Simulations Overcoming Disability

In the last decade, we have seen simulation technology advance at a rapid pace. Some of the applications of this technology that we see today would not have been possible just a couple of months before. As the quality and effectiveness of simulations continues to grow, people have found that this technology is extremely impactful in helping people with disabilities. In the last few years, there have been a lot of new technologies that were released to help aid people with mental health issues to physical mobility problems. Some of the most promising examples include virtual reality (VR) simulations for physical therapy and social skills training (SST) for autistic individuals, augmented reality for visual impairment, and chatbots for mental health. All of these technologies have shown promising results, but there is still a large gap to bridge before this technology is accepted by the general public. There still exist prominent physical and simulation limitations that need to be addressed before this technology can be widely adopted, and in order to overcome them, we must ensure that the disabled community is being properly represented.

Despite the fact that these technologies have not yet been adopted by the public, they have been proven to be extremely effective in aiding people with disabilities. A strong example of this is SightPlus’s electronic head-mounted low vision aid (e-LVA), which is designed to improve the quality of life for people with visual impairments. The device is a headset that takes a video of a user’s surrounding area and uses AR to clearly project it onto the user’s retinas. It was designed to replicate human vision as closely as possible by using low latency to reduce nausea and a high frames per second rate to ensure smoothness (Fearn, 2020). Researchers tested this device on patients admitted into low vision clinics with different eye conditions and found that visual acuity, or the sharpness of vision, improved in all but 1 of the 60 participants (Crosslands et al, 2019). Despite the positive outcome, the number of participants who were willing to use the technology on a regular basis was only 33 of the original 60 participants. So, while there is high efficacy, there is not necessarily a high chance of adoption.

Another application that shows promise is virtual reality intervention. One way that researchers have integrated VR into treatment is using it to simulate social situations for patients with autism. Virtuoso VR is designed to help adults with autism navigate public transport and to provide a safe and controlled space for them to practice their social skills. The Virtuoso training process consists of four stages: skill introduction, 360-degree video, VR training, and real life application (Schmidt et al, 2021). The first phase of testing was done with five patients diagnosed with autism, and the results were positive, with participants indicating high ease of use and both cognitive and physical accessibility (Schmidt et al, 2021). However, since this was only the initial phase of testing, experts have to test the training method on more patients and a broader group of autism patients before they can implement it for the general public. Similarly, virtual reality interventions for physical therapy are becoming increasingly common. A couple of researchers, in collaboration with KairosXR, developed their own VR Physical Therapy (VR-PT) program involving a series of training sessions, games, and a progress dashboard for three common physical therapy exercises. This setup was tested on 15 patients with lower extremity injuries, and the majority of them preferred VR-PT over traditional PT and were even willing to replace the traditional exercises entirely (Reilly et al, 2021). Overall, VR and AR interventions have been received well by people with disabilities, whether it was used as a tool for their daily use, a safe space for them to practice social skills, or even as a motivator to practice rehabilitation.

Solutions to help people improve their health are not limited to VR and AR simulations and can take the form of a chatbot. Chatbots are tools that move beyond simulating an environment and work to simulate human interaction. The use of chatbots to treat mental health issues is becoming prevalent and is actively implemented by Headspace through Ebb. Compared to a therapist, Ebb is designed to provide personalized meditation and stress-relieving recommendations, listen to users discuss their emotions, and respond any time of the day. An Ebb user reported, “We all need support, and when the world around us seems out of control or unreal, turning to someone—or something—is not only useful but also imperative” (Quellman, 2023). This indicates a lot about why chatbots like Ebb are so useful in this application: they replicate human interaction. They mention how turning to Ebb, despite being a “something” is like turning to “someone”. A lot of mental health issues can be prevented or treated with the right support system, and it is often recommended to talk out these problems with another person. Ebb is an effective tool because it simulates that humanlike response, and people find comfort in chatting with it in the same way that they would converse with another human. We know this because chatbots have passed the “Turing Test” back in 2014, or in other words, chatbots have deceived humans into thinking that they are also human (Marino, 2025). This effect, whether conscious or subconscious, can be reflected in chatbots we know today, such as Ebb. The most value lies in using chatbots for mental health when they are able to simulate human interaction.

Knowing that all of these technologies have been proven effective through multiple accounts, why do we still struggle with turning these technologies from ideas into treatment? While all of these technologies show promise, there are some physical limitations that exist when implementing these technologies. SightPlus is a strong example of this, as wearing a device on a daily basis proves to be inconvenient. A major reason provided by the group of participants for not using the headset routinely was that the headset was too heavy and in some cases, nausea inducing (Crossland et al, 2019). While this seems like an obvious concern, it is only a factor that comes into consideration when testing long term usage as opposed to testing for a single experiment. In the long run, convenience is an important consideration to make and is something that is noted by David Chalmers when discussing the future direction of VR technologies. He notes that VR headsets are “bulky”, but he is also optimistic that these are only temporary limitations, as “the headsets will get smaller, and we will transition to glasses, contact lenses, and eventually retinal or brain implants” (Chalmers, 2022). The most important part of his philosophy here is that the immersiveness of these technologies will only increase. The physical feeling of the device is ultimately a part of the illusion, as feeling the weight of the headset takes us out of the simulation. Similarly, if visually impaired individuals are simply trying to go through their daily routine, a heavy device would take away from their quality of life. Simulating vision also involves some physical considerations, which is one improvement technologies like SightPlus would need to work on.

