I use the term ambient intelligence for all the research on the interpretation
of sensor data, mobile and context-sensitive applications. This research area corresponds
to (or maybe intersects with) the areas of ubiquitous and pervasive computing. I am particularly
interested in analyzing human physical and psychological condition and behavior
with sensors, but I occasionally also
work on building automation and related topics. My main domain of interest is
health, and because of that I throw in some health-related research that has
little to do with ambient intelligence.
This page first lists all the projects I worked on (as a principal
investigator, with the exception of the Confidence project), followed by
publications. Quick links to these things can be found on top of the page.
In this AAL project we are
developing a system that will help seniors with memory impairment better tackle
their activities of daily living. We will do this by recognizing their
activities and needs using computer vision and other types of sensing, and
providing reminders and instructions adapted to their current situation. Our
department is focusing on computer vision and sensing with wearables.
STRAW (2018-2022)
The goal of this Flemish-Slovenian project
is to improve the understanding of risk factors for day-to-day stress in the
workplace. We are also developing methods for stress detection from
physiological signals and behavioural cues obtained from smartphones.
Insension (2018-2021)
The goal of this H2020 project is to
interpret the behaviour of people with profound intellectual and multiple
disabilities, and to provide them with a rudimentary control over digital
services by reacting to their expressions of pleasure and displeasure, as well
as some other communication attempts. Our department
is developing methods for extracting physiological signals from video,
and for high-level interpretation of the user's behaviour.
Project video
Photoplethysmogram (PPG) reconstruction from video
WellCo (2017-2021)
This is a H2020 project developing a
virtual coach to advise seniors on wellbeing and health. Our deaprtment is
developing methods for nutrition monitoring with food-frequency questionnaires
and sensors in a smartwatch. We are also working on emotion detection from
speech, which is intended to improve spoken interaction with the coach.
CrowdHealth (2017-2020)
The goal of this H2020 project was to develop a platform to analyse big data related to health, in order to craft
better public health policies. Our department focused on forecasting,
clustering and risk assessment methods. We closely collaborated with the
Faculty of Sport on the
SloFit dataset, which is collected throught a national
surveillance system for physical and motor development of children and youth.
HeartMan (2016-2019)
I was the coordinator of the HeartMan H2020
project, which developed a personal health system for congestive heart failure
patients.
The system consists of a mobile application, a sensing wristband, a backend for
data management, and a web application for medical professionals. It provides
comprehensive advice on the self-management of heart failure, ranging from physical exercise,
nutrition, medication and self-monitoring, to psychological support. Our department
adapted the methods for physical activity monitoring from the previous Fit4Work project,
and developed methods for blood pressure estimation from photoplethysmogram, as well as the
decision support system for providing the advice to the patients. The project
was inspired by our previous work on the Chiron project.
Project video
Blood pressure estimation when stationary
Blood pressure estimation after exercise
e-Gibalec (2015)
This was a national project in which we developed a mobile application to
encourage children to exercise. The application was based on gamification: the
children could earn virtual currency by being physically active, and buy
equipment for their avatars with this currency. The physical activity was
detected automatically via smartphone sensors, or entered manually and confirmed
by parents. The collected data was accessible to the users' physical education
teachers.
Fit4Work (2014-2017)
This was an AAL project in which we
developed a mobile application to help older workers improve and maintain their
physical fitness, cope with mental stress and manage their work environment (temperature, humidity, CO2 level ...).
The application was connected to a wristband with physiological sensors that was
used for activity monitoring and stress detection. Our department adapted the
methods for physical activity monitoring from the Commodity12 project to use the
wristband, and developed methods for stress
monitoring and recommending best actions to improve the work environment (e.g.,
opening the window).
Promotional video
Activity and stress monitoring
Commodity12 (2013-2015)
This was an FP7 project in which we developed a personal health system for
diabetic patients. The system consisted of a mobile aplication and an optional
ECG monitor (with an embedded accelerometer) for cardiovascular comorbidities.
Our department developed methods for physical activity recognition and human
energy expenditure estimation with the smartphone and/or the ECG monitor, which
automatically adapted to the current device configuration. We also analysed ECG
data to predict glycemias.
