Assistive technologies have come a long way in recent years, with Artificial Intelligence (AI) revolutionizing the way people with disabilities access and interact with the technology around them. From voice-activated assistants to eye-tracking systems, assistive technologies are helping people with disabilities overcome the challenges they face every day. In this article, we will be taking a deep dive into AI in assistive technologies, exploring the impact it is having on people’s lives, and looking at some real-life examples.
## What is AI in Assistive Technologies?
AI in assistive technologies involves the use of advanced algorithms and machine learning to assist people with disabilities. These technologies can recognize and respond to user behavior, making them incredibly intuitive and user-friendly. They can also learn from the user, adapting to their individual needs and preferences over time.
One of the most innovative applications of AI in assistive technologies is natural language processing (NLP). NLP enables computers to understand and respond to human language, making it possible for people with speech disabilities to communicate more effectively. This technology is used in devices such as voice-activated assistants, which allow users to control other technology in their environment hands-free. Similarly, machine vision technologies can enable users with limited mobility to control their surroundings using their gaze alone.
## Impact on People’s Lives
AI in assistive technologies is having a significant impact on people’s lives, enabling them to live more independently and making it easier to navigate the world around them. For example, one of the most significant benefits of this technology is improved communication. People with speech disabilities can use devices that employ NLP to communicate with others, opening up more opportunities for education, work, and social interaction.
Another significant benefit of AI in assistive technologies is greater mobility. People who would otherwise be unable to move around freely due to mobility impairments can use devices such as powered wheelchairs that utilize machine vision to navigate their surroundings, avoiding obstacles in real-time.
## Real-life Examples
There are already many AI-powered assistive technologies on the market today that are making significant improvements to the lives of people with disabilities. Here are just a few examples:
### Eye-tracking Systems
Eye-tracking systems utilize machine vision to track the user’s eye movements and respond accordingly. One of the most common applications of this technology is in assistive communication technology. For example, devices such as the Tobii Dynavox EyeMobile Plus allow users with speech and motor disabilities to type text and control other devices entirely using their eyes.
### Smart Home Hub
Smart home hubs such as Google Nest Hub and Amazon Echo can connect with other smart devices in the user’s home, enabling people with disabilities to control their surroundings easily. For example, a user with limited mobility could use a smart hub to turn on the lights, adjust the temperature, or control their entertainment system, all without leaving their chair.
### Autonomous Wheelchair
Autonomous wheelchairs are an exciting development in assistive technology. These devices use machine vision to navigate the user around their environment, avoiding obstacles in real-time. Users with mobility impairments can control the wheelchair using only their gaze, enabling them to move freely around their surroundings. An example of such an innovation is the WHILL Autonomous Drive system, which uses cameras, sensors, and a sophisticated AI algorithm to provide a safe and reliable experience.
## Conclusion
AI in assistive technologies is revolutionizing the way people with disabilities interact with the world around them. These technologies are making it easier to communicate, move around, and access the technology we all rely on every day. With continued innovation and investment, we can expect to see even more exciting developments in this field in the years to come, extending the reach of a use case that is slowly transitioning beyond the realm of niche applications.