Automated Prompting in a Smart Home Environment

With more older adults and people with cognitive disorders preferring to stay independently at home, prompting systems that assist with Activities of Daily Living (ADLs) are in demand. In this paper, with the introduction of “The PUCK”, we take the very first approach to automate a prompting system without any predefined rule set or user feedback. We statistically analyze realistic prompting data and devise a classifier from statistical outlier detection methods. Further, we devise a sampling technique to help with skewed and scanty data sets. We empirically find a class distribution that would be suitable for our work and validate our claims with the help of three classical machine learning algorithms.

[1]  Gustavo E. A. P. A. Batista,et al.  Learning with Skewed Class Distributions , 2002 .

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

[3]  G. Frisoni,et al.  An electronic memory aid to support prospective memory in patients in the early stages of Alzheimer's disease: A pilot study , 2003, Aging & mental health.

[4]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[5]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[6]  Martha E. Pollack,et al.  Autominder: an intelligent cognitive orthotic system for people with memory impairment , 2003, Robotics Auton. Syst..

[7]  J. Bates,et al.  Psychosocial interventions for people with a milder dementing illness: a systematic review. , 2004, Journal of advanced nursing.

[8]  Martha E. Pollack,et al.  Entropy-Driven online active learning for interactive calendar management , 2007, IUI '07.

[9]  Diane J Cook,et al.  Tracking Activities in Complex Settings Using Smart Environment Technologies. , 2009, International journal of biosciences, psychiatry, and technology.

[10]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[11]  M. Braga,et al.  Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[12]  Jesse Hoey,et al.  A Decision-Theoretic Approach to Task Assistance for Persons with Dementia , 2005, IJCAI.

[13]  Thomas M. Cover,et al.  The Best Two Independent Measurements Are Not the Two Best , 1974, IEEE Trans. Syst. Man Cybern..

[14]  Daehee Kim,et al.  A Smart Medication Prompting System and Context Reasoning in Home Environments , 2008, 2008 Fourth International Conference on Networked Computing and Advanced Information Management.

[15]  Foster Provost,et al.  The effect of class distribution on classifier learning: an empirical study , 2001 .

[16]  Ozioma C. Okonkwo,et al.  Mild cognitive impairment and everyday function: evidence of reduced speed in performing instrumental activities of daily living. , 2008 .

[17]  Joelle Pineau,et al.  Towards robotic assistants in nursing homes: Challenges and results , 2003, Robotics Auton. Syst..

[18]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.