ESS-H - Embedded Sensor Systems for Health Research Profile

Status:

finished

Start date:

2013-04-01

End date:

2019-03-31

ESS-H is a research profile targeting Embedded Sensor Systems for Health. The profile is hosted by Mälardalen University, Embedded Systems, and is conducted together with industrial partners. The profile combines a unique set of competences including both hardware and software aspects at Mälardalen University and at the partner companies, in collaboration with healthcare and care providers. The research groups in ESS-H already have well-established cooperation with companies and public organizations in the healthcare area. The goal of the ESS-H profile is that academia and industry together achieve a significant increase in research in the area of embedded sensor systems for health performed at Mälardalen University and participating companies, and that ESS-H is established as a nationally leading and an internationally renowned research center for embedded sensor systems for health. The core competence areas within ESS-H are Biomedical Sensor Technology, Biomedical Signal Processing, and Intelligent Decision. Dependability, verifiability and safety are important properties of embedded sensor systems in health applications. The supporting technology areas Software Testing and Dependable Wireless Communication contribute to ensure these properties, and are thus important for the successful deployment of embedded sensor systems in health applications. Close co-production will ensure industrial relevance in the formulation of research problems, user relevance in system design and implementation, as well as channels to adapt, deploy, and commercialize research results from ESS-H. Three main research challenges are identified within ESS-H: Reliable Acquisition of Physiological Data, Personal Biofeedback, and Reliable Distribution of Decision Support. Each of these challenges requires multi-disciplinary research. The research challenges will be addressed in the setting of collaborative research projects, where each project will combine all competence areas within ESS-H, including Mälardalen University researchers from the core areas, expertise from the partner companies, and Mälardalen University researchers from the supporting technology areas. The research within the projects will be systems-oriented and the projects will address the three main research challenges identified. There will be three projects within ESS-H: Sensor Systems for Health Monitoring at Home, Sensor Systems for Health at Work, and Infrastructure for Physiological Data Management. Three to six companies will participate in each project, together with Mälardalen University researchers. There will be considerable synergies and collaboration between the projects.

[Show all publications]

A Neural Network for Stance Phase detection in smart cane users (Jun 2019)
Juan Rafael Caro-Romero , Joaquin Ballesteros, Francisco Garcia-Lagos , Francisco Sandoval
15th International Work-Conference on Artificial Neural Networks (IWANN'19)

Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a Hierarchical Genetic Algorithm (Jun 2019)
Miguel Leon Ortiz, Joaquin Ballesteros, Jonatan Tidare, Ning Xiong, Elaine Åstrand
IEEE Congress on Evolutionary Computation (IEEE CEC'19)

A cane-based low-cost sensor to implement attention mechanisms in telecare robots (May 2019)
Joaquin Ballesteros, Alberto Tudela , Juan Rafael Caro-Romero , Cristina Urdiales
2019 International Conference on Robotics and Automation (ICRA'19)

Detection of Human Bodypart Abnormalities by Microwaves – A New Approach (May 2019)
Nikola Petrovic, Ivan Tomasic, Maria Lindén, Per Olov Risman
42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2019)

FIREWORK: Fog orchestration for secure IoT networks (Apr 2019)
Maryam Vahabi, Hossein Fotouhi, Mats Björkman
International Conference on Security and Privacy in New Computing Environments (SPNCE'19)

Drivers' Sleepiness Classification using Machine Learning with Physiological and Contextual data (Mar 2019)
Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum
First International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2019)

PartnerType
Addiva Industrial
Cambio Healthcare Systems Industrial
DELTA AB Industrial
Giraff AB Industrial
Hök instrument AB Industrial
JC Development Industrial
Medfield Diagnostics AB Industrial
Motion Control Industrial
Quality Pharma Industrial

Maria Lindén, Professor

Email: maria.linden@mdh.se
Room: U1-139
Phone: +46-21-101548