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Driven by quality and cost issues, the healthcare systems have to change radically in the near future. One possibility to meet the healthcare demands is to move from managing illness to maintaining wellness. In this transformation, pervasive technologies will play a major role.

The envisioned pervasive computing infrastructure makes healthcare available everywhere, anytime and to anyone. Important enabling technologies are: pervasive sensing and computing, pervasive communication and human-computer interfaces. In several projects, we are researching pervasive computing technologies towards paving the way for a pervasive, user-centered and preventive healthcare model.


Smart Bed System (running)

In this project, a smart bed systems with bed integrated sensors will be used to get estimations on nightly body movements, heart rate and respiration rate.

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Balance and Gait (running)

In this project, the sensor system developed in this group was used to assess gait and balance performance of elderly subjects and patients. In particular, the system was successfully used in assessments of patients with Benign Paroxysmal Positional Vertigo (BPPV).

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CuPiD (completed)

In the EU FP7 CuPiD Project (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.

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iCareNet (completed)

The main objective of the Marie-Curie funded iCareNet project is to make a decisive contribution towards context-aware systems in healthcare, wellness and assisted living applications, leveraged through an interdisciplinary perspective. We develop an ambient-wearable ubiquitous context-aware system to recognize activity routines in daily life for assisted living.

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Stress Monitoring (completed)

In this project, we investigate new methods for assessing the stress experience of people during daily life. This personal stress assistant could help us to create a better work-life balance.

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MONARCA (completed)

The goal of the EU FP7 MONARCA Project 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 solutions with an appropriate platform and a set of services into an innovative system for management, treatment, and self-treatment of the disease.

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Wearable Reaction Time Test (completed)

A simple reaction time (RT) test is a valuable measure to detect mild cognitive loss or Alzheimer's disease. RT tests measure duration and variation of the elapsed time between a stimulus and the individual's response to it. In this work we have designed a wearable watch-like RT test user interface device which generates haptic stimuli and recognizes user’s hand movement as response.

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ProFiTex (completed)

The overall objective of the EU FP7 ProFiTex Project is to increase work safety and efficiency of fire fighting interventions through advanced protective equipment including in particular sensing technologies for increased situational awareness. Our contribution consists of monitoring firefighter's health and performance by means of wearable sensor technologies.

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SEAT (completed)

In the EU FP7 SEAT 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 bio-physiological parameters (like breathing or heart rate).

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daphnet (completed)

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.

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Automatic Dietary Monitoring (completed)

In this EU-project we are focusing on the on-line monitoring of nutrition using body-worn, unobtrusive and non-invasive sensors. Recognition methods for three sensing domains have been explored: body motion during intake, food fractioning sounds and bolus swallowing reflex.

[project page]

Project Links

Current projects: Completed projects:

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