Personalising digital interfaces over time with continuous learning of usage habits
- There is such diversity between services, interfaces and the people using them that managing all of this poses a major challenge in the field of digital accessibility.
- Users are all different. Some have no particular constraints but have usage habits and preferences. Others, such as people with disabilities or seniors, may have, in addition to those habits, constraints when using a digital service.
- These constraints can be very diverse, of a perceptual nature (visual, auditory, tactile), of a motor nature (pointing, manipulation, speech) or cognitive (comprehension, reading). However, it is hardly conceivable to anticipate all of them when designing a service.
- What if any service, or any interface, could continually adjust to users’ usage habits and constraints? This is where a Machine Learning algorithm can prove highly relevant.
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- Users are all different. Some have no particular constraints but have usage habits and preferences. Others, such as people with disabilities or seniors, may have, in addition to those habits, constraints when using a digital service.
- These constraints can be very diverse, of a perceptual nature (visual, auditory, tactile), of a motor nature (pointing, manipulation, speech) or cognitive (comprehension, reading). However, it is hardly conceivable to anticipate all of them when designing a service.
- What if any service, or any interface, could continually adjust to users’ usage habits and constraints? This is where a Machine Learning algorithm can prove highly relevant.
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