Annotating sleep states in children from wrist-worn accelerometer data using Machine Learning

Ashwin Ram, Sundar Sripada V. S., Shuvam Keshari, Zizhe Jiang
12/9/2023
eess.SPcs.CVcs.CY

No official code found. Best implementation: zhukovanadezhda/sleep-states (1 star, MIT, last commit 2024-12-16).

Abstract

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.

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Code Implementations(1)

zhukovanadezhda/sleep-statesCommunity72%
MIT

Detect sleep onset and wake from wrist-worn accelerometer data 💤

13Python, Jupyter NotebookSep 20, 20241 years agoMIT
binary-classificationdeep-learningsleep-state-classificationsleep-tracker

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Cite this paper

@article{ram2023annotating,
  title  = {Annotating sleep states in children from wrist-worn accelerometer data using Machine Learning},
  author = {Ashwin Ram and Sundar Sripada V. S. and Shuvam Keshari and Zizhe Jiang},
  year   = {2023},
  eprint = {2312.07561},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2312.07561v1}
}

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