Situ-Centric Reinforcement Learning for Recommendation of Tasks in Activities of Daily Living In Smart Homes

The smart home has begun playing an important role in supporting independent living by monitoring the activities of daily living, typically for the elderly who live alone. Activity recognition in smart homes has been studied by many researchers with much effort spent on modeling user activities to predict behaviors. Most people, when performing their daily activities, interact with multiple objects both in space and through time. The interactions between user and objects in the home can provide rich contextual information in interpreting human activity. This paper shows the importance of spatial and temporal information for reasoning in smart homes and demonstrates how such information is represented for activity recognition. Evaluation was conducted on three publicly available smart-home datasets. Our method achieved an average recognition accuracy of more than 81% when predicting user activities given the spatial and temporal information.

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Intelligent Recognition of Activities of Daily Living for Assisting Memory and/or Cognitively Impaired Elders in Smart Homes

International Journal of Ambient Computing and Intelligence ◽

10.4018/jaci.2009062204 ◽

2009 ◽

Vol 1 (4) ◽

pp. 46-62 ◽

Cited By ~ 8

Author(s):

Mehdi Najjar ◽

François Courtemanche ◽

Habib Hamam ◽

Alexandre Dion ◽

Jéremy Bauchet

Keyword(s):

Activities Of Daily Living ◽

Daily Living ◽

Smart Homes ◽

Cognitively Impaired ◽

Generic Architecture ◽

Assistance Systems ◽

Intelligent Recognition

The article describes a recognition approach of undertaken activities of daily living (ADLs) performed by memory and/or cognitively impaired elders in smart homes. The proposed technique is materialized via a recognition module inserted in a modular generic architecture which aims to offer a framework to conceive intelligent ADLs assistance systems.

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Supervised classification of activities of daily living in health smart homes using SVM

10.1109/iembs.2009.5334931 ◽

2009 ◽

Cited By ~ 21

Author(s):

A. Fleury ◽

N. Noury ◽

M. Vacher

Keyword(s):

Activities Of Daily Living ◽

Daily Living ◽

Supervised Classification ◽

Smart Homes

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A Time Series Based Sequence Prediction Algorithm to Detect Activities of Daily Living in Smart Home

Methods of Information in Medicine ◽

10.3414/me14-01-0061 ◽

2015 ◽

Vol 54 (03) ◽

pp. 262-270 ◽

Cited By ~ 5

Author(s):

M. B. I. Reaz ◽

M. A. M. Ali ◽

L. F. Rahman ◽

M. Marufuzzaman

Keyword(s):

Activities Of Daily Living ◽

Daily Living ◽

Smart Homes ◽

Prediction Algorithm ◽

Intelligent Environment ◽

Time Duration ◽

Sequence Prediction ◽

Time Component ◽

Future Events ◽

Handicapped People

SummaryObjectives: The goal of smart homes is to create an intelligent environment adapting the inhabitants need and assisting the person who needs special care and safety in their daily life. This can be reached by collecting the ADL (activities of daily living) data and further analysis within existing computing elements. In this research, a very recent algorithm named sequence prediction via enhanced episode discovery (SPEED) is modified and in order to improve accuracy time component is included.Methods: The modified SPEED or M-SPEED is a sequence prediction algorithm, which modified the previous SPEED algorithm by using time duration of appliance’s ON-OFF states to decide the next state. M-SPEED discovered periodic episodes of inhabitant behavior, trained it with learned episodes, and made decisions based on the obtained knowledge.Results: The results showed that M-SPEED achieves 96.8% prediction accuracy, which is better than other time prediction algorithms like PUBS, ALZ with temporal rules and the previous SPEED.Conclusions: Since human behavior shows natural temporal patterns, duration times can be used to predict future events more accurately. This inhabitant activity prediction system will certainly improve the smart homes by ensuring safety and better care for elderly and handicapped people.

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