Assistive technology to support people with autism spectrum disorder in their autonomy and safety: A scoping review

Abstract

BACKGROUND:

Autism Spectrum Disorder (ASD) is a lifelong developmental disorder with increasing prevalence rates. People affected by ASD do often need support in various activities of daily living mainly provided by formal or informal caregivers. Assistive technology can help to increase autonomy and safety of people on the autism spectrum and thus decrease the burden of care.

OBJECTIVE:

This study aims to assess the state of the art of assistive technology (AT) that supports autonomy, self-reliance, comfort and wellbeing of people with ASD or aiming to prevent dangerous situations or shutdowns, caused by stressful (environmental) situations. Another aim is to analyze the fields of application and type of the proposed technologies and to explore the evaluations conducted.

METHODS:

A scoping review was carried out where the databases MEDLINE, IEEE and ACM Digital Library were searched. The identified articles were grouped according to the objective of the technology – the supported area of life that is assisted by the proposed systems. Furthermore, the conducted evaluations of the ATs in the papers were analyzed.

RESULTS:

A total of 40 articles were included in this review with a balanced distribution in the different fields of application (Communication & Social Life; Daily Living Assistants; Safety & Security). Eighteen studies conducted an evaluation of the proposed technology with people with ASD, mainly testing the functionality of the systems.

CONCLUSION:

The proposed technologies support people in the autism spectrum according to the main outcomes and symptoms of ASD. Further research is needed to determine the usefulness and acceptance of the ATs.

Keywords

Autism spectrum disorder (ASD)assistive technology scoping review

1. Introduction

Clichés of Autism like savant syndrome do only tell a part of the whole story. Autism Spectrum Disorder (ASD) can manifest in several different ways. It is a lifelong developmental disorder that comes with a wide range of symptoms affecting people who are on the spectrum in various severity levels in their activities of daily living. According to the DSM-V, the diagnostic criteria are persistent deficits in communication and social interaction and restricted, repetitive patterns of behavior, interests or activities [1].

The symptoms affect different areas of life that result in manifold challenges people on the autism spectrum face. Several studies [2, 3, 4] investigated the outcomes of ASD in different stages of life Amongst adolescents and adults, these include problems with language and speech as well as social relationships, behavioral problems challenges in education and employment, independence and self-care and difficulties with adaptive behavior.

Due to these outcomes, people do often need support in their daily living activities. This support is often provided by formal or informal caregivers. As a consequence, this implies a huge caregiver burden for families taking care of their (adult) children with ASD [5]. A study found, that the caregiver burden is even higher for parents of people with ASD than of people with attention deficit and hyperactivity disorder (ADHD) [6]. In close correlation is also the residential status of people with ASD. For young adults after high school, who usually leave their parents’ house and live independently, it was less likely for people with ASD to live alone. As a study on the living situation of young adults with ASD showed, only 16.6% have ever lived independently since leaving high school [7]. Another study on people with ASD with mean age of 24.8 years showed, that only 9% of people with ASD do live independently [3].

Assistive technology (AT) can help people with ASD to live a more independent life and thus support not only the people on the spectrum themselves but also their caregivers. AT is meant to support people in their living but still, the different purposes of the AT developed within the autism field are manifold and do reach from directly assistive measures like communication aids or daily life planner to oblique ones like the detection of certain behaviors for the purpose of diagnostics or therapy [8].

In recent years, literature reviews have explored the field of AT for people in the autism spectrum pursuing different goals. Some reviews focus on specific life areas of people with ASD [9] or symptoms and outcomes of ASD [10, 11]. Other articles include AT solely for children as the primary target group [12, 13]. Moreover, certain papers focus on diagnoses and therapy [14, 15], and thus the described AT only intermediately supports people on the autism spectrum. To our knowledge, there is no literature review existing, that specifically addresses AT providing direct support for people with ASD.

The goal of this scoping review is to provide a comprehensive overview of AT that either directly supports people with ASD in terms of autonomy, self-reliance, comfort and well-being or that helps people with ASD (and their caregivers) to protect against possible dangerous events. Other than that, the aim is to examine which of the identified papers did evaluate the developed technology with people on the autism spectrum and what are the evaluation domains. A focus is also laid on smart environments for people with ASD. This type of AT serves the advantage that no devices need to be put on (like wearables) or carried with (like smartphones).

