Category: Awards


Awards during IEEE/IES HSI 2019 Conference

During the 12th International Conference on Human System Interaction IEEE HSI 2019 (Richmond, USA, June 25-27) teams from our department received BEST PAPER awards. Papers had been evaluated by reviewers before the conference and additionally presentations were evaluated during the conference. The following works presented by our team were awarded:

  1. Influence of Thermal Imagery Resolution on Accuracy of Deep Learning based Face Recognition (Maciej Szankin, Alicja Kwasniewska, Jacek Ruminski)
  2. Estimation of electrode contact in capacitive ECG measurement (Adam Bujnowski, Mariusz Kaczmarek, Jerzy Wtorek, Kamil Osinski, Domnika Strupinska)





Awarded student project – ReFlex

Group project “ReFlex” was awarded and took second place in the Group Project contest.

Group project “ReFlex – Rehabilitation support system using EMG signal to control the game” was awarded in Group Project contest, edition 2017. The goal of the project was to prepare the rehabilitation support system designed to increase:
– Attractiveness of exercises,
– Effectiveness of rehabilitation process,
– Motivation to exercise,
– Muscle activity.
All above was realized by ensuring continuous measurement of electrical muscle activity. The signal is sent wirelessly to the mobile device, where it is used to control the dedicated application.
Project was realized by Anna Gozdan, Dorota Dettlaff, Paweł Troka and Hubert Toczko during last year, and was commissioned by Dynamic Precision sp z o. o.


Best Paper Award – HSI 2017

Krzysztof Czuszynski, Jacek Ruminski and Jerzy Wtorek received the Best Paper Award during the 10th IEEE/IES International Conference on Human System Interaction. The title of the paper: Pose classification in the gesture recognition using the linear optical sensor.

Gesture sensors for mobile devices, which have a capability of distinguishing hand poses, require efficient and accurate classifiers in order to recognize gestures based on the sequences of primitives. Two methods of poses recognition for the optical linear sensor were proposed and validated. The Gaussian distribution fitting and Artificial Neural Network based methods represent two kinds of classification approaches. Three types of hand poses, differing in the number of fingers joined together, were investigated. The reflected light intensity pattern originated by hand located closely to the sensor was parameterized into 14 features. The change of reflection pattern originated by hand dislocation was reduced by application of two variable functions in the first of the methods. A one and two hidden layers topologies were considered in the neural network related approach. Both methods were designed with the use of a training set of samples and validated with another (testing) set. The results present the average poses recognition rate of 81.19% for Gaussian distribution fitting and 90.02% for ANN based method.
More info at: IEEE Xplore