The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
POMDP Models for Assistive Technology
Abstract
This chapter presents a general decision theoretic model of interactions between users and cognitive assistive technologies for various tasks of importance to the elderly population. The model is a partially observable Markov decision process (POMDP) whose goal is to work in conjunction with a user towards the completion of a given activity or task. This requires the model to monitor and assist the user, to maintain indicators of overall user health, and to adapt to changes. The key strengths of the POMDP model are that it is able to deal with uncertainty, it is easy to specify, it can be applied to different tasks with little modification, and it is able to learn and adapt to changing tasks and situations. This chapter describes the model, gives a general learning method which enables the model to be learned from partially labeled data, and shows how the model can be applied within our research program on technologies for wellness. In particular, we show how the model is used in four tasks: assisted handwashing, stroke rehabilitation, health and safety monitoring, and wheelchair mobility. The first two have been fully implemented and tested, and results are summarized. The second two are meant to demonstrate how the POMDP can be applied across a wide variety of domains, but do not have specific implementations or results. The chapter gives an overview of ongoing work into each of these areas, and discusses future directions.
Related Content
|
Timothy Gifford.
© 2023.
23 pages.
|
|
Sandy White Watson.
© 2023.
18 pages.
|
|
Elaine Wilson, Sarah Chesney.
© 2023.
32 pages.
|
|
Michael Finetti, Nicole Luongo.
© 2023.
30 pages.
|
|
Anurag Vijay Agrawal, R. Pitchai, C. Senthamaraikannan, N. Alangudi Balaji, S. Sajithra, Sampath Boopathi.
© 2023.
23 pages.
|
|
Keri A. Sullivan.
© 2023.
13 pages.
|
|
Nicole L. Lambright.
© 2023.
16 pages.
|
|
|