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Machine Learning for Assistive Technologies
Jesse Hoey · Pascal Poupart · Thomas Ploetz

Fri Dec 10 07:30 AM -- 06:30 PM (PST) @ Westin: Glacier
Event URL: http://www.cs.uwaterloo.ca/~jhoey/mlat-nips2010/index.html »

An aging demographic has been identified as a challenge for healthcare provision, with technology tipped to play an increasingly significant role. Already, assistive technologies for cognitive and physical disabilities are being developed at an increasingly rapid rate. However, the use of complex technological solutions by specific and diverse user groups is a significant challenge for universal design. For example, 'smart homes' that recognise inhabitant activities for assessment and assistance have not seen significant uptake by target user groups. The reason for this is primarily that user requirements for this type of technology are very diverse, making a single universal design extremely challenging. Machine learning techniques are therefore playing an increasing role in allowing assistive technologies to be adaptive to persons with diverse needs. However, the ability to adapt to these needs carries a number of theoretical challenges and research directions, including but not limited to decision making under uncertainty, sequence modeling, activity recognition, active learning, hierarchical models, sensor networks, computer vision, preference elicitation, interface design and game theory. This workshop will expose the research area of assistive technology to machine learning specialists, will provide a forum for machine learning researchers and medical/industrial practitioners to brainstorm about the main challenges, and will lead to developments of new research ideas and directions in which machine learning approaches are applied to complex assistive technology problems.

Author Information

Jesse Hoey (University of Waterloo)
Pascal Poupart (University of Waterloo)
Thomas Ploetz (Newcastle University)

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