Publikationen

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen

2019

Luzhnica Granit, Veas Eduardo Enrique

Optimising the Encoding for Vibrotactile Skin Reading

ACM CHI Conference on Human Factors in Computing Systems, 2019

Konferenz
This paper proposes methods of optimising alphabet encoding for skin reading in order to avoid perception errors. First, a user study with 16 participants using two body locations serves to identify issues in recognition of both individual letters and words. To avoid such issues, a two-step optimisation method of the symbol encoding is proposed and validated in a second user study with eight participants using the optimised encoding with a seven vibromotor wearable layout on the back of the hand. The results show significant improvements in the recognition accuracy of letters (97%) and words (97%) when compared to the non-optimised encoding.
2019

Luzhnica Granit, Veas Eduardo Enrique

Background Perception and Comprehension of Symbols Conveyed through Vibrotactile Wearable Displays

ACM International Conference on Intelligent User Interfaces , Los Angelos, 2019

Konferenz
2019

Remonda Adrian, Krebs Sarah, Luzhnica Granit, Kern Roman, Veas Eduardo Enrique

Formula RL: Deep Reinforcement Learning for Autonomous Racing usingTelemetry Data

Workshop on Scaling-Up Reinforcement Learning (SURL) @ Int. Joint Conf. on Artificial Intelligence, 2019

Konferenz
This paper explores the use of reinforcement learning (RL) models for autonomous racing. In contrast to passenger cars, where safety is the top priority, a racing car aims to minimize the lap-time. We frame the problem as a reinforcement learning task witha multidimensional input consisting of the vehicle telemetry, and a continuous action space. To findout which RL methods better solve the problem and whether the obtained models generalize to drivingon unknown tracks, we put 10 variants of deep deterministic policy gradient (DDPG) to race in two experiments: i) studying how RL methods learn to drive a racing car and ii) studying how the learning scenario influences the capability of the models to generalize. Our studies show that models trained with RL are not only able to drive faster than the baseline open source handcrafted bots but also generalize to unknown tracks.
2019

Luzhnica Granit, Veas Eduardo Enrique

Boosting Word Recognition for Vibrotactile Skin Reading

ACM International Symposium on Wearable Computing, 2019

Konferenz
Proficiency in any form of reading requires a considerable amount of practice. With exposure, people get better at recognising words, because they develop strategies that enable them to read faster. This paper describes a study investigating recognition of words encoded with a 6-channel vibrotactile display. We train 22 users to recognise ten letters of the English alphabet. Additionally, we repeatedly expose users to 12 words in the form of training and reinforcement testing.Then, we test participants on exposed and unexposed words to observe the effects of exposure to words. Our study shows that, with exposure to words, participants did significantly improve on recognition of exposed words. The findings suggest that such a word exposure technique could be used during the training of novice users in order to boost the word recognition of a particular dictionary of words.
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