TY - GEN
T1 - Situ-Centric Reinforcement Learning for Recommendation of Tasks in Activities of Daily Living in Smart Homes
AU - Oyeleke, Richard O.
AU - Yu, Chen Yeou
AU - Chang, Carl K.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - In this paper, we propose a new recommendation system that considers the changing human mental state, behavior and environmental contexts to provide guidance to persons diagnosed of mild cognitive impairment (MCI) or Alzheimer's in performing their activities of daily living (ADLs). The recommendation system is based on the Situ-framework that represents an activity as a sequence of situations, and a situation is a 3-tuple where M denotes human mental state, B is the behavior context and E is the environmental context. More so, it uses an automaton to model an activity thereby generating corresponding task sequences. Interactions between the inhabitants and the sensor networks and the activity being performed are represented as an information space. It then uses a modified model learning algorithm as its decision making tool to recommend appropriate actions or tasks sequence to the inhabitant in a situation of uncertainty caused by episode of confusion or memory loss to ensure he reaches his goal. Consequently, this helps ensure that safety of the person is not compromised when he makes a poor decision regarding the sequence in which ADL tasks are to be performed. We use a single activity class dataset generated from a smart home for 50 independent learning experiments for three (3) ADLs case studies. The accuracy of the sequential-tasks recommendations was found to be high. We further evaluated the risk sensitivity of the recommendation system using the action selection probabilities and corresponding reward points associated with performing a task. The results show that the proposed recommendation system is risk averse.
AB - In this paper, we propose a new recommendation system that considers the changing human mental state, behavior and environmental contexts to provide guidance to persons diagnosed of mild cognitive impairment (MCI) or Alzheimer's in performing their activities of daily living (ADLs). The recommendation system is based on the Situ-framework that represents an activity as a sequence of situations, and a situation is a 3-tuple where M denotes human mental state, B is the behavior context and E is the environmental context. More so, it uses an automaton to model an activity thereby generating corresponding task sequences. Interactions between the inhabitants and the sensor networks and the activity being performed are represented as an information space. It then uses a modified model learning algorithm as its decision making tool to recommend appropriate actions or tasks sequence to the inhabitant in a situation of uncertainty caused by episode of confusion or memory loss to ensure he reaches his goal. Consequently, this helps ensure that safety of the person is not compromised when he makes a poor decision regarding the sequence in which ADL tasks are to be performed. We use a single activity class dataset generated from a smart home for 50 independent learning experiments for three (3) ADLs case studies. The accuracy of the sequential-tasks recommendations was found to be high. We further evaluated the risk sensitivity of the recommendation system using the action selection probabilities and corresponding reward points associated with performing a task. The results show that the proposed recommendation system is risk averse.
KW - Activities of daily living (ADLs)
KW - Human activity recognition
KW - Model learning
KW - Situation
KW - Smart environment
UR - http://www.scopus.com/inward/record.url?scp=85055521307&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055521307&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC.2018.10250
DO - 10.1109/COMPSAC.2018.10250
M3 - Conference contribution
AN - SCOPUS:85055521307
T3 - Proceedings - International Computer Software and Applications Conference
SP - 317
EP - 322
BT - Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
A2 - Demartini, Claudio
A2 - Reisman, Sorel
A2 - Liu, Ling
A2 - Tovar, Edmundo
A2 - Takakura, Hiroki
A2 - Yang, Ji-Jiang
A2 - Lung, Chung-Horng
A2 - Ahamed, Sheikh Iqbal
A2 - Hasan, Kamrul
A2 - Conte, Thomas
A2 - Nakamura, Motonori
A2 - Zhang, Zhiyong
A2 - Akiyama, Toyokazu
A2 - Claycomb, William
A2 - Cimato, Stelvio
T2 - 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018
Y2 - 23 July 2018 through 27 July 2018
ER -