This paper gives a brief overview of currently existing assistive technologies for different kinds of disabilities. An elaborate discussion of all types of assistive technologies is beyond the scope of this paper. Assistive technologies have evolved dramatically in recent years and will continue to be further developed thanks to major progress in artificial intelligence, machine learning, robotics, and other areas. Previously, assistive technologies were highly specialized and were often difficult or expensive to acquire. Today, however, many assistive technologies are included in mainstream products and services. An introduction and state of the art of assistive technologies are presented first. These are followed by an overview of technological trends in assistive technologies and a conclusion

Darvishy, Alireza

English

ZHAW digitalcollection

Proceedings of the 1st Swiss Conference on Barrier-free Communication, ZHAW, Winterthur 2017 DOI 10.21256/zhaw-3000     52 Alireza Darvishy Assistive Technologies: Short Overview and Trends Abstract This paper gives a brief overview of currently existing assistive technologies for different kinds of disabilities. An elaborate discussion of all types of assistive technologies is beyond the scope of this paper. Assistive technologies have evolved dramatically in recent years and will continue to be further developed thanks to major progress in artificial intelligence, machine learning, robotics, and other areas. Previously, assistive technologies were highly specialized and were often difficult or expensive to acquire. Today, however, many assistive technologies are included in mainstream products and services. An introduction and state of the art of assistive technologies are presented first. These are followed by an overview of technological trends in assistive technologies and a conclusion. 1 Introduction Assistive technologies (ATs) are any item, piece of equipment, software program, or product system that is used to increase, maintain, or improve the functional capabilities of persons with disabilities (PWDs). Assistive technology helps people with a variety of impairments, be it difficulty speaking, typing, writing, remembering, pointing, seeing, hearing, learning, or walking. ATs can come in many forms, from low-tech to high-tech. For the context of this paper, we will consider only ICT-based assistive technologies, such as computer hardware (e.g. special switches, keyboards, and pointing devices) and software (e.g. screen readers and communication programs). Due to the advancement of computer technologies, assistive technologies have become both more affordable and more sophisticated in recent years. For instance, refreshable Braille displays and screen readers for visually impaired users have changed dramatically. The first refreshable Braille display for visually impaired people, called VersaBraille, was developed in 1982, and was very expensive (Van Gerven/Taylor 2009). In 1986, IBM created the first screen reader for DOS computers (Cooke 2004) and in 1989, Macintosh released the first screen reader for graphical user interfaces called OutSpoken. Today, sophisticated screen readers are already embedded into most operating systems and mainstream computer devices for free (e.g. VoiceOver for Apple operating systems). 2 Assistive technologies: State of the art A great many ATs today are based on existing mainstream technologies, particularly wireless Internet and mobile devices. Some ATs are already embedded into these technologies, such as the accessibility settings on an iPhone. There are also many specialized tools based on mainstream Internet technologies developed specifically for PWDs. For example, assistive mobile apps, also called “accessibility apps,” have sharply increased in number in recent years. Navigation and map apps such as Wayfindr (n.d.), ViaOpta (Novartis Pharma AG 2014), and WheelMap (Sozialhelden e.V. n.d.) allow users with mobility and/or vision impairments to locate and navigate accessible places. Apps such as these might use GPS information to analyse the user’s location and recommend safe routes, or they may rely on crowdsourcing to identify and rate accessibility in various establishments, such as restaurants, shops, or public buildings. Remote person-to-person apps, such as Convo (2017), VEASYT (2018), Be My Eyes (n.d.), and