Human activity recognition and energy expenditure estimation
Chiron (2010-2014)
This was a large Artemis project that developed a framework for patient monitoring
and decision support at home and in clinical environments. Our department
developed methods for physical activity monitoring of congestive heart failure
patients with wearable sensors, and a decision support system to assess their
health risk.
Confidence (2008-2011)
This FP7 project was my first foray into the area of ambient intelligence. The
goal of the project was to detect falls and other health problems of the elderly
by recognizing anomalies in their posture and movement. This was done using location
sensors that detected one to four tags worn on the user' bodies. Our department
developed method for fall detection and the detection of other health problems.
Publications
2021
Bohanec, M., Vodopija, A., Luštrek, M., et al. (2021). HeartMan DSS: A
decision support system for self-management of congestive heart failure.
Expert Systems with Applications 186. [paper
in pdf] [link]
Krishnamurthy, S., Kapeleshh, K. S., Dovgan, E., Luštrek, M., Gradišek
Piletič, B., Srinivasan, K., Li, Y.-C. (J.), Gradišek, A., and Syed-Abdul,
S. (2021). Machine learning prediction models for chronic kidney disease
using national health insurance claim data in Taiwan.
Healthcare 9 (5).
[paper in pdf] [link]
Gjoreski, M., Mahesh, B., Kolenik, T., Uwe-Gabras, J., Seuss, D.,
Gjoreski, H., Luštrek, M., Gams, M., and Pejović, V. (2021). Cognitive load
monitoring with wearables - Lessons learned from a machine learning
challenge. IEEE Access 9. [paper
in pdf] [link]
Luštrek, M., Bohanec, M., Dovgan, E., Lampe, J., Mlakar, M., Slapničar,
G., Valič, J., Vodopija, A., et al. (2021). A personal health system for
self-management of congestive heart failure (HeartMan): Development,
technical evaluation, and proof-of-concept randomized controlled trial. JMIR Medical Informatics 9 (3).
[paper in pdf] [link]
Janko, V., and Luštrek, M. (2021). A general framework for making
context-recognition systems more energy efficient. Sensors 21 (3). [paper in
pdf] [link]
Clays, E., Luštrek, M., Bohanec, M., Valič, J., Vodopija, A., et al.
(2021). Proof-of-concept trial results of the HeartMan mobile personal
health system for self-management in congestive heart failure. Nature Scientific Reports 11.
[paper in pdf] [link]
2020
Picard, C., Janko, V., Reščič, N., Gjoreski, M., and Luštrek, M. (2020).
Identification of cooking preparation using motion capture data: A
submission to the cooking activity recognition challenge. Human Activity
Recognition Challenge. [paper in pdf]
Álvarez-García, J. A., Cvetković, B., and Luštrek, M. (2020). A survey
on energy expenditure estimation using wearable devices.
ACM Computing
Surveys 53 (5). [paper in pdf] [link]
Gjoreski, M., Kolenik, T., Knez, T., Luštrek, M., Gams, M., Gjoreski,
H., and Pejović, V. (2020). Datasets for cognitive load inference using
wearable sensors and psychological traits.
Applied Sciences 10 (11). [paper
in pdf] [link]
Gjoreski, M., Gams, M., Luštrek, M., Genc, P., Garbas, J.-U., Hassan, T.
(2020). Machine learning and end-to-end deep learning for monitoring driver
distractions from physiological and visual signals.
IEEE Access 8. [paper in
pdf] [link]
Gjoreski, M., Janko, V., Slapničar, G., Mlakar, M., Reščič, N., Bizjak,
J., Drobnič, V., Marinko, M., Mlakar, N., Luštrek, M., and Gams, M. (2020).
Classical and deep learning methods for recognizing human activities and
modes of transportation with smartphone sensors.
Information Fusion 62.
[paper in pdf] [link]
Dovgan, E., Gradišek, A., Luštrek, M., Uddin, M., Nursetyo, A. A., Kiran
Annavarajula, S., Li, Y.-C., and Syed-Abdul, S. (2020). Using machine
learning models to predict the initiation of renal replacement therapy among
chronic kidney disease patients.
