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Martin Gjoreski
Martin Gjoreski
SNSF Ambizione Fellow, Scientific Collaborator, Università della Svizzera italiana, CH
Verified email at usi.ch - Homepage
Title
Cited by
Cited by
Year
Monitoring stress with a wrist device using context
M Gjoreski, M Luštrek, M Gams, H Gjoreski
Journal of biomedical informatics 73, 159-170, 2017
3122017
Continuous stress detection using a wrist device: in laboratory and real life
M Gjoreski, H Gjoreski, M Luštrek, M Gams
proceedings of the 2016 ACM international joint conference on pervasive and …, 2016
2322016
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
1622018
How accurately can your wrist device recognize daily activities and detect falls?
M Gjoreski, H Gjoreski, M Luštrek, M Gams
Sensors 16 (6), 800, 2016
1422016
Machine learning and end-to-end deep learning for the detection of chronic heart failure from heart sounds
M Gjoreski, A Gradišek, B Budna, M Gams, G Poglajen
Ieee Access 8, 20313-20324, 2020
1002020
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
962015
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 Reščič, J Bizjak, V Drobnič, ...
Information Fusion 62, 47-62, 2020
892020
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
862016
Datasets for cognitive load inference using wearable sensors and psychological traits
M Gjoreski, T Kolenik, T Knez, M Luštrek, M Gams, H Gjoreski, V Pejović
Applied Sciences 10 (11), 3843, 2020
642020
Machine learning and end-to-end deep learning for monitoring driver distractions from physiological and visual signals
M Gjoreski, MŽ Gams, M Luštrek, P Genc, JU Garbas, T Hassan
IEEE access 8, 70590-70603, 2020
602020
Chronic heart failure detection from heart sounds using a stack of machine-learning classifiers
M Gjoreski, M Simjanoska, A Gradišek, A Peterlin, M Gams, G Poglajen
2017 International Conference on Intelligent Environments (IE), 14-19, 2017
502017
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
402020
A new frontier for activity recognition: The Sussex-Huawei locomotion challenge
V Janko, N Rešçiç, 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
282014
My watch says I'm busy: Inferring cognitive load with low-cost wearables
M Gjoreski, M Luštrek, 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 Luštrek, 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 Reščič, M Mlakar, M Luštrek, 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 Luštrek, M Gams
Proceedings of the 2017 ACM international joint conference on pervasive and …, 2017
212017
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
202021
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