Assessing the Quality of Activities in a Smart Environment

By Diane J. Cook, Maureen Schmitter-Edgecombe. Methods of Information in Medicine, 2009

402 citations recorded by the source corpus.

Summary 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.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/03d1383890c5c0de7588a3514b75961f28d078a3

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Sensor-Based Activity Recognition (2012, methodology): “HUMAN activity recognition is one of the most promising research topics for a variety of areas, including pervasive and mobile computing [1], [2], surveillance-based security [3]–[10], context-aware computing [41], [83], [152] and ambient assistive living [76], [93], [103], [126], [146].”
Recent trends in machine learning for human activity recognition—A survey (2018, methodology): “The authors have evaluated using the interleaved ADL activities (IAA) dataset (Cook & Schmitter-Edgecombe, 2009) which is described in Table 1.”
A Survey of Deep Active Learning (2020, background): “[95] OPPORTUNITY [95], WISDM [122], SenseBox [219], Skoda Daphnet [15],CASAS [40] Smart home”
Learning Setting-Generalized Activity Models for Smart Spaces (2010, methodology): “Other researchers [7] rely upon motion sensors as well as item sensors to recognize ADL activities that are being performed.”
The smart thermostat: using occupancy sensors to save energy in homes (2010, methodology): “To illustrate the concept of a deep setback, Figure 8 shows the distributions of leave and return times for the publicly available Kasteren [25] and Tulum [31] home monitoring datasets, excluding weekends.”
Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation (2013, methodology): “Currently used methods are mostly based on machine-learning models, such as Bayes classifiers [54], [61], Markov models [62], [63], artificial neural networks [64], [65], and support vector machines (SVMs) [66], [67], thanks to their ability to create context models from measured data and adapt…”