Abstract
A privacy preserving approach for recognising Activities of Daily Living (ADLs) is proposed within this work. Low-resolution thermal sensors and Convolutional Neural Network (CNN) models were utilised for detecting positions within a smart environment and classifying human poses. A Hidden Markov Model (HMM) was implemented for which the position and pose data acted as the model’s observable information. An average F-score of 0.8171 was achieved for the poses on a test dataset. From a separate test dataset, the times in which each ADL began and ended were estimated with a maximum of 30 s between estimations and ground truth. Each ADL was correctly classified from the test dataset. Further discussion on the results are presented in this article.
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Burns, M., Nugent, C., McClean, S., Quero, J.M., Polo-Rodríguez, A. (2023). A Deep Learning and Probabilistic Approach to Recognising Activities of Daily Living with Privacy Preserving Thermal Sensors. In: Bravo, J., Urzáiz, G. (eds) Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). UCAmI 2023. Lecture Notes in Networks and Systems, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-031-48642-5_15
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