Követés
Martin Gjoreski
Martin Gjoreski
SNSF Ambizione Fellow, Scientific Collaborator, Università della Svizzera italiana, CH
E-mail megerősítve itt: usi.ch - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
Monitoring stress with a wrist device using context
M Gjoreski, M Luątrek, M Gams, H Gjoreski
Journal of biomedical informatics 73, 159-170, 2017
3072017
Continuous stress detection using a wrist device: in laboratory and real life
M Gjoreski, H Gjoreski, M Luątrek, M Gams
proceedings of the 2016 ACM international joint conference on pervasive and …, 2016
2282016
Non-invasive blood pressure estimation from ECG using machine learning techniques
M Simjanoska, M Gjoreski, M Gams, A Madevska Bogdanova
Sensors 18 (4), 1160, 2018
1612018
How accurately can your wrist device recognize daily activities and detect falls?
M Gjoreski, H Gjoreski, M Luątrek, M Gams
Sensors 16 (6), 800, 2016
1442016
Machine learning and end-to-end deep learning for the detection of chronic heart failure from heart sounds
M Gjoreski, A Gradiąek, B Budna, M Gams, G Poglajen
Ieee Access 8, 20313-20324, 2020
992020
Automatic detection of perceived stress in campus students using smartphones
M Gjoreski, H Gjoreski, M Lutrek, M Gams
2015 International conference on intelligent environments, 132-135, 2015
942015
Comparing deep and classical machine learning methods for human activity recognition using wrist accelerometer
H Gjoreski, J Bizjak, M Gjoreski, M Gams
Proceedings of the IJCAI 2016 Workshop on Deep Learning for Artificial …, 2016
872016
Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors
M Gjoreski, V Janko, G Slapničar, M Mlakar, N Reąčič, J Bizjak, V Drobnič, ...
Information Fusion 62, 47-62, 2020
852020
Datasets for cognitive load inference using wearable sensors and psychological traits
M Gjoreski, T Kolenik, T Knez, M Luątrek, M Gams, H Gjoreski, V Pejović
Applied Sciences 10 (11), 3843, 2020
612020
Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals
M Gjoreski, M® Gams, M Luątrek, P Genc, JU Garbas, T Hassan
IEEE access 8, 70590-70603, 2020
592020
Chronic heart failure detection from heart sounds using a stack of machine-learning classifiers
M Gjoreski, M Simjanoska, A Gradiąek, A Peterlin, M Gams, G Poglajen
2017 International Conference on Intelligent Environments (IE), 14-19, 2017
472017
Can we ditch feature engineering? end-to-end deep learning for affect recognition from physiological sensor data
M Dzieżyc, M Gjoreski, P Kazienko, S Saganowski, M Gams
Sensors 20 (22), 6535, 2020
392020
A new frontier for activity recognition: The Sussex-Huawei locomotion challenge
V Janko, N Reąçiç, M Mlakar, V Drobnič, M Gams, G Slapničar, M Gjoreski, ...
Proceedings of the 2018 ACM International Joint Conference and 2018 …, 2018
322018
Detection of artifacts in ambulatory electrodermal activity data
S Gashi, E Di Lascio, B Stancu, VD Swain, V Mishra, M Gjoreski, S Santini
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2020
302020
Machine learning approach for emotion recognition in speech
M Gjoreski, H Gjoreski, A Kulakov
Informatica, 2014
272014
My watch says I'm busy: Inferring cognitive load with low-cost wearables
M Gjoreski, M Luątrek, V Pejović
Proceedings of the 2018 ACM International Joint Conference and 2018 …, 2018
252018
Cross-dataset deep transfer learning for activity recognition
M Gjoreski, S Kalabakov, M Luątrek, M Gams, H Gjoreski
Adjunct proceedings of the 2019 ACM international joint conference on …, 2019
232019
Applying multiple knowledge to Sussex-Huawei locomotion challenge
M Gjoreski, V Janko, N Reąčič, M Mlakar, M Luątrek, J Bizjak, G Slapničar, ...
Proceedings of the 2018 ACM International Joint Conference and 2018 …, 2018
222018
Deep affect recognition from RR intervals
M Gjoreski, H Gjoreski, M Luątrek, M Gams
Proceedings of the 2017 ACM international joint conference on pervasive and …, 2017
202017
Cognitive load monitoring with wearables–lessons learned from a machine learning challenge
M Gjoreski, B Mahesh, T Kolenik, J Uwe-Garbas, D Seuss, H Gjoreski, ...
IEEE Access 9, 103325-103336, 2021
192021
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