VerbaVoice connect users with hearing and vision impairments to a volunteer or interpreter, who receives a live camera and microphone feed from the user’s device and can then assist the user with a given task or provide sign language interpretation. There is a plethora of other internet-based mobile apps that offer a range of assistive tools. Webtools and online platforms also offer many services for PWDs, particularly services which are more complex, as these would be less well-suited to the simpler formats of mobile apps. Alireza Darvishy 53 Assistive Technologies: Short Overview and Trends  Examples of webtools for PWDs include Amovil (n.d.), a tool which helps users identify the best-fitted accessible mobile device for them, PAVE (ZHAW ICT-Accessibility Lab 2016), which assists document creators in making accessible PDFs, and Robobraille (Sensus ApS. 2018), an online service which automatically translates text into Braille. Other types of more hardware-based assistive technologies help people with cognitive and hearing impairments: so-called “social robots,” such as Pepper (Emotion Robotics n.d.), can help people on the autism spectrum to improve their social skills. Specialized hearing devices in combination with Bluetooth and cloud-based services, such as Oticon Opn (Oticon 2018), can help people with hearing impairments to overcome some barriers in their daily lives.  3 Assistive technology trends Different areas such as cloud-based technologies, the brain-computer interface (BCI), artificial intelligence (AI), machine learning, and the Internet of Things (IoT) are facilitating new types of assistive technologies for people with disabilities. Cloud-based assistive technologies, as their name implies, are stored in the Cloud and can be used as needed, independent of location. A European Commission-funded project, “Cloud4All,” has launched an initiative called the Global Public Inclusive Infrastructure (GPII), which aims to build a cloud-based system where users can store customized AT software. This initiative would allow users with disabilities to access their preferred ATs from any device and location. For example, a user with a cognitive impairment who uses a simplified computer interface could have these settings saved in the cloud, and could later access them from other devices, such as a library computer or work device. The GPII initiative is still under development, but it could have great potential in making ICTs more accessible for PWDs. The brain-computer interface is at the cutting edge of AT research, and may have a significant impact for persons with motor impairments. The BCI allows users to bypass conventional channels of communication (such as speech or gestures) by creating a direct connection between a user’s brain and a computer via electrodes or implants (Obiedat et. al 2014). The user can thus control a device, such as a wheelchair or computer, using only brain-generated electrophysiological signals. Artificial intelligence and machine learning, two distinct but intertwined phenomena (Marr 2016), have become buzz words in recent years. Artificial intelligence (AI) refers to the ability of machines to carry out tasks in a “smart” way, while machine learning refers to the application of AI when computers are given access to data and programmed to learn for themselves. For example, machine learning allows computers to draw from vast databases in order to “understand” certain external stimuli, such as images, text, and sounds. The increasing ability of computers to understand the world has many potential uses for PWDs. YouTube’s automated closed-captions are one major example of applied machine learning as an assistive technology. As of 2017, YouTube’s closed-captioning algorithms not only recognize speech with a high level of accuracy, but are also able to recognize other sounds such as applause, laughter, and music (Simonite 2017). Another example is Facebook, which recently launched an image recognition feature, allowing the platform to create text descriptions of images in a post, for example. Google has also developed its own algorithm for this task (Vinyals 2014).   