Diane J Cook1, M Schmitter-Edgecombe. 1. School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington 99163, USA. cook@eecs.wsu.edu
Abstract
OBJECTIVES: Pervasive computing technology can provide valuable health monitoring and assistance technology to help individuals live independent lives in their own homes. As a critical part of this technology, our objective is to design software algorithms that recognize and assess the consistency of activities of daily living that individuals perform in their own homes. METHODS: We have designed algorithms that automatically learn Markov models for each class of activity. These models are used to recognize activities that are performed in a smart home and to identify errors and inconsistencies in the performed activity. RESULTS: We validate our approach using data collected from 60 volunteers who performed a series of activities in our smart apartment testbed. The results indicate that the algorithms correctly label the activities and successfully assess the completeness and consistency of the performed task. CONCLUSIONS: Our results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology. These algorithms will be useful for automating remote health monitoring and interventions.
OBJECTIVES: Pervasive computing technology can provide valuable health monitoring and assistance technology to help individuals live independent lives in their own homes. As a critical part of this technology, our objective is to design software algorithms that recognize and assess the consistency of activities of daily living that individuals perform in their own homes. METHODS: We have designed algorithms that automatically learn Markov models for each class of activity. These models are used to recognize activities that are performed in a smart home and to identify errors and inconsistencies in the performed activity. RESULTS: We validate our approach using data collected from 60 volunteers who performed a series of activities in our smart apartment testbed. The results indicate that the algorithms correctly label the activities and successfully assess the completeness and consistency of the performed task. CONCLUSIONS: Our results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology. These algorithms will be useful for automating remote health monitoring and interventions.
Authors: B Reisberg; S Finkel; J Overall; N Schmidt-Gollas; S Kanowski; H Lehfeld; F Hulla; S G Sclan; H U Wilms; K Heininger; I Hindmarch; M Stemmler; L Poon; A Kluger; C Cooler; M Bergener; L Hugonot-Diener; P H Robert; S Antipolis; H Erzigkeit Journal: Int Psychogeriatr Date: 2001-06 Impact factor: 3.878
Authors: Parisa Rashidi; Diane J Cook; Lawrence B Holder; Maureen Schmitter-Edgecombe Journal: IEEE Trans Knowl Data Eng Date: 2011 Impact factor: 6.977
Authors: Barnan Das; Diane J Cook; Narayanan C Krishnan; Maureen Schmitter-Edgecombe Journal: IEEE J Sel Top Signal Process Date: 2016-02-29 Impact factor: 6.856