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

2023/12/09 by Ashwin Ram, Sundar Sripada V. S., Ram, Ashwin +5
Medicine · Computer Science · Psychology · #Obstructive Sleep Apnea Research #Context-Aware Activity Recognition Systems #Sleep and related disorders

paper · pdf · doi:10.48550/arxiv.2312.07561

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.

Related

• A robust generalizable device-agnostic deep learning model for sleep-wake determination from triaxial wrist accelerometry
• Sleep Quality Prediction from Wearables using Convolution Neural Networks and Ensemble Learning
• Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques
• A Generic Algorithm for Sleep-Wake Cycle Detection using Unlabeled Actigraphy Data
• Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device