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. 2009;48(5):480-5.
doi: 10.3414/ME0592. Epub 2009 May 15.

Assessing the quality of activities in a smart environment

Diane J Cook  1 , M Schmitter-Edgecombe
Affiliations

Assessing the quality of activities in a smart environment

Diane J Cook et al. Methods Inf Med. 2009.

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.

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Figures

Figure 1
Figure 1
Resident performing “hand washing” activity (left). This activity triggers motion sensor ON/OFF events as well as water flow sensor values (right). Sensors in the apartment (bottom) monitor motion (M), temperature (T), water (W), burner (B), phone (P), and item use (I).
Figure 2
Figure 2
Plots of the normalized Gaussian distributions for each of the five activities. The peak of each curve is the likelihood mean for each model.
See this image and copyright information in PMC

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