Aside from physical limitations, there are also limitations of representation. This means that the current ability of VR interventions to replicate reality is still limited. For example, SightPlus has some notable lag time between an object being detected by the camera and an object being projected onto the retina (Crossland et al, 2019). Similarly, the VR intervention to promote social skills training amongst autistic individuals has not proved to translate to real life. In this particular situation, an accurate representation of the real world is of utmost importance, as the overall objective is to translate skills learned in simulation into skills practiced in the real world. The paper’s researchers, themselves, have recognized the limitations of their intervention, as they have not tested how the VR skills translate to real world navigation (Schmidt et al, 2021). There will always be factors that cannot be accounted for by the current state of the VR interventions, as a real world environment is unpredictable. In fact, there are significant consequences of having patients with autism practice navigation in the real world, as they will be stressed or unable to respond to certain situations if they do not receive the training for it. This example reveals a more important concern, which is whether VR intervention can be effective if its success relies on simulating reality as closely as possible. In the autism social skills training, the intervention only effectively teaches users to navigate real world situations if the technology successfully simulates the real world. This is especially crucial to consider when we are dealing with physical and mental disability, as the consequences of a misrepresentation of reality can lead to social anxiety or even physical injury. So, understanding that simulations for disabilities are limited by their ability to represent reality, how can we make these technologies more applicable to their target users?

To make simulation solutions for accessibility more widely adopted, the key idea is better representation of the target groups. This includes more thorough research of the group that the technology is aiming to help and overall, a better understanding of the issue they deal with on a daily basis. In general, an accurate understanding of disabilities has always been limited. While researchers have implemented different methods to understand how disability affects individuals, including simulating the disability for themselves, there has not been a strong method to truly highlight how people with disabilities are impacted by their condition. Approaches like using blindfolds to simulate blindness or noise cancelling headphones to simulate deafness are known to undermine the long term effects these disabilities have on individuals (Babinszki, 2023). This conclusion is drawn from the broader idea that misrepresentative data ends up compounding in simulation technologies and aggravating the issue. Studies reveal that biased data can be reflected in the language models (Bender, 2021). Similarly, biased and misrepresentative data on individuals with disabilities leads to lower quality simulations. This idea is directly reflected by Virtuoso VR. Although the intervention provides a space for autistic adults to feel comfortable, researchers found that cybersickness is more prominent in patients with autism than in people without it (Schmidt et al, 2021). Overlooking such a key consideration, once again, had detrimental effects, as cybersickness was a key limitation noted in the long term implementation of Virtuoso VR. By having a stronger understanding of the patients they were attempting to help, Virtuoso VR’s developers could have mitigated the negative effects before testing the solution. So, while many limitations continue to exist, representation is a strong starting point.

Pushing for better representation of disabled communities, or by simply having a stronger understanding of the problem that is being solved, we can achieve much more effective simulation solutions for treating disability. Simulations are created for a certain purpose, but in order to achieve this purpose, there is a lot of consideration that must be put in before it can be implemented. While simulations have shown significant advancement in the last decade, it is still important to note that translating simulation to society requires much more research. Developers need to ensure that the technology is truly immersive and ensure that they have a strong grasp of the problem space they are trying to solve. Only once this understanding is reached can this technology reach a state that appeals to the general public. Then, we can see a space where simulations are successfully used to treat individuals with disability.

Works Cited

Babinszki, Tom. (2023, May 1). Rethinking Disability Simulation Activities: Moving from Empathy to Informed Action. Level Access. https://www.levelaccess.com/blog/rethinking-disability-simulation-activities-moving-from-empathy-to-informed-action/.

Bender, E., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March 1). On the dangers of stochastic parrots: Can language models be too big? FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623. https://doi.org/10.1145/3442188.3445922.

Chalmers, D. (2022). Is this the Real Life? Reality+: Virtual Worlds and the Problems of Philosophy. W.W. Norton.

Crossland MD, Starke SD, Imielski P, Wolffsohn JS, & Webster AR. (2019). Benefit of an electronic head-mounted low vision aid. Ophthalmic Physiol Opt 2019, 39, 422–431. https://doi.org/10.1111/opo.12646.

Fearn, Nicholas. (2020, January 8). How VR Is Helping Visually Impaired Patients Regain Close To Normal Levels Of Sight. Forbes. https://www.forbes.com/sites/nicholasfearn/2020/01/08/how-vr-is-helping-visually-impaired-patients-regain-close-to-normal-levels-of-sight/.

Marino, Mark. (2025). The programs that followed ELIZA. Please Go On. MIT Press.

Quellman, C. (2023, February 10). I tried an AI companion to help me process my emotions and found it helpful. CNET. https://www.cnet.com/tech/services-and-software/i-tried-an-ai-companion-to-help-me-process-my-emotions-and-found-it-helpful/.

Reilly, C. A., Greeley, A. B., Jevsevar, D. S., & Gitajn, I. L. (2021). Virtual reality-based physical therapy for patients with lower extremity injuries: feasibility and acceptability. OTA international: the open access journal of orthopaedic trauma, 4(2), e132. https://doi.org/10.1097/OI9.0000000000000132.

Schmidt, M. M., & Glaser, N. (2021). Piloting an adaptive skills virtual reality intervention for adults with autism: Findings from user-centered formative design and evaluation. Journal of Enabling Technologies, 15(3), 137-158. doi:https://doi.org/10.1108/JET-09-2020-0037.