Neural Network Based Sleep Phases Classification for Resource Constraint Environments

TL;DR

This paper introduces a memory-efficient neural network architecture for sleep stage classification suitable for embedded devices, achieving high accuracy without cloud connectivity.

Contribution

A novel, low-memory neural network architecture for sleep staging that operates independently on resource-constrained embedded systems.

Findings

Achieves 20% higher F1 score than competitors

Uses only 4.2 KB of RAM in implementation

Validated on data from 24 subjects over 31 nights

Abstract

Sleep is restoration process of the body. The efficiency of this restoration process is directly correlated to the amount of time spent at each sleep phase. Hence, automatic tracking of sleep via wearable devices has attracted both the researchers and industry. Current state-of-the-art sleep tracking solutions are memory and processing greedy and they require cloud or mobile phone connectivity. We propose a memory efficient sleep tracking architecture which can work in the embedded environment without needing any cloud or mobile phone connection. In this study, a novel architecture is proposed that consists of a feature extraction and Artificial Neural Networks based stacking classifier. Besides, we discussed how to tackle with sequential nature of the sleep staging for the memory constraint environments through the proposed framework. To verify the system, a dataset is collected from 24 different subjects for 31 nights with a wrist worn device having 3-axis accelerometer (ACC) and photoplethysmogram (PPG) sensors. Over the collected dataset, the proposed classification architecture achieves 20\% and 14\% better F1 scores than its competitors. Apart from the superior performance, proposed architecture is a promising solution for resource constraint embedded systems by allocating only 4.2 kilobytes of memory (RAM).