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).
翻译:恢复过程的效率是身体的睡眠恢复过程。 恢复过程的效率与每个睡眠阶段所花费的时间量直接相关。 因此, 通过穿戴装置自动跟踪睡眠过程吸引了研究人员和工业界。 目前最先进的睡眠跟踪解决方案是记忆和处理贪婪,它们需要云或移动电话连接。 我们提议一个记忆高效的睡眠跟踪架构,可以在嵌入环境中工作而不需要任何云或移动电话连接。 在这项研究中, 提出了一个新的架构, 由特征提取和人工神经网络组成, 并基于堆放分类。 此外, 我们讨论了如何通过拟议框架处理内存限制环境的睡眠积累的顺序性质。 为了校验系统, 将一个数据集从24个不同科目收集到31个晚上, 手腕磨损装置有3轴加速计(ACC) 和光电荷成像仪(PPG) 传感器。 在所收集的数据集中, 拟议的分类架构比其竞争者高出20 ⁇ 和14 ⁇ F1分。 除了高级性外, 拟议的架构是所有存储单位都拥有4.2千字的存储记录系统(RAM) 。