Sleep Quality Assessment Using Ensembles of Machine Learning and Deep Learning Models

Abstract

Estimating our sleep quality and state is essential to identify disorders or chronic ailments related to sleep patterns. The authors propose certain methods using machine learning and deep learning algorithms to assess the quality of sleep. The signals and hence the data taken from the wrist actigraphy and data accumulated through sleep research on postmenopausal women are used in this work. This data is preprocessed and used to score the sleep-wake pattern objectively and subjectively. With the availability of multi-ethnic data, both machine learning and deep learning models are created by rigorous training to avoid over-fitting and under-fitting. As the users of wearable active devices are increasing and proven to be a commercially successful model, this work is more relevant to multiple groups across ages.