Alireza Darvishy 54 Assistive Technologies: Short Overview and Trends  The “Internet of Things” (IoT) is expected to gain importance for assistive technologies. The IoT is what allows “smart” devices to operate autonomously via remote communication and data transmission with other devices and systems. For example, a user could use their smartphone to control the lighting in their home. The IoT can be applied to anything from transportation (such as self-driving wheelchairs or Google’s self-driving car), to security systems, to everyday objects such as refrigerators. An emerging field of development is the “smart home,” which combines various smart objects and systems in a domestic setting, allowing a user to have full control of their comfort and safety. Another emerging concept is that of “smart cities,” in which urban infrastructure is connected to the Internet. In this scenario, a PWD could use an app to find a free parking spot, for example, or could use a virtual city guide to find accessible building entrances. 4 Conclusions Assistive technologies play an important role for the inclusion of people with disabilities. In earlier times, assistive technologies were often highly specialized, expensive, and not widely available. Today, tremendous advances in information and communication technologies have made assistive technologies more affordable and easier to acquire than ever before. Assistive technologies are increasingly ubiquitous, and are frequently included in mainstream technologies. Advances in artificial intelligence, machine learning and computer vision are providing new opportunities to develop novel assistive technologies. Self-driving cars, robotics, and 3D printing facilitate new use cases for PWDs.   Alireza Darvishy 55 Assistive Technologies: Short Overview and Trends  Sources Amóvil (n.d.): “Collaborating to update accessible mobile devices.” http://www.amovil.es/en (24 April 2018) Be My Eyes (n.d.): “Bringing sight to blind and low vision people.” https://www.bemyeyes.com/ (24 April 2018) Convo (2017): “Video Relay Service.” https://www.convorelay.com/ (24 April 2018) Cooke, Annemarie (2004): “A History of Accessibility at IBM.” AccessWorld. American Foundation for the Blind. http://www.afb.org/afbpress/pub.asp?DocID=aw050207 (24 April 2018) Emotion Robotics (2017): “Emotion Robotics.” http://www.emotion-robotics.com/Robotics-For-Education.aspx (24 April 2018) Marr, Bernard (2016): “What is the Difference Between Artificial Intelligence and Machine Learning?” Forbes online – https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#7bbb37b32742 (24 April 2018) Novartis Pharma AG (2014): “ViaOpta.” https://www.viaopta-apps.com/ (24 April 2018) Obiedat, Qussai; Maysam Ardehali; Roger Smith (2014): “EEG Brain-Computer Interface as an Assistive Technology: Adaptive Control and Therapeutic Intervention.” Resna Annual Conference 2017. https://www.resna.org/sites/default/files/conference/2017/emerging_technology/Obiedat.html (11 May 2018) Oticon (2018): “Oticon Opn. Hearing aids.” https://www.oticon.com/solutions/opn (24 April 2018) Sensus ApS. (2018): “RoboBraille.” https://www.robobraille.org/ (24 April 2018) Simonite, Tom (2017): “Machine Learning Opens up New Ways to Help People with Disabilities.” MIT Technology Review – https://www.technologyreview.com/s/603899/machine-learning-opens-up-new-ways-to-help-disabled-people/ (24 April 2018) Sozialhelden e.V. (n.d.): “Wheelmap. Rollstuhlgerechte Orte finden.” https://wheelmap.org (24 April 2018) Van Gerven, Clara; Taylor, Anne (2009): “The Information Age Braille Technology Timeline.” Future Reflections 28 [1] – https://nfb.org/Images/nfb/Publications/fr/fr28/fr280109.htm (24 April 2018) VEASYT (2018): “Digital solutions for content accessibility.” http://www.veasyt.com/ (24 April 2018) VerbaVoice (n.d.): “Dolmetscher online zuschalten.” https://www.verbavoice.de/ (24 April 2018) Vinyals, Oriol; Alexander Toshev; Samy Bengio; Dumitru Erhan (2014): “A Picture Is Worth a Thousand (coherent) Words: Building a Natural Description of Images.” 17 November – http://googleresearch.blogspot.co.uk/2014/11/a-picture-is-worth-thousand-coherent.html (24 April 2018) Wayfindr (n.d.): “Accessible Indoor Audio Navigation.” https://www.wayfindr.net/ (24 April 2018) ZHAW ICT-Accessibility Lab (2016): “PAVE. Accessible PDF.” http://pave-pdf.org/ (24 April 2018) Author Alireza Darvishy is a Professor and Head of the ICT Accessibility Lab, a national competence center on eAccessibility at the Zurich University of Applied Sciences in Switzerland. E-mail: Alireza.Darvishy@zhaw.ch 

