A Personalized Adaptive Algorithm for Sleep Quality Prediction using Physiological and Environmental Sensing Data

The lacking data from wearable sensors to solve different problems in the healthcare area is obvious since it is not easy to find enough volunteers to collect data. Moreover, human reacts very differently to medical treatment/ exercise levels/ stress and so on. Therefore, we need an advanced prediction model which can reuse the public data and can be adapt to personal data to predict health parameters. This paper introduces a solution for this issue. We present a novel personalized adaptive algorithm based on ensemble learning to predict sleeping efficiency, the proposed framework can be extended to solve many problems in healthcare applications. In this work, the global model is built based on ensemble learning with common features from all clients. The global model is then combined with the model from the client with more personalized features. The client model will learn and be updated model every day. Our proposed framework was tested in two data sets PMData and another private data set and showed better results than the conventional method. The proposed algorithm/ framework is a great step to solve the prediction problem in healthcare since each person has their own characteristics, responds differently to treatments/environment/stressful levels. The proposed algorithm is a big enhancement in building a health navigator system to enhance human health.