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.

通过提供 EventSleep 数据集和方法，我们展示了事件相机在黑暗环境下的高准确性和鲁棒性，以及在睡眠监测和活动识别领域的潜力和应用。