Human activity recognition in time series in PIR sensor networks with reinforcement learning
Year: 2024
Type: article
Abstract: Recognizing human activity from IoT events is essential for automatic light scene setting. A challenge with IoT systems is the diversity among customers. For addressing this diversity a reinforcement learning method is explored that enables for each customer the learning of a model for classifying context of time series. The method is a Bayesian method and it is of interest as a prior is put on the states that are to be predicted. Results on activity recognition will be given for a household and the relative feature-importance of time-of-the-day versus motion will be analyzed.
Author: Pijlman, Fetze
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W6911734489
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