PLoS One 15 (6). [paper in pdf] [link]
Reščič, N., Eftimov, T., Koroušić-Seljak, B., and Luštrek, M. (2020).
Optimising an FFQ using a machine learning pipeline to teach an efficient
nutrient intake predictive model.
Nutrients 12 (12). [paper
in pdf] [link]
Bolliger, L., Lukan, J., Luštrek, M., De Bacquer, D., and Clays, E.
(2020). Protocol of the STRess at Work (STRAW) project: How to disentangle
day-to-day occupational stress among academics based on EMA, physiological
data, and smartphone sensor and usage data.
International Journal of
Environmental Research and Public Health 17 (23). [paper
in pdf] [link]
Vodopija, A., Janko, V., Luštrek, M., and Filipič, B. (2020).
Constrained multiobjective optimization for the design of energy-efficient
context recognition systems. BIOMA
conference. [paper in pdf]
Slapničar, G., Janko, V., Kolenik, T., Luštrek, M., and Gams, M. (2020).
Cognitive, psychological and social influence on spread of COVID-19. Information Society conference.
[paper in pdf]
Slapničar, G., Dovgan, E., Valič, J., and Luštrek, M. (2020). Mental
state estimation of people with PIMD using physiological signals.
Information Society conference. [paper in
pdf]
Reščič, N., Jordan, M., De Boer, J., Bierhoff, I., and Luštrek, M.
(2020). Mobile nutrition monitoring system: Qualitative and quantitative
monitoring.
Information Society conference. [paper in
pdf]
Lukan, J., Katrašnik, M., Bolliger, L., Clays, E., and Luštrek, M.
(2020). STRAW application for collecting context data and ecological
momentary assessment.
Information Society conference. [paper in
pdf]
Kalabakov, S., Stankoski, S., Reščič, N., Kiprijanovska, I., Andova, A.,
Picard, C., Janko, V., Gjoreski, M., Luštrek, M. (2020). Tackling the SHL
challenge 2020 with person-specific classifiers and semi-supervised
learning. Ubicomp conference,
HASCA workshop. [paper
in pdf] SHL Challenge 2020 third place
Monto, L., Schiariti, M., Fedele Calvisi, P., Bonfiglio, S., Luštrek,
M., and Puddu, P. E. (2020). Association of patient-reported outcomes and
heart rate trends in heart failure: A report from the Chiron project.
Nature
Scientific Reports 10. [paper in pdf] [link]
Mavrogiorgou, A., Kiourtis, A., Maglogiannis, I., Kyriazis, D., De
Nigro, A., Selva, V. B., García-Gómez, J., Luštrek, M., Gradišek, A., et
al., and Gallos, P. (2020). CrowdHEALTH: An e-health big data
driven platform towards public health policies.
ICT4AWE conference. [paper in pdf]
Gjoreski, H., Stankoski, S., Kiprijanovska, I., Nikolovska, A.,
Mladenovska, N., Trajanoska, M., Velichkovska, B., Gjoreski, M., Luštrek,
M., Gams, M. (2020). Wearable sensors data-fusion and machine-learning
method for fall detection and activity recognition. Challenges and Trends in
Multimodal Fall Detection for Healthcare. [link]
Challenge UP winner
2019
Kyriazis, D., Autexier, S., Boniface, M., Engen, V., Jimenez-Peris, R.,
Jordan, B., Jurak, G., Kiourtis, A., Luštrek, M., at al., and Wajid, U. (2019).
The CrowdHEALTH project and the Hollistic Health Records: Collective wisdom
driving public health policies. Acta
Informatica Medica 27 (5). [paper
in pdf] [link]
Slapničar, G., Mlakar, N., and Luštrek, M. (2019). Blood pressure
estimation from photoplethysmogram using a spectrotemporal deep neural
network. Sensors 19 (15).
[paper
in pdf] [link]
Dovgan, E., Leskošek, B., Jurak, G., Starc, G., Sorić, M., and Luštrek,
M. (2019). Enhancing BMI-based student clusteringby considering fitness as
key attribute. Discovery Science conference.