2. Method

A scoping review has been conducted to find and synthesize literature on AT for people on the autism spectrum. This type of review was selected to have a more exploratory character in gaining an overview of existing AT for people with ASD. Furthermore, another focus was laid on the analysis of the evaluation of AT, where the comparability of evaluation domains, methods and results was unclear. The process of compiling this review was based on the guideline PRISMA for scoping reviews [16].

2.1 Search strategy

To identify relevant literature, research was carried out in the databases IEEE, ACM Digital Library and MEDLINE (PubMed) Keywords were selected and linked with the logic operators AND and OR. The searches were conducted using two search strings that differed between databases. The IEEE Database and ACM were searched with the following search string: (autism OR ASD OR autistic) AND (smart OR sensor OR “home automation” OR assist*). MEDLINE was searched using the string: (autism OR ASD) AND (smart OR sensor OR “home automation” OR “assisted living” OR “assistive technology”). The different strings were chosen due to the non-technical alignment of MEDLINE to avoid shifting offtopic by using only assist*. In the databases, the title, abstract and keywords were searched. The last database search was conducted at the end of January 2021.

2.2 Inclusion and exclusion criteria

The inclusion criteria applied for selecting literature were technologies aiming to directly assist any areas of daily living of people with ASD or help to ensure safety for people with ASD. Further criteria were, that the literature specifically deals with AT for people with autism spectrum disorder. Therefore autism spectrum disorder or related terms must have been mentioned in title, abstract or keywords. Papers in English or German Language that were published in the timeframe between 01.2010 and 12.2020 were included in the research.

In the literature research process, articles were excluded dealing with technologies, that do not serve the purpose of directly assisting people with ASD or ensuring safety. This excludes for example technology that is intended as learning tools to teach abilities like social skills, attention, etc., diagnostic and (tele)-therapeutic tools as well as (serious) games.

2.3 Practical implementation

The identification of literature was conducted independently by two researchers. All papers that were selected by any researcher were included after the first stage. The identified literature was inserted in Zotero, a software used for source management where also duplicates were removed in this process. Following a discussion and review process, literature was excluded during fulltext review that did not fit the inclusion or exclusion criteria.

2.4 Categorization of literature

To compare what fields of assistance the technologies are addressing and to reveal the distribution, also across different years of publication the identified literature was grouped according to the objective of the technology. To establish the categories, subcategories were used in a first step that describe the articles in terms of the objective of the technology. These subcategories were then summarized to the final categories. The grouping process was conducted independently by two researchers. Next to the objective of the technology, also the used type of technology and the evaluation domains were analyzed.

3. Results

A total of 40 papers were included in this scoping review. Figure 1 shows the process of identifying the relevant literature. In electronic databases, 900 articles (429 IEEE, 331 ACM, 140 MEDLINE) were found and title and abstract screened. After the removal of duplicates, 74 papers remained for further analysis. The reasons for exclusion and respective quantity of removed articles are evident in Fig. 1. Thirty-four articles were excluded during fulltext review for the following reasons: the described technology was not ASD related, the article was not in English or German language, the AT was developed for training or therapy or did not fulfill the aim of assisting people with ASD for other reasons (e.g. solely detection of repetitive stereotypical behavior), the technology was a serious game or the same technology was described in different papers. An overview of the identified articles with the year of publication, the objective of the technology the type of technology, the main elements of the proposed technology and conducted evaluations with domains and participants in the articles is shown in Table 1.

Figure 1.

PRISMA diagram.

3.1 Objective of the technology

As stated above, the identified articles were grouped according to the objective of the technology described in the paper. The following categories were identified: Communication & Social Life, Daily Life Assistants and Safety & Security.

Within the category Communication & Social Life the technologies support in terms of different alternative and augmentative communication (AAC) systems with picture to speech generators or generating speech output through lip movement. Furthermore, the measurement of personal space during conversations, prosody detection, emotion recognition, the selection of single speaker sounds in conversation with multiple people and speech volume and gaze direction assistance were proposed. The person with ASD receives feedback via smartphone apps or smart glasses. The predominant amount of AT in this category supports the lack of communication abilities of people with ASD while the support in social life is underrepresented. With the measurement of personal space or emotion recognition, only minor parts of social life assistants are covered. Challenges in social life are more far-reaching in people with ASD as they for example lack the ability to build relationships [3]. One paper presents a solution to foster independence in social situations [17].

