EDI wearable sensor group is working on wearable sensor system development with potential applications in medicine, sport, and daily activities. Mainly it involves inertial sensors, but the group also has experience with biosensors (ECG, EMG and EEG). The group has developed an energy-efficient wired communication solution that can provide data acquisition from a few hundred sensor nodes. This communication solution is further developed outside the wearables area in engineering structures (bridges, buildings) deformation monitoring. Main challenges in this area are:
  • energy-efficient sensor development in terms of data acquisition and sampling;
  • sensor data processing with constrained processing resources;
  • extraction of useful information from sensor data;
  • ease of use.
Developed solutions can help people to improve quality of life, by obtaining information about their body and daily activities, allowing them to create and follow a more balanced regime. In sports these solutions can help the training process, making it more efficient and accurate. In medicine these solutions can help in rehabilitation by providing feedback about how well the exercises are performed and by allowing rehabilitation exercises without supervision of a medical specialist. EDI has developed unique energy-efficient sensor communication solution that allows data acquisition from a large number of sensor nodes with minimal number of wired connections. This solution is the main building block for sensor systems developed at EDI with applications in medicine, sport, and daily activities. EDI has developed unique 3D surface reconstruction algorithms, that allows surface reconstruction in constrained computational resources. Competencies we provide:
  • sensor system development;
  • inertial sensors;
  • low power sensor system development;
  • PCB design;
  • Flexible PCB design;
  • digital communication interfaces;
  • Bluetooth and Bluetooth Low Energy communication in the application level;
  • inertial signal processing;
  • bio-electric signal processing;
  • machine-learning algorithms;
  • 3D visualization;
  • simulations;
  • software development for smart devices (Android OS smartphones, tablets) in wearable sensor context.


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