CogWatch: Intelligent agent-based system to assist stroke survivors during tea-making

Stroke survivors often have difficulties performing Activities of Daily Living (ADL). When they try to complete a task, they tend to rely on caregivers who give them cues when necessary. However, this reliance on caregivers' support may affect their ability to live an independent life. Hence, to tackle this issue, while giving them the guidance they need during ADL, the development of a specific Intelligent Assistive Planning System (AIPS) may be beneficial. In this paper, the design of CogWatch, which is a system implemented to assist stroke survivors during tea-making, is described. The Markov Decision Process framework that can be used in such a context, and which can potentially model any sequential tasks, is also explained. Finally, preliminary results involving twelve patients interacting with the system are analysed, and future plans are discussed.

[1]  Guy Shani,et al.  An MDP-Based Recommender System , 2002, J. Mach. Learn. Res..

[2]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[3]  Martha E. Pollack,et al.  Autominder: A Case Study of Assistive Technology for Elders with Cognitive Impairment , 2006 .

[4]  Martin J. Russell,et al.  Intelligent Assistive System Using Real-Time Action Recognition for Stroke Survivors , 2014, 2014 IEEE International Conference on Healthcare Informatics.

[5]  Sven Wachsmuth,et al.  TEBRA: An Automatic Prompting System for Persons with Cognitive Disabilities in Brushing Teeth , 2013, HEALTHINF.

[6]  Sven Koenig,et al.  Optimal Probabilistic and Decision-Theoretic Planning using Markovian , 1992 .

[7]  Stuart J. Russell,et al.  Control Strategies for a Stochastic Planner , 1994, AAAI.

[8]  A. Mihailidis,et al.  The COACH prompting system to assist older adults with dementia through handwashing: An efficacy study , 2008, BMC geriatrics.

[9]  Steve J. Young,et al.  Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems , 2010, Comput. Speech Lang..

[10]  M. Schwartz,et al.  Errorless learning in cognitive rehabilitation: A critical review , 2012, Neuropsychological rehabilitation.

[11]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[12]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[13]  Martin J. Russell,et al.  POMDP Based Action Planning and Human Error Detection , 2015, AIAI.

[14]  Jesse Hoey,et al.  A planning system based on Markov decision processes to guide people with dementia through activities of daily living , 2006, IEEE Transactions on Information Technology in Biomedicine.

[15]  Anthony Jameson,et al.  When policies are better than plans: decision-theoretic planning of recommendation sequences , 2001, IUI '01.

[16]  Giuseppe Riccardi,et al.  Combining user intention and error modeling for statistical dialog simulators , 2010, INTERSPEECH.

[17]  Craig Boutilier,et al.  Stochastic dynamic programming with factored representations , 2000, Artif. Intell..

[18]  G. Humphreys,et al.  Systematic assessment of apraxia and functional predictions from the Birmingham Cognitive Screen , 2012, Journal of Neurology, Neurosurgery & Psychiatry.

[19]  Roberto Pieraccini,et al.  A stochastic model of human-machine interaction for learning dialog strategies , 2000, IEEE Trans. Speech Audio Process..

[20]  Martha E. Pollack,et al.  Adaptive cognitive orthotics: combining reinforcement learning and constraint-based temporal reasoning , 2004, ICML.

[21]  David Maxwell Chickering,et al.  Using Temporal Data for Making Recommendations , 2001, UAI.

[22]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..

[23]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[24]  A. Gillespie,et al.  Simulating naturalistic instruction: the case for a voice mediated interface for assistive technology for cognition , 2008 .

[25]  E. Renzi,et al.  The Executive And Ideational Components of Apraxia , 1988, Cortex.

[26]  A. Mihailidis,et al.  Assistive technology for cognitive rehabilitation: State of the art , 2004 .

[27]  Andrew G. Barto,et al.  Learning to Act Using Real-Time Dynamic Programming , 1995, Artif. Intell..