The category Daily Life Assistants includes daily life planner and task instructions through different approaches like apps or smart glasses, urban street maps with safe points and paths and the signalization of

Table 1
Overview on identified articles

Autor	Year	Supported area of life	Used technology	Main content	Evaluation participants	Evaluation domains	Main findings
Ahmed et al. [21]	2017	Safety & Security	Wearable, App	Pulse, temperature and location tracking; notification to caregiver	–	–	–
Ahmed et al. [22]	2016	Communication & Social Life	Wearable, Stationary Device	AAC device to send predefined audio messages that are played by loudspeakers	–	–	–
Alhalabi et al. [23]	2014	Safety & Security	Wearable	Detection of repetitive behavior, pulse, temperature, sounds; notification to person with ASD and caregiver	–	–	–
Alivar et al. [24]	2020	Safety & Security	Algorithm, Stationary Device	Detection of sleep patterns and prediction of daytime behavior	2 people with ASD; mean age 15.4 years	Functionality	Restlessness as a 78% and 79% (different classifier) accurate prediction for daytime behavior
Alwakeel et al. [25]	2015	Safety & Security	Wearable	Detection of repetitive behavior, pulse, temperature, sounds, location; notification to caregiver; ambient control (e.g. locking of dangerous objects)	–	–	–
Babb et al. [17]	2020	Communication & Social Life	App	Visual scene display for assisting independence in daily life tasks and fostering communication	2 people with ASD and 2 with Down Syndrome; 13–21 years	Functionality	Increase in task performance and communication abilities
Bansode et al. [26]	2019	Safety & Security	Wearable	Detection of repetitive motion; information to people with ASD and activity monitoring for caregiver	–	–	–
Barman and Deb [27]	2017	Communication & Social Life	App	AAC – picture to speech	10 people with ASD; no age stated	Usefulness, Easiness, Relevance, Applicability	Usefulness, Easiness and Relevance rated 90% excellent or good 10% average; Applicability rated 60% excellent or good 40% average
Blommaert et al. [28]	2010	Daily Life Assistants	App	Schedule with instructions to perform tasks of daily living	–	–	–
Boyd et al. [29]	2017	Communication & Social Life	Wearable, app	Measuring of proximity (personal space) and displaying on smartphone	10 people with ASD; 7–14 years	Functionality, Usability	3 of 10 participants performed better with the AT in terms of proximity; 6 of 10 participants were able to use the system immediately
Boyd et al. [30]	2016	Communication & Social Life	Wearable	Detection and feedback on prosody with smart glasses	10 people with ASD; mean age 249 years	Functionality Usability	95% correct pitch detection and 97% correct volume detection; simple to use and understandable
Cantin-Garside et al. [31]	2020	Safety & Security	Wearable	Detection of self-injury behavior to support care of people with ASD	11 people with ASD; 5–14 years	Functionality Accuracy	Classifiers k-nearest neighbors (kNN) and support vector machines (SVM) with best results (99.1% accuracy)
Cena et al. [32]	2018	Daily Life Assistants	App	Interactive urban street map with specific information for people with ASD (safe points, crowded places, safe paths)	–	–	–

