EventSleep: Sleep Activity Recognition with Event Cameras
Carlos Plou Nerea Gallego Alberto Sabater Eduardo Montijano Pablo Urcola Luis Montesano Ruben Martinez-Cantin Ana C. Murillo
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.