android application store (Google Play - [71]) were presented worldwide, representing a before and after in the proliferation and use of mobile applications for any purpose. This fact may be fundamental for the number of publications categorized as S-APP to grow drastically this year, reaching 11 papers in 2008 compared to the maximum of 2 published during the previous decade. On the other hand, at the end of 2015, the WHO will publish the “Health technology assessment” (HTA) [72], the systematic evaluation of properties, effects, and impact of ATs. This document provides the institutional framework for regulating these technologies and, therefore, the impact the following year on the number of related publications does not seem to be a simple coincidence. 24 Page 24 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review Table 5. AT Categories considered Category Identifier Computer Vision - Augmented reality CV-AR Computer Vision - Environment description CV-ED Computer Vision - Machine Learning CV-ML Computer Vision - Obstacle avoidance CV-OA Computer Vision - Sign language recognition CV-SL Computer Vision - Virtual reality CV-VR Computer Vision - Others CV Human Computer Interface - Balance detection HCI-BD Human Computer Interface - EEG HCI-EEG Human Computer Interface - Eye tracking HCI-ET Human Computer Interface - Morse HCI-M Human Computer Interface - Motion recognition HCI-MR Human Computer Interface - Myography HCI-MYO Human Computer Interface - Neurostimulation HCI-NEURO Human Computer Interface - Object detection HCI-OD Human Computer Interface - Tongue motion input HCI-TMI Human Computer Interface - Touch/keyboard/haptic HCI-HAP Human Computer Interface - Voice recognition (non-vocal input) HCI-VRNV Human Computer Interface - Voice recognition (vocal input) HCI-VRV Output device - Vibrations OD-V Robotics - Assistant robot (humanoids, wheelchairs) R-AR Robotics - Assisted rehabilitation R-REHAB Robotics - Exoskeleton / Prosthesis R-EX Robotics - Software control system R-SCS Software - Application (app or desktop) S-APP Software - Environment description S-ED Software - Navigation support technology S-NST Software - Serious games S-SG Software - Simulator S-SIM Software - Smart homes S-SH Software - Robotic process automation S-RPA Software - Text-to-speech synthesis S-TTS Software - Web technology S-WEB Software - Cloud technology S-CLOUD Wearable WEAR 31 Page 31 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review Two Decades of Assistive Technologies to Empower People with Disability: a Systematic Mapping Study J.G. Enr´ıquez, Luis M. Soria Morillo, J.A. Garc´ıa-Garc´ıa, and Juan A. ´ Alvarez-Garc´ıa Computer Languages and Systems Department. University of Seville, Spain. Escuela T´ecnica Superior de Ingenier´ıa Inform´atica, Avenida Reina Mercedes, s/n., 41012, Seville. ARTICLE HISTORY Compiled September 22, 2023 1. Supplementary table with the primary studies under study corresponding to each disability in each of the technological categories described CONTACT J.G. Enr´ıquez. Email: [email protected] Page 32 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review Table 1. Disabilities vs. technological categories classification scheme 06 6A00 6A01 6A02 6A03 6A04 6D71 6D8x 08 8A0x 8A2x 8B6x 8D2x 09 10 15 20 21 22 Cross CV- AR [1] [2] [1] [3] CVED [4] [5] [6] [7–15] [5] [16] CVML [17, 18] [18] [19] [20,21] CVOA [22] [23] [24–27] [24,25] [12,28– 30] [23,31] [26, 27] [32,33] CVSL [34, 35] [36] CVVR [37] [38] [39,40] [37] [36,36,41,42] CV [43,44] [45] [46] [47] [48,49] [43,46, 48–51] [45,52– 54] [51,55] [44] [56,57] HCIBD [58–60] [58,61] [62] HCIEEG [63,64] [65, 66] [67,68] [64,65, 69–71] [63,67–69,72– 89] [66,82, 85,90] [91] [72,74, 76,78,79, 84,92] [77] [93] [42,94] HCI- ET [95] [95–100] [98, 101] [96,97, 99,102] [100,101,103– 114] [107– 111,115] [55,103, 104,116] [117] [118] HCIMYO [119] [98] [120] [98, 121] [122] [82,87,121– 123] [82,115] [31,119] [124, 125] [36] HCIHAP [126] [38, 127] [128– 132] [68, 133– 136] [129, 137, 138] [68,139,140] [140– 142] [53,91, 143–146] [92,116, 130,138, 139,147, 148] [131, 149] [150–152] HCIVRNV [153– 155] [154,156] [157,158] HCIVRV [159, 160] [63,161] [162] [161] [63,163–167] [162,163, 168–170] [171– 173] [92,164, 167,174] [170] [175] [41,176–179] HCINEURO [180,181] 2 Page 33 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review 06 6A00 6A01 6A02 6A03 6A04 6D71 6D8x 08 8A0x 8A2x 8B6x 8D2x 09 10 15 20 21 22 Cross HCIMR [182] [119] [100,161, 183] [184] [185– 187] [161, 188] [48,100,112, 140,166,183, 185,189–192] [48,50, 62,140, 193–196] [39,40, 182,197] [198] [148,190, 193,199] [117, 119, 195] [21,56,176, 178,200–203] HCI- M [204] [205, 206] [204,207–209] [90,208, 210] [207,210] [211–213] HCITMI [183,214, 215] [46] [49,166,216– 220] [46,51, 85,221] [51,174, 221] [222] [223,224] HCIOD [225] [91,225, 226] [227] OD- V [59,60] [228] [107] [229] R- AR [95] [44,95, 230] [187, 231– 235] [23, 102, 122] [5,24–27,84, 89,113,122, 140,165,230, 231,233,235– 244] [24,25, 140,236, 237,244] [5,23,84, 232,242, 243,245– 251] [26, 27, 44, 175, 247, 252, 253] [94,150,202, 224,254–259] R- REHAB [230] [232, 260] [47] [230,260] [261] [232,246] [259] R- EX [262] [262–264] [31,247, 263,265] [124, 247] [203] R- SCS [266] [107,264,266, 267] [195] [107] [124,195] S- APP [268– 271] [119, 154, 272– 276] [2, 127, 277– 280] [281] [214] [282, 283] [22, 184] [284] [67, 121, 162, 266, 285] [205, 206, 268, 286– 288] [67,121,139, 181,191,266, 275,285,285, 289,290] [108,109, 142,221, 261,291– 293] [13,162, 286,288, 294–300] [172, 198, 229, 301– 304] [119,147, 157,221] [276, 305– 309] [284, 310] [118,212,213, 256,283,311– 318] S- NST [270, 271] [282] [235] [228] [24,235] [24] [13,29, 30,319] [320, 321] [32,33,315, 322] S- RPA [323] [190,323] [324] [190] S- SG [18] [325, 326] [325] [43] [185, 186] [18,185,327] [43] [328] S- SIM [204] [83] [285] [27,204,285] [27, 83, 253] [42,329–332] S- SH [58,64, 97,129, 131] [283] [22, 83] [64,97, 129] [58,81,167, 192] [167] [83, 131] [179,201,259, 283,313,322, 333–335] S- TTS [274, 336] [337] [162] [162,168, 337–342] [171] [151,343] S- WEB [344] [345] [323] [323] [298,324, 346,347] [348] [309, 344] [349–352] S- CLOUD [353] [45] [67, 266] [137] [67,75,192, 266] [291] [10,45, 170,354– 356] [357] [170] [358–360] S- ED [225] [272, 361] [362] [225,226, 363–365] [366] [151] WEAR [280] [281] [112,190,263, 289] [196] [12,143, 197,226, 367–369] [370] [190,251, 263] [281] [371] [41,118,203, 358,372,373] 3 Page 34 of 58 URL: http:/mc.manuscriptcentral.com/drtech Email: [email protected] Disability and Rehabilitation: Assistive Technology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 For Peer Review References [1] Julius Gelvsvartas and Rimvydas Simutis and Rytis Maskeliunas. 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