[paper in pdf]
Gallos, P., Aso, S., Autexier,S., Brotons, A., De Nigro, A., Jurak, G.,
Kiourtis, A., Kranas, P., Luštrek, M., et al, and Vizitiu, A.-M. (2019).
CrowdHEALTH: Big data analytics and holistic health records.
EFMI
Special Topic Conference.
Janko, V., Gjoreski, M., Slapničar, G., Mlakar, M., Reščič, N., Bizjak,
J., Drobnič, V., Marinko, M., Mlakar, N., Gams, M., and Luštrek, M. (2019).
Winning the Sussex-Huawei locomotion-transportation recognition challenge.
Human Activity Sensing: Corpus and Applications. [chapter
in pdf] [link]
Kosiedowski, M., Radziuk,A., Szymaniak, P., Kapsa, W., Rajtar, T.,
Stroinski, M., Campomanes-Alvarez, C., Campomanes-Alvarez, B. R., Luštrek,
M., Cigale, M., Dovgan, E., and Slapničar, G. (2019). On applying ambient
intelligence to assist people with profound intellectual and multiple
disabilities.
Intelligent Systems Conference.
[paper
in pdf] [link]
Cigale, M., Slapničar, G., and Luštrek, M. (2019). Categorising
behavioural states of people with profound intellectual and multiple
disabilities. Information Society conference.
[paper
in pdf]
Dovgan, E., Luštrek, M. (2019). Fitness-based student clustering
combining clustering algorithms and dimensionality reduction.
Information Society conference. [paper
in pdf]
Valič, J., Mlakar, M., Budna, B., and Luštrek, M. (2019). Predictive
modelling of feeling of health for congestive heart failure patients.
Information Society conference. [paper
in pdf]
Katrašnik, M., Lukan, J., Luštrek, M., and Štruc, V. (2019). Razvoj
postopka diarizacije govorcev z algoritmi strojnega učenja.
Information Society conference. [paper
in pdf]
Best paper award
Janko, V., Gjoreski, M., De Masi, C., Reščič, N., Luštrek, M., and Gams,
M. (2019). Cross-location transfer learning for the Sussex-Huawei locomotion
recognition challenge. Ubicomp
conference, HASCA workshop.
[paper
in pdf]
SHL Challenge 2019 winner
Gjoreski, M., Kalabakov, S., Luštrek, M., Gams, M., and Gjoreski, H.
(2019). Cross-dataset deep transfer learning for activity recognition.
Ubicomp conference,
HASCA workshop. [paper
in pdf]
Baert, A., Clays, E., Bolliger, L., De Smedt, D., Luštrek, M., Vodopija,
A., Bohanec, M., Puddu, P. E., Ciancarelli M. C., et al., and Pardaens, S.,
on behalf of the HeartMan consortium (2018). A personal decision support
system for heart failure management (HeartMan): Study protocol of the
HeartMan randomized controlled trial.
BMC Cardiovascular
Disorders 18. [paper
in pdf] [link]
Slapničar, G., Luštrek, M., and Marinko, M. (2018). Continuous blood
pressure estimation from PPG signal.
Informatica 42 (1). [paper
in pdf]
Gjoreski, M, Luštrek, M., Gams, M., and Mitrevski, B. (2018). An
inter-domain study for arousal recognition from physiological signals. Informatica 42 (1). [paper
in pdf]
Cvetković, B., Szeklicki, R., Janko, V., Lutomski, P., and Luštrek, M.
(2018). Real-time activity monitoring with a wristband and a smartphone. Information
Fusion 43. [paper
in pdf][link]
Mlakar, M., Puddu, P. E., Somrak, M., Bonfiglio, S., and Luštrek, M.
(2018), on behalf of the Chiron and HeartMan research projects. Mining
telemonitored physiological data and patient-reported outcomes of congestive
heart failure patients. PLoS
One 13 (3). [paper
in pdf] [link]
Janko, V., and Luštrek, M. (2018). Using Markov chains and
multi-objective optimization for energy-efficient context recognition. Sensors 18 (1). [paper
in pdf] [link]
Gradišek, A., Mlakar, M., Cigale, M., Lajovic, L., Luštrek, M., Sorić,
M., Starc, G., Leskošek, B., and Jurak, G. (2018). Physical fitness
forecasting and risk estimation in Slovenian schoolchildren. ICIMTH conference. [paper
in pdf]
Cigale, M., Gradišek, A., Mlakar, M., and Luštrek, M. (2018).
