3D convolutional neural network for home monitoring using low resolution thermal-sensor array

Lili Tao, Timothy Volonakis, Bo Tan, Ziqi Zhang, Yanguo Jing, Melvyn Smith

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)


The recognition of daily actions, such as walking, sitting orstanding, in the home is informative for assisted living, smarthomes and general health care. A variety of actions in complexscenes can be recognised using visual information. Howevercameras succumb to privacy concerns. In this paper, we presenta home activity recognition system using an 8×8 infared sensor array. This low spatial resolution retains user privacy, but is still a powerful representation of actions in a scene. Actions are recognised using a 3D convolutional neural network, extracting not only spatial but temporal information from video sequences. Experimental results obtained from a publicly available dataset Infra-ADL2018 demonstrate a better performance of the proposed approach compared to the state-of-the-art. We show that the sensor is considered better at detecting the occurrence of falls and activities of daily living. Our method achieves an overall accuracy of 97.22% across 7 actions with a fall detection sensitivity of 100% and specificity of 99.31%
Original languageEnglish
Title of host publication3rd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2019)
ISBN (Print)9781839530883
Publication statusPublished - 19 Sept 2019
Externally publishedYes


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