EventSleep: Sleep Activity Recognition with Event Cameras

• URL: http://arxiv.org/abs/2404.01801v1
• Date: Tue, 2 Apr 2024 10:03:23 GMT
• Title: EventSleep: Sleep Activity Recognition with Event Cameras
• Authors: Carlos Plou, Nerea Gallego, Alberto Sabater, Eduardo Montijano, Pablo Urcola, Luis Montesano, Ruben Martinez-Cantin, Ana C. Murillo,
• Abstract summary: Event cameras are a promising technology for activity recognition in dark environments. We present EventSleep, a new dataset and methodology to study the suitability of event cameras for a medical application.
• Score: 12.584362614255213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event cameras are a promising technology for activity recognition in dark environments due to their unique properties. However, real event camera datasets under low-lighting conditions are still scarce, which also limits the number of approaches to solve these kind of problems, hindering the potential of this technology in many applications. We present EventSleep, a new dataset and methodology to address this gap and study the suitability of event cameras for a very relevant medical application: sleep monitoring for sleep disorders analysis. The dataset contains synchronized event and infrared recordings emulating common movements that happen during the sleep, resulting in a new challenging and unique dataset for activity recognition in dark environments. Our novel pipeline is able to achieve high accuracy under these challenging conditions and incorporates a Bayesian approach (Laplace ensembles) to increase the robustness in the predictions, which is fundamental for medical applications. Our work is the first application of Bayesian neural networks for event cameras, the first use of Laplace ensembles in a realistic problem, and also demonstrates for the first time the potential of event cameras in a new application domain: to enhance current sleep evaluation procedures. Our activity recognition results highlight the potential of event cameras under dark conditions, and its capacity and robustness for sleep activity recognition, and open problems as the adaptation of event data pre-processing techniques to dark environments.

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