Forecasting the physical fitness and all-cause mortality based on
schoolchildren's fitness measurements.
Information Society conference. [paper
in pdf]
Cigale, M., Luštrek, M., Gams, M., Krämer, T., Engelhardt, M., and
Zentel, P. (2018). The quest for understanding: Helping people with PIMD to
communicate with their caregivers. Information
Society conference. [paper
in pdf]
Trajanoska, M., Katrašnik, M., Lukan, J., Gjoreski, M., Gjoreski, H.,
and Luštrek, M. (2018). Context-aware stress detection in the AWARE
framework. Information Society conference.
[paper
in pdf]
Gjoreski, M., Luštrek, M., and Gams, M. (2018). Multi-task ensemble
learning for affect recognition. Ubicomp
conference, doctoral colloquium. [paper
in pdf]
Gjoreski, M., Janko, V., Reščič, N., Mlakar, M., Luštrek, M., Bizjak,
J., Slapničar, G., Marinko, M., Drobnič, V., and Gams, M. (2018). Applying
multiple knowledge to Sussex-Huawei locomotion challenge. Ubicomp
conference, HASCA workshop.
[paper in pdf] SHL Challenge 2018 winner
Janko, V., Reščič, N., Mlakar, M., Drobnič, V., Gams, M., Slapničar, G.,
Gjoreski, M., Bizjak, J., Marinko, M., and Luštrek, M. (2018). A new
frontier for activity recognition : The Sussex-Huawei locomotion challenge. Ubicomp
conference, HASCA workshop.
[paper in pdf] SHL challenge 2018 second place
Kužnar, D., Piltaver, R., Gradišek, A., Gams, M., and Luštrek, M.
(2017). An intelligent system to monitor refrigeration devices.
Expert
Systems 34 (5). [paper
in pdf] [link]
Cvetković, B., Gjoreski, M., Šorn, J., Frešer, M., Luštrek, M.,
Bogdański, M., Jackowska, K., Kosiedowski, M., and Stroiński, A. (2017).
Management of physical, mental and environmental stress at the workplace.
Intelligent Environments conference.
[paper
in pdf]
Kyriazis, D., Autexier, S., Brondino, I., Boniface, M., Donat, L.,
Engen, V., Fernandez, R., Jimenez-Peris, R., Luštrek, M., et al., and Wajid,
U. (2017). CrowdHEALTH: Holistic health records and big data analytics for
health policy making and personalized health.
ICIMTH conference. [paper
in pdf]
Cvetković, B., Gjoreski, M., Šorn, J., Maslov, P., and Luštrek, M.
(2017). Monitoring physical activity and mental stress using wrist-worn
device and a smartphone. ECML PKDD conference, demo.
[paper
in pdf]
Mlakar, M., and Luštrek, M. (2017). Automatic tennis analysis with the
use of machine learning and multi-objective optimization. Information Society
conference. [paper
in pdf]
Bohanec, M., Dovgan, E., Maslov, P., Vodopija, A., Luštrek, M., Puddu,
P. E., Schiariti, M., Ciancarelli, M. C., Baert, A., Pardaens, S., and
Clays, E. (2017). Designing a personal decision support system for
congestive heart failure management. Information Society
conference. [paper
in pdf]
Gjoreski, M., Mitrevski, B., Luštrek, M., and Gams, M. (2017). R-R vs
GSR - An inter-domain study for arousal recognition. Information Society
conference. [paper
in pdf]
Cvetković, B., Drobnič, V., and Luštrek, M. (2017). Recognizing
hand-specific activities with a smartwatch placed on dominant or
non-dominant wrist. Information Society
conference. [paper
in pdf]
Mlakar, M., and Luštrek, M. (2017). Analyzing tennis game through sensor
data with machine learning and multi-objective optimization. Ubicomp conference, poster. [paper
in pdf] [poster
in pdf]
Cvetković, B., Gjoreski, M., Šorn, J., Maslov, P., Kosiedowski, M.,
Bogdański, M., Stroiński, A., and Luštrek, M. (2017). Real-time physical
activity and mental stress management with a wristband and a smartphone.
