International KES Conference on INTELLIGENT DECISION TECHNOLOGIES (KES-IDT-16)
Puerto de la Cruz, June 2016. Health care is becoming an upcoming topic in modern societies, with the basic goal to improve living quality by increasing a personal health state. The presented approach describes an architecture for a mobile system providing a working prototype to capture several parameters relevant to sleep quality measurement.
Prof. Dr. Seepold presenting results of the cooperation from HTWG Konstanz and Reutlingen University
Sleep is an important aspect in life of every human being. The average sleep duration for an adult is approximately 7 hours per day. Sleep is necessary to regenerate physical and psychological state of a human. A bad sleep quality has a major impact on the health status and can lead to different diseases. In this paper an approach will be presented, which uses a long-term monitoring of vital data gathered by a body sensor during the day and the night supported by mobile application connected to an analyzing system, to estimate sleep quality of its user as well as give recommendations to improve it in real-time. Actimetry and historical data will be used to improve the individual recommendations, based on common techniques used in the area of machine learning and big data analysis.