Scope
Healthy and good sleep is a prerequisite for a rested mind and body. Both form the basis for physical and mental health. The frequency of medically diagnosed sleep disorders increases from the age of 40. The aim of this project is the continuous monitoring of vital data of patients during sleep over long periods using exclusively non-invasive technologies. The system offers an opportunity to enhance the quality of life of an ageing society by putting the collection of sleep data at the center of the analysis and making the data available for medical evaluations.
Objective
The Project aims to monitor vital signs during sleep and over long periods in the home environment of the patients through the exclusive use of non-invasive technologies. The proposed project aims to develop a system (including both the software and the hardware) for automatically collecting data relevant to sleep. The system would provide recommendations (e.g., Cognitive Behavioural Therapy for Insomnia (CBT-I) based) and verify compliance. Continuously measured values (e.g., sleep/wake state, respiration, or heart rate during sleep) are compared with the recommendations and readjusted if necessary.
Research Questions
- How does the sleep quality of older people differ from average assumptions, and how can rule-based systems or machine learning improve the ability to personalize scoring?
- How can non-invasive sleep quality measurement support sleep therapy support?
- Is there a reduced set of vital signs that can be used to determine sleep phases, and how well can these parameters be captured?
- Are there significant differences in vital signs between older women and men?
Morpheus - Strategy
Non invasive and unobtrusive sleep analisys
This project offers an opportunity to enhance the quality of life of an ageing society by putting the collection of sleep data at the center of the analysis and making the data available for medical evaluations. Healthy and good sleep is a prerequisite for a rested mind and body.
Sleep Analysis
AI powered
Using a non-invasive and unobtrusive network of sensors the system captures vital data from the patient in order to extract valuable sleep information and identify potential disorders using Artificial intelligence and Machine Learning technologies.
Cloud based technologies
Privacy preserving
Using IoT protocols, cloud based technologies, and fog computing the system is able to process large amount of information, improve machine learning models without jeopardizing patients privacy.
Development
General Strategy
The Core component provides the basic functionality of an embedded computer and implements the interfaces to the other components.
Algorithms process the signal data using different intelligent techniques to analyse the sleep quality and relevant vital data in a personalized way.
A non-invasive network of Hardware sensors captures vital data of the patient.
An API provides an open interface to external platforms.
A Responsive Application shows data in a suitable way for patients, physicians and other target groups.
Testing FSR Sensors
Initial testing is being performed using Force Sensing Resistors (FSR). These sensors deliver a variable electrical resistance as exposed to physical force or pressure applied to them—putting them in the broader category of piezoresistive devices. Typical construction consists of a membrane-like flexible substrate that is printed with two unconnected halves of an interdigitated circuit. When the sensor is in a neutral state, the circuit remains open, and electricity is unable to pass from one wire to the other.
Optimizing Solution
Currently, laboratory is testing different distributions of FSR-sensors with various combination in order to find the most efficient and optimized solution in terms of sensitivity, reliability, and signal quality. However, the solution is not restricted only to FSR sensors but further investigations on the piezoresistive, strain gauge, and fiber bragg grating (FBG) sensors have been planned.
Partners and Collaborations
Several European academic and industrial partners covering broad range of expertise from electrical and mechanical engineering to physicians, data and computer science have come together in a interactive and dynamic discussion led by HTWG Konstanz (UC-Lab) in order to push the state of the start and moving on the edging of the field.
- AWO District Association Schwarzwald
- Kempten University of Applied Sciences (HSKT) / Softwarehaus Zuleger (SHZ)
- Charité – University of Berlin
- Reutlingen University (HSRT)
- SmartHOme & Living Baden-Württemberg e.V. (SH&L)
- Universidad de Seville – Spain
- Università Politecnica delle Marche (UPM) – Italy
- Université Paris 1 Panthéon – Sorbonne – France
This project is funded by the Carl-Zeiss-Stiftung (no. P2019-03-003) for the duration of three years with the budget of 1 M Euros
