| Monitoring stress with a wrist device using context M Gjoreski, M Luštrek, M Gams, H Gjoreski Journal of biomedical informatics 73, 159-170, 2017 | 347 | 2017 |
| Accelerometer placement for posture recognition and fall detection H Gjoreski, M Lustrek, M Gams 2011 Seventh International Conference on Intelligent Environments, 47-54, 2011 | 302 | 2011 |
| 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 | 245 | 2016 |
| An agent-based approach to care in independent living B Kaluža, V Mirchevska, E Dovgan, M Luštrek, M Gams Ambient Intelligence: First International Joint Conference, AmI 2010, Malaga …, 2010 | 211 | 2010 |
| 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 | 177 | 2018 |
| Automatic recognition of gait-related health problems in the elderly using machine learning B Pogorelc, Z Bosnić, M Gams Multimedia tools and applications 58, 333-354, 2012 | 165 | 2012 |
| 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 | 145 | 2016 |
| Artificial intelligence and ambient intelligence M Gams, IYH Gu, A Härmä, A Muñoz, V Tam Journal of Ambient Intelligence and Smart Environments 11 (1), 71-86, 2019 | 133 | 2019 |
| 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 | 113 | 2020 |
| Transforming arbitrary tables into logical form with TARTAR A Pivk, P Cimiano, Y Sure, M Gams, V Rajkovič, R Studer Data & Knowledge Engineering 60 (3), 567-595, 2007 | 110 | 2007 |
| 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 | 102 | 2015 |
| 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 | 99 | 2020 |
| New measurements highlight the importance of redundant knowledge M Gams Proceedings of the fourth european working session on learning, 71-79, 1989 | 95 | 1989 |
| What makes classification trees comprehensible? M Luštrek, M Gams, S Martinčić-Ipšić Expert Systems with Applications 62, 333-346, 2016 | 91 | 2016 |
| Learning in distributed systems and multi-agent environments P Brazdil, M Gams, S Sian, L Torgo, W Van de Velde Machine Learning—EWSL-91: European Working Session on Learning Porto …, 1991 | 91 | 1991 |
| 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 | 87 | 2016 |
| 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 | 76 | 2020 |
| Efficient activity recognition and fall detection using accelerometers S Kozina, H Gjoreski, M Gams, M Luštrek Evaluating AAL Systems Through Competitive Benchmarking: International …, 2013 | 76 | 2013 |
| 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 | 74 | 2020 |
| Context-based ensemble method for human energy expenditure estimation H Gjoreski, B Kaluža, M Gams, R Milić, M Luštrek Applied Soft Computing 37, 960-970, 2015 | 69 | 2015 |
Mitja LuštrekArtificial intelligence researcher, Jožef Stefan InstituteVerified email at ijs.si