Assistive technologies : short overview and trends

Proceedings of the 1st Swiss Conference on Barrier-free Communication, ZHAW, Winterthur 2017 
DOI 10.21256/zhaw-3000 
 
 
  52 
Alireza Darvishy 
Assistive Technologies: Short Overview and Trends 
Abstract 
This paper gives a brief overview of currently existing assistive technologies for different kinds of 
disabilities. An elaborate discussion of all types of assistive technologies is beyond the scope of this 
paper. Assistive technologies have evolved dramatically in recent years and will continue to be further 
developed thanks to major progress in artificial intelligence, machine learning, robotics, and other areas. 
Previously, assistive technologies were highly specialized and were often difficult or expensive to 
acquire. Today, however, many assistive technologies are included in mainstream products and 
services. An introduction and state of the art of assistive technologies are presented first. These are 
followed by an overview of technological trends in assistive technologies and a conclusion. 
1 Introduction 
Assistive technologies (ATs) are any item, piece of equipment, software program, or product 
system that is used to increase, maintain, or improve the functional capabilities of persons with 
disabilities (PWDs). Assistive technology helps people with a variety of impairments, be it 
difficulty speaking, typing, writing, remembering, pointing, seeing, hearing, learning, or walking. 
ATs can come in many forms, from low-tech to high-tech. For the context of this paper, we will 
consider only ICT-based assistive technologies, such as computer hardware (e.g. special 
switches, keyboards, and pointing devices) and software (e.g. screen readers and 
communication programs). 
Due to the advancement of computer technologies, assistive technologies have become both 
more affordable and more sophisticated in recent years. For instance, refreshable Braille 
displays and screen readers for visually impaired users have changed dramatically. The first 
refreshable Braille display for visually impaired people, called VersaBraille, was developed in 
1982, and was very expensive (Van Gerven/Taylor 2009). In 1986, IBM created the first screen 
reader for DOS computers (Cooke 2004) and in 1989, Macintosh released the first screen 
reader for graphical user interfaces called OutSpoken. Today, sophisticated screen readers 
are already embedded into most operating systems and mainstream computer devices for free 
(e.g. VoiceOver for Apple operating systems). 
2 Assistive technologies: State of the art 
A great many ATs today are based on existing mainstream technologies, particularly wireless 
Internet and mobile devices. Some ATs are already embedded into these technologies, such 
as the accessibility settings on an iPhone. There are also many specialized tools based on 
mainstream Internet technologies developed specifically for PWDs. For example, assistive 
mobile apps, also called “accessibility apps,” have sharply increased in number in recent years. 
Navigation and map apps such as Wayfindr (n.d.), ViaOpta (Novartis Pharma AG 2014), and 
WheelMap (Sozialhelden e.V. n.d.) allow users with mobility and/or vision impairments to 
locate and navigate accessible places. Apps such as these might use GPS information to 
analyse the user’s location and recommend safe routes, or they may rely on crowdsourcing to 
identify and rate accessibility in various establishments, such as restaurants, shops, or public 
buildings. Remote person-to-person apps, such as Convo (2017), VEASYT (2018), Be My 
Eyes (n.d.), and VerbaVoice connect users with hearing and vision impairments to a volunteer 
or interpreter, who receives a live camera and microphone feed from the user’s device and 
can then assist the user with a given task or provide sign language interpretation. There is a 
plethora of other internet-based mobile apps that offer a range of assistive tools. 
Webtools and online platforms also offer many services for PWDs, particularly services which 
are more complex, as these would be less well-suited to the simpler formats of mobile apps. 
Alireza Darvishy 53 
Assistive Technologies: Short Overview and Trends 
 
