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).