A survey of algorithmic methods for partially observed Markov decision processes
Annals of Operations Research
The context toolkit: aiding the development of context-enabled applications
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Designing cognitive supports for dementia
ACM SIGACCESS Accessibility and Computing
An Integrated Approach to Context Specification and Recognition in Smart Homes
ICOST '08 Proceedings of the 6th international conference on Smart Homes and Health Telematics
Human-Computer Interaction
Ambient kitchen: designing situated services using a high fidelity prototyping environment
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Design and prototype of a device to engage cognitively disabled older adults in visual artwork
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Structure learning on large scale common sense statistical models of human state
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Slice&Dice: Recognizing Food Preparation Activities Using Embedded Accelerometers
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Will it be a capital letter: signalling case mode in mobile phones
Interacting with Computers
Computer Vision and Image Understanding
Problems people with dementia have with kitchen tasks: The challenge for pervasive computing
Interacting with Computers
Goal-oriented sensor selection for intelligent phones: (GOSSIP)
Proceedings of the 2011 international workshop on Situation activity & goal awareness
Empathy, participatory design and people with dementia
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The french kitchen: task-based learning in an instrumented kitchen
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
People, sensors, decisions: Customizable and adaptive technologies for assistance in healthcare
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
HCSE'12 Proceedings of the 4th international conference on Human-Centered Software Engineering
Combining embedded accelerometers with computer vision for recognizing food preparation activities
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
FoodBoard: surface contact imaging for food recognition
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
ACM Transactions on Accessible Computing (TACCESS)
Interactive activity recognition and prompting to assist people with cognitive disabilities
Journal of Ambient Intelligence and Smart Environments - Home-based Health and Wellness Measurement and Monitoring
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Activity recognition in intelligent environments could play a key role for supporting people in their activities of daily life. Partially observable Markov decision process (POMDP) models have been used successfully, for example, to assist people with dementia when carrying out small multistep tasks such as hand washing. POMDP models are a powerful, yet flexible framework for modeling assistance that can deal with uncertainty and utility in a theoretically well-justified manner. Unfortunately, POMDPs usually require a very labor-intensive, manual set-up procedure. This paper describes a knowledge-driven method for automatically generating POMDP activity recognition and context-sensitive prompting systems for complex tasks. It starts with a psychologically justified description of the task and the particular environment in which it is to be carried out that can be generated from empirical data. This is then combined with a specification of the available sensors and effectors to build a working prompting system. The method is illustrated by building a system that prompts through the task of making a cup of tea in a real-world kitchen. The case is made that, with further development and tool support, the method could feasibly be used in a clinical or industrial setting.