Examples of webtools for PWDs include Amovil (n.d.), a tool which helps users identify the 
best-fitted accessible mobile device for them, PAVE (ZHAW ICT-Accessibility Lab 2016), 
which assists document creators in making accessible PDFs, and Robobraille (Sensus ApS. 
2018), an online service which automatically translates text into Braille. 
Other types of more hardware-based assistive technologies help people with cognitive and 
hearing impairments: so-called “social robots,” such as Pepper (Emotion Robotics n.d.), can 
help people on the autism spectrum to improve their social skills. Specialized hearing devices 
in combination with Bluetooth and cloud-based services, such as Oticon Opn (Oticon 2018), 
can help people with hearing impairments to overcome some barriers in their daily lives.  
3 Assistive technology trends 
Different areas such as cloud-based technologies, the brain-computer interface (BCI), artificial 
intelligence (AI), machine learning, and the Internet of Things (IoT) are facilitating new types 
of assistive technologies for people with disabilities. 
Cloud-based assistive technologies, as their name implies, are stored in the Cloud and can be 
used as needed, independent of location. A European Commission-funded project, 
“Cloud4All,” has launched an initiative called the Global Public Inclusive Infrastructure (GPII), 
which aims to build a cloud-based system where users can store customized AT software. This 
initiative would allow users with disabilities to access their preferred ATs from any device and 
location. For example, a user with a cognitive impairment who uses a simplified computer 
interface could have these settings saved in the cloud, and could later access them from other 
devices, such as a library computer or work device. The GPII initiative is still under 
development, but it could have great potential in making ICTs more accessible for PWDs. 
The brain-computer interface is at the cutting edge of AT research, and may have a significant 
impact for persons with motor impairments. The BCI allows users to bypass conventional 
channels of communication (such as speech or gestures) by creating a direct connection 
between a user’s brain and a computer via electrodes or implants (Obiedat et. al 2014). The 
user can thus control a device, such as a wheelchair or computer, using only brain-generated 
electrophysiological signals. 
Artificial intelligence and machine learning, two distinct but intertwined phenomena (Marr 
2016), have become buzz words in recent years. Artificial intelligence (AI) refers to the ability 
of machines to carry out tasks in a “smart” way, while machine learning refers to the application 
of AI when computers are given access to data and programmed to learn for themselves. For 
example, machine learning allows computers to draw from vast databases in order to 
“understand” certain external stimuli, such as images, text, and sounds. 
The increasing ability of computers to understand the world has many potential uses for PWDs. 
YouTube’s automated closed-captions are one major example of applied machine learning as 
an assistive technology. As of 2017, YouTube’s closed-captioning algorithms not only 
recognize speech with a high level of accuracy, but are also able to recognize other sounds 
such as applause, laughter, and music (Simonite 2017). Another example is Facebook, which 
recently launched an image recognition feature, allowing the platform to create text 
descriptions of images in a post, for example. Google has also developed its own algorithm for 
this task (Vinyals 2014). 
  
Alireza Darvishy 54 
Assistive Technologies: Short Overview and Trends 
 
The “Internet of Things” (IoT) is expected to gain importance for assistive technologies. The 
IoT is what allows “smart” devices to operate autonomously via remote communication and 
data transmission with other devices and systems. For example, a user could use their 
smartphone to control the lighting in their home. The IoT can be applied to anything from 
transportation (such as self-driving wheelchairs or Google’s self-driving car), to security 
systems, to everyday objects such as refrigerators. An emerging field of development is the 
“smart home,” which combines various smart objects and systems in a domestic setting, 
allowing a user to have full control of their comfort and safety. Another emerging concept is 
that of “smart cities,” in which urban infrastructure is connected to the Internet. In this scenario, 
a PWD could use an app to find a free parking spot, for example, or could use a virtual city 
guide to find accessible building entrances. 
4 Conclusions 
Assistive technologies play an important role for the inclusion of people with disabilities. In 
earlier times, assistive technologies were often highly specialized, expensive, and not widely 
available. Today, tremendous advances in information and communication technologies have 
made assistive technologies more affordable and easier to acquire than ever before. Assistive 
technologies are increasingly ubiquitous, and are frequently included in mainstream 
technologies. Advances in artificial intelligence, machine learning and computer vision are 
providing new opportunities to develop novel assistive technologies. Self-driving cars, robotics, 
and 3D printing facilitate new use cases for PWDs. 
  
