Intelligent embedded systems research laboratory

Leader: István Oniga, DSc

Intelligent embedded systems laboratory research topics are both fundamental (basic) and applied researches. The main topics for the theoretical research are learning systems, machine learning and for the applied research are wearable computing, mobile robotics, neural networks hardware implementation and ambient intelligent systems development.

Main research topics

  • Implementation of artificial neural networks (ANN) in field-programmable gate arrays
    • The research aim is to design hardware implemented artificial neural networks using reconfigurable hardware (FPGAs).
  • e-Health and Ambient assisted living systems
    • Our research aims to assist elderly or sick people everyday independent activities using the latest assistive technologies based on Internet of Things. The research directions are as follows:
      • Activity and health status platform development

For human activity monitoring we use multiple wearable sensor tags like the CC2541 power-optimized system-on-chip (SoC) that combines the performance of a Bluetooth low energy (BLE) transceiver with the industry-standard enhanced 8051 MCU. The tag contains a 6 axis motion sensor MPU6050 (acceleration + angular velocity) and BMP180 pressure and temperature sensor. We acquire data using these tags and we intend to make publically available a large dataset with human activities in order to support reproducible research.

  • Activity and health status recognition using neural networks modeled in Matlab

We started to develop our own methods related to Human Activity Recognition for feature extraction and feature selection on publically available datasets, using a combination of feature extraction and selection methods from public repositories, and we plan to continue this research on our dataset too.

  • Activity and health status recognition using hardware implemented ANNs

Following the successful completion of the first two goals we will use the results to implement in hardware a real-time human activity recognition system using neural networks. We aim to extend the recognition system to the health status recognition also.

  • Assistive robots

The main goal is to develop an Open Platform for development of telepresence or assistive robots. The robot must be able to be remotely controlled or autonomous movement, obstacle detection and avoidance, video streaming of the on board camera images, etc. Such a robot could be used for daily life assistance of older adults or persons with different type of disabilities.

Intelligent embedded systems design and applications

  • We design a hardware-software co-design platform based on FPGA, developed for fast prototyping of embedded systems using hardware modules that can be easily connected and “driver” modules that can manage I/O devices and sensors basic behavior. Using ANN to add learning capabilities and adaptive behavior is essential for an intelligent system and the use of FPGA is an important feature in terms of their hardware implementation. Among possible applications are intelligent computer peripherals enabling people with any kind of handicap to use computer and communicate, as any kind of industrial or domestic device with embedded and hidden intelligence at user for prosthetic, automotive, “domotic” and automation fields where the trend is to produce easy-to-use devices.

Updated: 2018.01.25.