Ubicomp conference, demo. [paper
in pdf]
Cvetković, B., Janko, V., Romero, A. E., Kafali, Ö., Stathis, K., and Luštrek,
M. (2016). Activity recognition for diabetic patients using a smartphone.
Journal of Medical
Systems 40 (12). [paper
in pdf] [link]
Luštrek, M., Dovgan, E., Vodopija, A., Bohanec, M., Baert, A., Pardaens,
S., and Clays, E. (2016). Aplikacija za pomoč pri telesni vadbi bolnikov s
srčnim popuščanjem. Information Society
conference. [paper
in pdf]
Vodopija, A., Cvetković, B., Luštrek, M., Rudel, D., and Balorda, Z.
(2016). Napovedovanje hospitalizaciji pri bolnikih s srčnim popuščanjem iz
podatkov telespremljanja. Information Society
conference. [paper
in pdf]
Cvetković, B., Gjoreski, M., Frešer, M., Kosiedowski, M., and Luštrek,
M. (2016). Monitoring and management of physical, mental and environmental
stress at work. Information Society
conference. [paper
in pdf]
Frešer, M., Cvetković, B., Gradišek, A., Luštrek, M. (2016). An
intelligent system to improve T-H-C parameters at the workplace.
Ubicomp conference, poster.
[paper in pdf]
Cvetković, B., Janko, V., Gradišek, A., Luštrek, M., Kajtna, T., and
Štrumbelj, B. (2016). Mobile application to stimulate physical activity in
schoolchildren.
Intelligent Environments conference. [paper
in pdf]
Cvetković, B., Pangerc, U., Gradišek, A., and Luštrek, M. (2016).
Monitoring patients with diabetes using wearable sensors: Predicting
glycaemias using ECG and respiration rate.
ECAI conference,
AID workshop.
[paper in pdf]
Cvetković, B., Vodopija, A., Rudel, D., Balorda, Z., and Luštrek, M.
(2016). Hospitalisation prediction from telemonitoring data in congestive
heart failure patients. IJCAI conference,
KDHealth workshop.
[paper
in pdf]
Gjoreski, M., Gjoreski, H., Luštrek, M., and Gams, M. (2016).
How accurately can your wrist device recognize daily activities and detect falls?
Sensors 16 (6). [paper
in pdf] [link]
Gjoreski, M., Gjoreski, H., Cvetković, B., Luštrek, M., and Gams, M.
(2016). Activity and stress monitoring using smartphone and wrist device. IPSSC conference. [paper
in pdf]
Gjoreski, H., Kaluža, B., Gams, M., Milić, R., and Luštrek, M. (2015). Context-based
ensemble method for human energy expenditure estimation.
Applied Soft
Computing 37. [paper
in pdf] [link]
Zupančič, D., Luštrek, M., and Gams, M. (2015). Multi-agent architecture
for control of heating and cooling in a residential space.
The Computer Journal 58 (6).
[paper
in pdf] [link]
Luštrek, M., Gjoreski, H., Gonzáles Vega, N., Kozina, S., Cvetković, B.,
Mirčevska, V., and Gams, M. (2015). Fall detection using location sensors and
accelerometers. IEEE
Pervasive Computing 14 (4). [paper
in pdf] [link]
Cvetković, B., Piltaver, R., Gams, M., and Luštrek, M. (2015). Evolution
of activity monitoring through various projects.
ICT Innovations conference. [paper
in pdf]
Cvetković, B., Pangerc, U., and Luštrek, M. (2015). Prepoznavanje in
napovedovanje hiperglikemij in hipoglikemij na neinvaziven način. Information Society
conference. [paper
in pdf]
Gjoreski, M., Gjoreski, H., Luštrek, M., and Gams, M.
(2015). Recognizing atomic activities with wrist-worn accelerometer using
machine learning. Information Society
conference. [paper
in pdf]
Somark, M., Gradišek, A., Luštrek, M., and Gams, M. (2015).