Table 1, continued
Autor	Year	Supported area of life	Used technology	Main content	Evaluation participants	Evaluation domains	Main findings
Cerga-Pashoja et al. [33]	2020	Daily Life Assistants	Algorithm	Simplification of texts in order to foster understanding	243 people with ASD; 153 adults; mean age 35.3 years; 90 adolescents; mean age 14.0 years	Functionality	Significantly better understanding – people gave more correct answers for the simplified texts ( $p<$ 0.001)
Chuah and Diblasio [34]	2012	Safety & Security	App	Detection of stereotypical behavior and notification to person with ASD; detection of environmental factors to relate with behavior	–	–	–
Eshetu et al. [35]	2018	Safety & Security	Stationary Device	Motion detection, reminders, geo fencing, blinds, panic button via Smart Home solution	–	–	–
Gentry et al. [36]	2015	Daily Life Assistants	App	Personal Digital Assistant to support in the beginning of a new job	50 people with ASD; mean age 24.0 years	Functionality Efficacy	Significantly less job coaching support needed when starting a job ( $p=$ 0.013)
Grozdanovska et al. [37]	2020	Communication & Social Life	App	AAC – picture to speech	–	–	–
Hong et al. [38]	2010	Daily Life Assistants	App	Daily life planner (todo list) for people with ASD and caregiver	5 people with ASD; no age stated 3 caregivers;	Usability	All participants (5 people with ASD and 3 caregivers) stated that it is easy to use and clear
Hou Lee and Wong [39]	2020	Communication & Social Life	Algorithm	Real time emotion recognition to support people with ASD	–	–	–
Imbiriba et al. [40]	2020	Safety & Security	Algorithm	Usage of biosensor data for prediction of aggressive behavior in children with ASD	20 people with ASD; mean age 10.8 years	Functionality	High rate of predicting aggressive behavior (AUC 0.98)
Khullar et al. [41]	2019	Safety & Security	Wearable	Detection of environmental factors (sensory overload); information to person with ASD and caregiver	10 people with ASD; mean age 11.8 years; 10 caregivers	Functionality, Satisfaction	80% of caregivers satisfied with the AT, 95% of caregivers satisfied with feedback system
Koumpouros and Toulias [42]	2020	Safety & Security	Wearable	Location monitoring, communication, scheduling, physiological parameters monitoring for people with ASD and caregivers	20 people with ASD mean age 13.5 years; 25 caregivers	Ease of use, Functionality, Sense of security	26% of people with ASD and 51% of caregivers stated excellent ease of use; 44% of people with ASD and 55% of caregivers stated that assistance is good or excellent
Lin [18]	2012	Safety & Security	Wearable	Detection of hunchback behavior and information to person with ASD	1 person with ASD; no age stated	Functionality	Significant improvement of hunchback behavior
Lin et al. [43]	2020	Communication & Social Life	Wearable	Emotion recognition through smart glasses	People with ASD; no age stated	Functionality, Usability	81% accuracy in emotion detection; High usability scores in clarity (4.25/5) and intuitiveness (4.3/5) and medium score in disruptiveness (1.9/5)
Machado et al. [44]	2019	Daily Life Assistants	Wearable	Smart glasses with augmented reality instructions for people with ASD on daily living tasks (conceptual framework only)	–	–	–
Mamun et al. [45]	2019	Safety & Security	Wearable	Detection of blood pressure, heart rate, body temperature, body motion, speech signals; alert to hospital	15 people with ASD; no age stated	Functionality	Bad health condition in children was detected in 5 cases (3 of them were detected correctly)

Table 1, continued
Autor	Year	Supported area of life	Used technology	Main content	Evaluation participants	Evaluation domains	Main findings
Mohammedali et al. [46]	2011	Daily Life Assistants	App	App with alert buttons and environmental info to cope with anxiety attacks by informing caregiver or playing videos	–	–	–
Niwa et al. [47]	2014	Daily Life Assistants	App	Scheduling app with instructions to tasks, reward system and timer system to support people with ASD	–	–	–
Postawka [48]	2017	Safety & Security	Algorithm	Detection of stereotypical behavior	–	–	–
Rajeswari et al. [49]	2011	Communication & Social Life	Wearable	Generating speech output through movement of lips and tongue for mute people with ASD	–	–	–
Ryu et al. [50]	2017	Daily Life Assistants	App	Self-management tool for controlling behavior and communication with caregiver	–	–	–
Samonte et al. [51]	2020	Communication & Social Life	App	AAC – picture to speech	–	–	–
Schafer et al. [52]	2016	Daily Life Assistants	Wearable	Remote microphone technology for hearing difficulties in people with ASD	12 people with ASD; mean age 9.7 years; parents and teachers	Functionality	Improvements in speech recognition in noise in 8 people, higher auditory memory in 7 people and acceptance of poorer signal-to-noise-ratio in 5 people
Tang and Winoto [53]	2018	Communication & Social Life	App	AAC – picture to speech with context awareness module	–	–	–
Tang et al. [54]	2016	Daily Life Assistants	App, Stationary Device	Daily life support with instructions on performing tasks with ambient sensors for context information	10 people with ASD; mean age 22.7 years; 10 caregivers	Usefulness Ease of use	All participants strongly agreed or agreed (Likert Scale) that the system is useful and easy to use
Toe and Yen [55]	2010	Communication & Social Life	Algorithm	Emotion recognition in real time	–	–	–
Tomczak et al. [56]	2018	Daily Life Assistants	Wearable	Recognition of abnormal stress level and information to person with ASD and co-workers	16 people with ASD; age 15–50 years	Functionality	Similar results of developed system compared to existing system in skin resistance (0.85 vs. 0.96MOhm)
Wang et al. [57]	2013	Communication & Social Life	Wearable	Selection of single speakers (sounds) when in a conversation with multiple people	–	–	–
Xu et al. [58]	2015	Communication & Social Life	Wearable	Speech volume and gaze direction recognition to support people with ASD (in job interviews)	–	–	–