Alireza Darvishy 55 
Assistive Technologies: Short Overview and Trends 
 
Sources 
Amóvil (n.d.): “Collaborating to update accessible mobile devices.” http://www.amovil.es/en (24 April 
2018) 
Be My Eyes (n.d.): “Bringing sight to blind and low vision people.” https://www.bemyeyes.com/ (24 
April 2018) 
Convo (2017): “Video Relay Service.” https://www.convorelay.com/ (24 April 2018) 
Cooke, Annemarie (2004): “A History of Accessibility at IBM.” AccessWorld. American Foundation for 
the Blind. http://www.afb.org/afbpress/pub.asp?DocID=aw050207 (24 April 2018) 
Emotion Robotics (2017): “Emotion Robotics.” http://www.emotion-robotics.com/Robotics-For-
Education.aspx (24 April 2018) 
Marr, Bernard (2016): “What is the Difference Between Artificial Intelligence and Machine Learning?” 
Forbes online – https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-
between-artificial-intelligence-and-machine-learning/#7bbb37b32742 (24 April 2018) 
Novartis Pharma AG (2014): “ViaOpta.” https://www.viaopta-apps.com/ (24 April 2018) 
Obiedat, Qussai; Maysam Ardehali; Roger Smith (2014): “EEG Brain-Computer Interface as an 
Assistive Technology: Adaptive Control and Therapeutic Intervention.” Resna Annual Conference 
2017. https://www.resna.org/sites/default/files/conference/2017/emerging_technology/Obiedat.html 
(11 May 2018) 
Oticon (2018): “Oticon Opn. Hearing aids.” https://www.oticon.com/solutions/opn (24 April 2018) 
Sensus ApS. (2018): “RoboBraille.” https://www.robobraille.org/ (24 April 2018) 
Simonite, Tom (2017): “Machine Learning Opens up New Ways to Help People with Disabilities.” MIT 
Technology Review – https://www.technologyreview.com/s/603899/machine-learning-opens-up-
new-ways-to-help-disabled-people/ (24 April 2018) 
Sozialhelden e.V. (n.d.): “Wheelmap. Rollstuhlgerechte Orte finden.” https://wheelmap.org (24 April 
2018) 
Van Gerven, Clara; Taylor, Anne (2009): “The Information Age Braille Technology Timeline.” Future 
Reflections 28 [1] – https://nfb.org/Images/nfb/Publications/fr/fr28/fr280109.htm (24 April 2018) 
VEASYT (2018): “Digital solutions for content accessibility.” http://www.veasyt.com/ (24 April 2018) 
VerbaVoice (n.d.): “Dolmetscher online zuschalten.” https://www.verbavoice.de/ (24 April 2018) 
Vinyals, Oriol; Alexander Toshev; Samy Bengio; Dumitru Erhan (2014): “A Picture Is Worth a 
Thousand (coherent) Words: Building a Natural Description of Images.” 17 November – 
http://googleresearch.blogspot.co.uk/2014/11/a-picture-is-worth-thousand-coherent.html (24 April 
2018) 
Wayfindr (n.d.): “Accessible Indoor Audio Navigation.” https://www.wayfindr.net/ (24 April 2018) 
ZHAW ICT-Accessibility Lab (2016): “PAVE. Accessible PDF.” http://pave-pdf.org/ (24 April 2018) 
Author 
Alireza Darvishy is a Professor and Head of the ICT Accessibility Lab, a national competence center on 
eAccessibility at the Zurich University of Applied Sciences in Switzerland. 
E-mail: Alireza.Darvishy@zhaw.ch 


https://digitalcollection.zhaw.ch/bitstream/11475/15671/3/2018_Darvishy_Assistive%20Technologies_Proceedings%20Barrier-free%20Communication.pdf

Assistive technologies : short overview and trends

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