Personal e-Health

Projects

• CuPiD (EU FP7) - Closed loop system for personalized and at-home rehabilitation of people with Parkinson's disease: We develop wearable technologies for the rehabilitation of persons with Parkinson disease.
  
• In the interdisciplinary research project Micro-level behavior and team performance: A social signal processing approach to teamwork we are collaborating with social psychologists. We develop new theories on how psychological constructs determine team performance and how these constructs are connected with observable behavior. We will subsequently test these theories by using body-worn sensors to measure team behavior. Specifically, we will investigate behavior measures in firefighting teams, in surgical teams, and in simulated management teams.
• ProFiTex is a research project funded under the 7th Framework Programme of the European Union.  The overall objective of ProFiTex is to increase work safety and efficiency of fire fighting interventions through advanced protective equipment including in particular sensing technologies for increased situational awareness and a novel solution for robust communication.  Our contribution consists of monitoring firefighter's health and performance by means of wearable sensor technologies.
• MONARCA is a research project funded under the 7th Framework Programme of the European Union. The overall objective is to develop and validate mobile technologies for multi-parametric, long term monitoring of behavioral and physiological information relevant to bipolar disorder. It will combine those solutions with an appropriate platform and a set of services into an innovative system for management, treatment, and self-treatment of the disease.
• In the area of mental health we focus on the automatic detection of emotions and stress as a rather young discipline that is gaining more and more interest. Besides the HCI field, emotion and stress recognition is relevant for personal health care. For people suffering from a mental disease, emotion recognition can provide the therapists with more insight into the daily variations of the patient’s state. Additionally, emotion recognition might be used for prevention and early detection of stress, burn-outs and depressions.
• In the SEAT EU-project, we are focusing on multi-parametric monitoring of the user’s health- and mental state in a seat environment. By using innovative electronic sensors that are embedded into the seat, we are able to continuously measure a variety of parameters (like breathing or heart rate) with a certain degree of accuracy. During our experiments, we additionally collect reference data with established techniques like ECG recordings using wet electrodes. The fusion of the embedded sensor systems enables us to investigate correlations between the unobtrusive but imprecise sensor readings with the established gold standard.
• Within the daphnet project we extend previous research on the properties of single physiological signals to long-term effects and interrelations between a multitude of signals. The simultaneous collection of several long-term signals will enable the construction of physiological networks using dynamical synchronization and cross-correlations patterns, whose momentary state together with the properties of each signal can give a picture of the health status of an individual. The Wearable Computing Lab is the leading partner in the area of the wearable platform and on-body electronics. Furthermore, the Wearable Computing Lab supports the project in the field of context recognition.
• Embedded in the MyHeart EU-project we are focusing on the on-line monitoring of nutrition using body-worn, unobtrusive and non-invasive sensors. Obesity monitoring, detection of water ingestions and hypoglycaemic shock prevention are possible applications. Recognition methods for three sensing domains have been explored: body motion during intake, food fractioning sounds and bolus swallowing reflex.