Sleep detection and annotation are crucial for researchers to understand sleep patterns, especially in children. With modern wrist-worn watches comprising built-in accelerometers, sleep logs can be collected. However, the annotation of these logs into distinct sleep events: onset and wakeup, proves to be challenging. These annotations must be automated, precise, and scalable. We propose to model the accelerometer data using different machine learning (ML) techniques such as support vectors, boosting, ensemble methods, and more complex approaches involving LSTMs and Region-based CNNs. Later, we aim to evaluate these approaches using the Event Detection Average Precision (EDAP) score (similar to the IOU metric) to eventually compare the predictive power and model performance.

翻译：睡眠检测与标注对于研究人员理解睡眠模式至关重要，尤其是针对儿童群体。通过内置加速度计的现代腕戴式手表，可以收集睡眠日志。然而，将这些日志标注为不同的睡眠事件（入睡和醒来）颇具挑战性。这些标注必须实现自动化、精确且可扩展。我们提出采用多种机器学习技术对加速度计数据进行建模，包括支持向量机、提升方法、集成方法，以及涉及LSTM和基于区域的CNN等更复杂的方法。随后，我们将使用事件检测平均精度（EDAP）得分（类似于IoU指标）评估这些方法，最终比较其预测能力和模型性能。