Prepoznavanje bolezni na podlagi vprašalnika in meritev s senzorji vitalnih
znakov. Information Society
conference. [paper
in pdf]
Luštrek, M., Cvetković, B., Janko, V., Štrumbelj, B., Štihec, J., and Kajtna,
T. (2015). Mobile application encouraging physical activity of schoolchildren
for more effective physical education. VIVID conference. [paper
in pdf]
Gjoreski, M., Gjoreski, H., Luštrek, M., and Gams, M. (2015). Towards unobtrusive
stress detection. IPSSC conference. [paper
in pdf]
Cvetković, B., Janko, V., and Luštrek, M. (2015). Demo abstract:
Activity recognition and human energy expenditure estimation with a
smartphone. PerCom conference, demo. [paper
in pdf] [link]
Gjoreski, H., Kozina, S., Gams, M., Luštrek, M., Álvarez-García, J. A.,
Hong, J.-H., Ramos, J., Dey, A. K., Bocca, M., and Patwari, N. (2015). Competitive
live evaluations of activity-recognition systems.
IEEE Pervasive Computing
14 (1). [paper
in pdf] [link]
2014
Luštrek, M., Cvetković, B., Janko, V. (2014). Monitoring patientsʼ
lifestyle with a smartphone and other devices placed freely on the body.
AmI conference. [paper
in pdf]
Somrak, M., Gradišek, A., Luštrek, M., Mlinar, A., Sok, M., and Gams, M.
(2014). Medical diagnostics based on combination of sensor and user-provided
data. ECAI conference,
AI-AM/NetMed workshop.
[paper
in pdf]
Gjoreski, H., Kozina, S., Luštrek, M., and Gams, M. (2014). Using multiple
contexts to distinguish standing from sitting with a single accelerometer.
ECAI conference. [paper
in pdf]
Mirčevska, V. Luštrek, M., and Gams, M. (2014). Combining domain knowledge
and machine learning for robust fall detection.
Expert
Systems 31 (2). [pre-review
paper in pdf] [link]
Gjoreski, H., Rashkovska, A., Kozina, S., Luštrek, M., and Gams, M. (2014).
Telehealth using ECG sensor and accelerometer.
MIPRO conference. [paper
in pdf]
Gjoreski, H., Kozina, S., Luštrek, M., and Gams, M. (2014). Recognizing
human activities and detecting falls in real-time.
IPSSC conference. [paper
in pdf] Best paper award
Somrak, M., Luštrek, M., Šušteršič, J., Krivic, T., Mlinar, A., Travnik,
T., Stepan, L., Mavsar, M., and Gams, M. (2014). Tricorder: Consumer medical
devices for discovering common medical conditions.
Informatica 38 (1). [paper
in pdf] XPrize finalist
Gjoreski, H., Kozina, S., Gams, M., and Luštrek, M. (2014). RAReFall - Real-time
activity recognition and fall detection system.
PerCom conference, demo. [paper
in pdf]
Kaluža, B., Cvetković, B., Dovgan, E., Gjoreski, H., Gams, M., and Luštrek,
M. (2014). A multi-agent care system to support independent living. International
Journal on Artificial Intelligence Tools 23 (1). [paper
in pdf] [link]
Mazomenos, E., Rodríguez, J. M., Cavero, C., Tartarisco, G., Pioggia, G.,
Cvetković, B., Kozina, S., Gjoreski, H., Luštrek, M., Solar, H., Marinčič, D.,
Lampe, J., Bonfiglio, S., and Maharatna, K. (2014). Case studies. System Design
for Remote Healthcare. [link]
Cavero, C., Rodríguez, J. M., Puddu, P. E., Luštrek, M., Cvetković, B.,
Bordone, M., Soudah, E., Moreno, A., de la Pena, P., Rugnone, A., Foresti, F.,
and Tamburini, E. (2013). Advanced medical expert support tool (A-MEST): EHR-based
integration of multiple risk assessment solutions for congestive heart failure
patients. Mediterranean Conference on
Medical and Biological Engineering and Computing. [paper
in pdf] [link]
Gjoreski, H., Kaluža, B., Gams, M., Milić, R., and Luštrek, M. (2013). Ensembles
of multiple sensors for human energy expenditure estimation.