crowded places, automatic simplification of texts on the computer to foster understanding, work assistants to reduce support by others when starting a job and instructions to cope with anxiety attacks. Also tools to support hearing deficits, control behavior and recognizing abnormal stress levels were proposed in the articles. The most commonly supposed technologies (five papers) were supportive systems for activities of daily living like cleaning or cooking. Organizing and scheduling the own life is at least a subgoal of the AT in six of the articles. Two of the papers describe technology specially developed as digital support in working life.

The Safety & Security category comprises technologies for measuring vital parameters, location tracking like geofencing, detection of stereotypical behavior, prediction of (aggressive) behavior, detection of sensory overload and detection of hunchback behavior. The detected parameters or derived information are forwarded to the person with ASD or the caregivers are informed subsequently. In this category the detection of certain behavior (stereotypical behavior like hand flapping or auto-aggressive behavior) is the most abundant AT to support the safety and security among people with ASD. Seven papers describe this type of technology. Contrary to the symptoms of ASD is the detection of vital parameters, as abnormal vital signs are not a common symptom of ASD [1]. Similarly is the detection of hunchback behavior, which is no general symptom of ASD but still proposed by one article [18].

The distribution of the identified papers according to the supported area of life with the AT was very steady between the three categories. Fourteen papers were assigned to each of the categories Communication & Social life and Safety & Security and 12 papers to Daily Life Assistants. The distribution of years the identified papers were published showed an increasing number of papers in recent years (Fig. 2). The most papers were published by far was in 2020, where 9 papers were published, followed by 5 papers in 2019 and 2017. In terms of the objective of the used technology, there were only minor differences between distribution in years except for 2020, where no article was published that dealt with an AT as a daily life assistant.

Figure 2.

Distribution of publications per year and category.

3.2 Type of technology

The type of technology used for the assistive devices were wearables, apps, algorithms and stationary devices. Wearables comprise tools like smart glasses or fitness trackers while apps are used with devices like smartphones or tablets. Algorithms focus on the function itself and are not bound to a certain device. They are for example used to detect certain stereotypical behavior or simplify texts to make them easier understandable. Stationary devices do include environmental sensors and actuators that are not meant to be carried with. Some of the identified articles do use more than one of the herestated technologies.

The most commonly used technologies were wearables and apps with more than three of four articles state one of these as the used technology. An algorithm as the main element of the AT is stated by six of the 40 papers, which means that these technologies are not based on certain devices but the function itself is the main element of the AT. The most underrepresented area is the field of stationary devices, which also includes smart environments/home automation, where only four devices are based on this type of technology.

3.3 Evaluation of the technology

An evaluation of the proposed technology with people with ASD was conducted in 18 of the 40 identified articles. The most often conducted evaluation was the assessment of the functionality of the system. A commonly treated question is if the technology supports the area of life or the tasks that it aims to or detects the respective parameters properly. That comprises for example the increase in reading skills, the proximity between people during conversations or the accuracy of detection of stereotypical behavior. Rather seldomly tested was the acceptance. The technology acceptance comprises the usefulness and ease of use [19], which was tested by four of the stated articles.

4. Discussion

The literature research showed a large interest in the topic of AT for people with ASD in the recent years. Although many articles were identified in the databases searched (900 articles), only a smaller number of papers was actually included in this review. This was mainly due to the objectives of the proposed technology. While many technologies are designed for people with ASD, only a few of them do have the purpose of directly assisting the needs of this particular user group.

The aim of the AT developed within the identified literature reflects the diagnostic criteria according to DSM-V to a large extent. The two main diagnostic criteria are persistent deficits in communication and social interaction and restricted, repetitive patterns of behavior, interests or activities. Especially the communication area was well represented but only a few included the social life aspect with challenges like building relationships or starting conversations. Also well represented was the field of safety and security, where many technologies aim to detect stereotypical behavior. An unexpected area was the detection of vital signs as abnormalities of those are no common symptom of ASD.

