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Small gestures, big impact: Google ATAP’s latest work

Garrison Redd, a male individual who uses a wheelchair, pulls a strength band from the ceiling of a gym. He is surrounded by various athletic weights and equipment.
Garrison Redd trains at his gym

Google is dedicated to making tech accessible for everyone, and our hardware innovation division, Google ATAP, is working on this as well. As part of a Jacquard (a connected apparel platform) research project, we at ATAP worked closely with members and advocates of the disability community to understand how advanced wearable technologies, like smart textiles, gesture interfaces and on-device AI can help more people.

Earlier this year, we worked with members of Champions Place, a shared living residence for young adults with disabilities, to better understand why existing technologies sometimes fall short of meeting the full needs of people with mobility and dexterity disabilities.

This research inspired us to use Jacquard technology to create a soft, interactive patch or sleeve that allows people to access digital, health and security services with simple gestures. This woven technology can be worn or positioned on a variety of surfaces and locations, adjusting to the needs of each individual. 

We teamed up with Garrison Redd, a Para powerlifter and advocate in the disability community, to test this new idea. 

Garrison’s feedback has been invaluable, and he’s shared some of his favorite functions. “The selfie option is helpful as far as creativity,” Garrison says. “If I’m in the gym and have the armband on I can capture images from a proper angle for my coach and the training staff, without having to wheel into position, which isn’t ideal. So that does increase my independence, which is important for individuals who have disabilities.” He also pointed out areas where we could improve. “It’s important that the surface can be sensitive to one or two fingers for people who may have more needs than I have.”

We hugely benefited from Garrison’s background and expertise, and implemented his feedback into our work. For example, we’re now developing machine learning models for gesture recognition that adapt over time to each person's unique dexterity. This will allow people with different levels of motor disabilities to use simple gestures to do things like call someone, or order a rideshare service. These might seem like incremental steps forward, but as Garrison says, “It’s the small things that make a difference between being dependent and independent.”

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