Journal ArticleDOI
Keeping the Resident in the Loop: Adapting the Smart Home to the User
Parisa Rashidi,Diane J. Cook +1 more
- Vol. 39, Iss: 5, pp 949-959
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TLDR
CASAS is an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns.Abstract:
Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.read more
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Journal ArticleDOI
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
TL;DR: A generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which is suitable for multimodal wearable sensors, does not require expert knowledge in designing features, and explicitly models the temporal dynamics of feature activations is proposed.
Journal ArticleDOI
A Survey on Ambient-Assisted Living Tools for Older Adults
Parisa Rashidi,Alex Mihailidis +1 more
TL;DR: The emergence of `ambient-assisted living’ (AAL) tools for older adults based on ambient intelligence paradigm is summarized and the state-of-the-art AAL technologies, tools, and techniques are summarized.
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A Knowledge-Driven Approach to Activity Recognition in Smart Homes
TL;DR: This paper presents a generic system architecture for the proposed knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes, and describes the underlying ontology-based recognition process.
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