A similar picture becomes clear when comparing the results to the main outcomes of autism [2, 3, 4]. Problems with language and speech are centered in many papers, often by AAC devices (5 papers that describe this technology) e.g. through picture-based communication while social relationships are underrepresented in the articles. Behavioral problems are focused by technologies in terms of safety and security, for example by the detection of stereotypical behavior, detection of sensory overload or the prediction of aggressive behavior. Challenges in education and employment, independence and self-care as well as difficulties with adaptive behavior are represented by daily life assistants like planners, task instruction or work assistants.

An underrepresented field are smart homes or home automation for people with autism. While wearable devices such as smart glasses or smart watches/fitness trackers are frequently used hardware and smartphone or tablet apps are also commonly used for AT, stationary devices (as smart homes are) were found only in two of the identified articles. This may be due to the limitation to the own four walls compared to wearables that can be used independently of the location. We do see large benefits in the use of smart homes for people with ASD because it is not necessary to wear or carry devices as this is sometimes not tolerated by them. In terms of ensuring safety for people with ASD, Smart Environments could be useful for a larger spectrum of people with autism because it is more independent of actual resources and barriers in the daily life skills of people with ASD.

More than half of the articles are to a large extent dealing with the technical development of the proposed assistive solutions. An evaluation of the technologies with people with ASD was performed in 18 of the 40 articles. From these papers, the main focus is laid on the functionality of the proposed technology. The actual acceptance and therefore use of AT depends on the perceived usefulness and ease of use of the proposed technologies [19]. These evaluation domains were underrepresented in the identified articles what may be due to the developmental state of the technologies described in the articles. It would be interesting for further research to see these technologies be evaluated with a broad user spectrum.

4.1 Limitations

This scoping review has some limitations. One of them is, that for the identification of the articles broader search terms were used in order to identify as many relevant articles as possible what also entails irrelevant literature. Nevertheless other sources, especially gray literature should be considered in further investigations. The identified articles were often conference papers, which is a common platform for newly developed AT and therefore journal papers were underrepresented. The categorization of the identified articles was in some cases ambiguous, as the described technology would have fit in more categories. In these cases, the main purpose of the AT was taken as the deciding factor. Furthermore, the results of this review are only up to date as of December 2020.

4.2 Conclusion

Forty articles were identified that propose ATs that aim to directly support people on the autism spectrum in the three areas Communication & Social Life, Daily Living Activities and Safety & Security. Eighteen of the articles described a conducted evaluation with people on the autism spectrum, dealing with functional tests to a large extent.

Autism diagnoses are likely to increase in the future [20]. Therefore it is very likely that also the AT sector for people with ASD will advance. When developing new technologies, it should be ensured that people with ASD (and their caregivers) are directly involved in the development but also in previous phases, where resources, barriers and challenges in daily living should be addressed. It is necessary, that the developed solutions are evaluated in the means of usefulness and user experience to ensure, that ATs will be developed that suit the needs of people on the autism spectrum.

Author contributions

CONCEPTION: Lukas Wohofsky, Philip Scharf, Daniela Krainer and Sandra Lisa Lattacher

PERFORMANCE OF WORK: Lukas Wohofsky and Philip Scharf

INTERPRETATION OR ANALYSIS OF DATA: Lukas Wohofsky and Philip Scharf

PREPARATION OF THE MANUSCRIPT: Lukas Wohofsky and Philip Scharf

REVISION FOR IMPORTANT INTELLECTUAL CONTENT: Daniela Krainer and Sandra Lisa Lattacher

SUPERVISION: Lukas Wohofsky

Ethical considerations

This study, as a literature review, is exempt from institutional review board approval.

Footnotes

Acknowledgments

The research leading to this paper is co-financed by the European Regional Development Fund and the Carinthian Economic Promotion Fund (KWF) under the framework of INTERREG IT-AT (SENSHOME project).

Conflict of interest

The authors have no conflicts of interest to report.

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Assistive technology to support people with autism spectrum disorder in their autonomy and safety: A scoping review

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Method

2.1 Search strategy

2.2 Inclusion and exclusion criteria

2.3 Practical implementation

2.4 Categorization of literature

3. Results

Table 1 Overview on identified articles

3.3 Evaluation of the technology

4. Discussion

4.1 Limitations

4.2 Conclusion

Author contributions

Ethical considerations

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Overview on identified articles