Annotating sleep states in children from wrist-worn accelerometer data
  using Machine Learning
Ashwin Ram Sundar Sripada V. S. Shuvam Keshari Zizhe Jiang
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.