2012 was a big year for Great Britain. The country was awash with national pride. Union Jacks flying at full mast across most British streets and a feverish party atmosphere. All while we hosted the best possible Olympic Games. During the games, we saw public recognition for the athleticism of disabled athletes. Friends down the pub are now just as likely to debate that Tanni Grey-Thompson, Ellie Simmonds and Lee Pearson are among Britain’s best Olympians.
As a result of the games, we are now seeing society becoming far more understanding and inclusive of the disabled community. The United Nations reports that around 15% of the world’s population suffer from some type of disability. This leaves a growing feeling that we need to do more, to help make the lives of people with disabilities easier.
Data science and artificial intelligence have come to the forefront of technology in the last few years. Several practitioners are taking a more philanthropic outlook on life. Supporting people suffering from both physical and mental disabilities.
Autism Spectrum Disorder
One of the areas where Machine Learning is playing a prominent role is the support of people suffering from Autism Spectrum Disorder (ASD). Approximately 1 in every 100 people suffer from this condition, with more men diagnosed than women. It affects children at around age 3, where they have difficulty processing or engaging in human interaction or emotion. This can make integrating into groups of other children very difficult. However, the London Knowledge Lab was successful in introducing a group of ASD children to a virtual autonomous robot called Andy. The experiment found that the children interacted with Andy. They listened intently, asking and answering questions far more freely than if it had been a human adult. Other companies are following the same suit, with the roll-out of the artificial intelligence robot Milo. Milo is in over 50 schools in the United States to encourage children with ASD to interact more readily face to face. In some cases, children even permit physical contact from the robot; unthinkable for the majority of ASD sufferers. While there is no recognised cure for ASD at present, the experiment offers hope to the families of children and adults suffering from ASD. If these children are comfortable conversing with AI software, there is hope that they could grow to interact more with other people.
Prosthetics
The implementation of intelligent prosthetics is another area in which Data Science is making the lives of disabled people easier. For many amputees or people living without the use of one or more limbs, options are very limited. They are often very rudimentary, focussing on the bare essentials rather than everyday usability. This is where Deka Research can help. Headed up by Segway inventor Dean Kamen, Deka has pioneered a prosthesis they call the ‘Luke’ arm (named in tribute after Luke Skywalker). The unique aspect of this piece of technology is that it’s modelled on the internal workings of a human arm. It resembles a likeness to human tendons, muscles and bones, allowing the user to have a greater range of natural motion. This is a huge improvement on the hook and claw mechanisms that have been more prevalent in the last 20 years. You can control the arm in several ways: with microscopic nerve endings attached to the base of the arm, or controllers in the wearer’s shoes. The project gained a significant financial boost from the US Army Research Office. Despite this, the prostheses are still likely to be incredibly expensive to produce on a mass scale. Some estimates are around the $50,000 mark. This may not be the answer for everyone who needs a prosthetic, but the range of movement and sophistication of technology on show here is a promising sign for the future. There is hope that similar technologies will become available for less cost, potentially with even more uses.
Nervous System Disorders
The electronic augmentative and alternative communication system (AAC) came to prominence via Professor Stephen Hawking. It has become integral to the lives of people who are unable to speak. Conditions such as motor neurone disease or cerebral palsy are often responsible for this. One of the latest innovations in this market is the DynaVox EyeMax. It is closely related to the ‘Equaliser’ device used by Hawking from 1986 onwards. This device uses Computer Vision techniques via a front-facing camera to track the movements of the user’s eyes across the screen of commands. It is often programmed to use certain intonation in order to not misconstrue speech. It has the potential to use Natural Language Processing and give speech to a large number of people who suffer from a disability of this nature. But there are some limitations to the technology. Each device must be individually programmed for the respective user. This includes places of interest, names of friends and family and other unique information. Another limitation is that the cost of the devices is rarely covered by health insurance. The incorporation of deep learning technology into devices in the future should reduce the time taken to program the device. This in turn could bring the cost of the devices down to a more affordable level.
How can we boost accessibility?
It is not only specialist medical firms that have seen the value of using data science technologies to improve the lives of disabled people. Google recently announced that they are going to boost the accessibility of their smartphones and other devices. This will benefit users who might have limited or no use of their sight, hearing or dexterity. One of the most exciting and unique developments is a Braille system for Google smartphones. Users can connect a Braille device to their phone via Bluetooth. In addition, Android systems are now controlled by ‘switches’. They are not dissimilar to those used by Stephen Hawking in his wheelchair. According to Google, this opens them up to new groups of users who otherwise would not have been able to access this technology. Users now have access to the same hardware as their friends and peers. No more custom-made and impractical technology! There is a huge financial incentive to these developments, as both Switch Access and BrailleBack are free to download for Android. This means that unlike many technologies designed for the disabled community, price does not have to be a barrier. Overall, Google’s contribution has set a benchmark for other technology companies to follow. Producing affordable and easy-to-use platforms for those with disabilities.
Autonomous Driving
Finally, there is something that is definitely more at the ‘proof of concept’ stage right now but shows enormous potential for the future. In 2011 Dr Dennis Hong, from Virginia Tech University’s RoMeLa Robotics and Mechanisms Laboratory, pioneered a car designed for blind or partially-sighted people to be able to drive independently. This car uses machine learning to learn pre-determined routes and sense obstacles such as pedestrians. Combined with a series of sensors packed into pressure-pad gloves, this will tell the driver if there is an obstacle ahead. During his TED talk in the same year, Dr Hong demonstrated the car with a blind person driving. They completed a whole lap of a pre-determined course safely at the Daytona Speedway. Of course there are a few reasons why this system is not implemented immediately. Least of which is that the car’s software took time to learn the route in order for the sensors to direct the driver. This is an enormous task to teach a car the twists and turns of every road globally. Roadworks, unexpected obstacles and car malfunctions are all risks that could result in injury or fatality. However, it shows great progress in the field of Data Science and Machine Learning. Most importantly, a huge leap forward for the blind and partially sighted community as a whole.
To be able to label the relationship between data science and disability a success, we must address several questions:
1. Is technology advanced enough to benefit those who are suffering from a disability now? Or is the real breakthrough likely to come in 5-10 years?
2. Are these innovations financially viable for consumers, who might have a limited income?
3. Is data science limited to making the lives of disabled people easier, or can we use deep learning in other ways? For example, to identify genes that cause certain disabilities, giving doctors the chance to study and limit these genes in the future?
The future for Data Science looks bright, already positively impacting the lives of disabled people around the world. The next step, whether that be into genetics research or another field, depends on whether companies will look past the profits and instead focus on improving life. Let’s hope it doesn’t take until the next Olympic Games until they do.