Ubicomp conference. [paper
in pdf]
Zupančič, D., Gams, M., Luštrek, M. (2013). Vmesnik za povezavo simuliranega
sistema stavbne avtomatike z več agentnim sistemom vodenja.
Information Society conference. [paper
in pdf]
Cvetković, B., and Luštrek, M. (2013). Estimation of human energy expenditure
using inertial sensors and heart rate sensor.
IPSSC conference. [paper
in pdf]
Gjoreski, H., Kozina, S., Gams, M., and Luštrek, M. (2013) Potential usage
of smartphone inertial sensors in healthcare applications.
IPSSC conference. [paper
in pdf]
Solar, H., Fernández, E., Tartarisco, G., Pioggia, G., Cvetković, B., Kozina,
S., Luštrek, M., and Lampe, J. (2013). A non invasive, wearable sensor platform
for multi-parametric remote monitoring in CHF patients.
Health and Technology. [paper
in pdf] [link]
Luštrek, M., Cvetković, B., Bordone, M., Soudah, E., Cavero, C., Rodríguez,
J. M., Moreno, A., Brasaola, A., and Puddu, P. E. (2012). Supporting clinical
professionals in decision-making for patients with chronic diseases.
Information Society conference. [paper
in pdf] [slides
in pptx]
Kozina, S., Kaluža, B., and Luštrek, M. (2012). Z repozitorijem za prepoznavanje
aktivnosti do bolj primerljivih rezultatov in hitrejšega napredka na področju
ambientalne inteligence. Information Society conference.
[paper
in pdf]
Cavero, C., Rodríguez, J. M., Valle, R., Rugnone, A., Foresti, F., Tamburini,
E., Paggetti, C., Gialelis, G., Chondros, P., Panagiotou, C., Ilardia, M., Eguia,
I., Puddu, P. E., and Luštrek, M. (2012). Medical expert support tool (MEST):
A person-centric approach for healthcare management.
ICOST conference. [paper
in pdf] [link]
Solar, H., Fernández, E., Tartarsico, G., Pioggia, G., Cvetković, B., Kozina,
S., Luštrek, M., and Lampe, J. (2012). A non invasive, wearable sensor platform
for multi-parametric remote monitoring in CHF patients.
ICOST conference. [paper
in pdf] [link]
Kaluža, B., Luštrek, M., Dovgan, E., and Gams, M. (2012). Context-aware
MAS to support elderly people. AAMAS conference,
demo. [abstract
in pdf] [video]
CCvetković, B., and Luštrek, M. (2012). Risk assessment using local outlier
factor algorithm. IPSSC conference. [paper
in pdf]
2011
Dovgan, E., Luštrek, M., Pogorelc, B., Gradišek, A., Burger, H., and Gams,
M. (2011). Intelligent elderly-care prototype for fall and disease detection.
Slovenian Medical Journal 80 (11). [paper
in pdf]
Luštrek, M., Gjoreski, H., Kozina, S., Cvetković, B., Mirčevska, V., and
Gams, M. (2011). Detecting falls with location sensors and accelerometers.
IAAI conference.
[paper
in pdf] [slides
in zipped pptx]
Cvetković, B., Luštrek, M., Kaluža, B., and Gams, M. (2011). Semi-supervised
learning for adaptation of human activity recognition classifier to the user.
IJCAI conference,
STAMI
workshop.. [paper
in pdf]
Gams, M., Dovgan, E., Cvetković, B., Mirčevska, V., Kaluža, B., Luštrek,
M., and Vélez, I. (2011). AAL for supporting elderly.
IST-Africa conference.
[paper
in pdf]
Cvetković, B., Dovgan, E., Kaluža, B., Luštrek, M., Gams, M., and Mirčevska,
V. (2010). Improving the quality of life for elderly by adapting to each specific
user. Confidence
conference. [paper
in pdf]
Dovgan, E., Cvetković, B., Mirčevska, V., Kaluža, B., Luštrek, M., and Gams,
M. (2010). Confidence interpretation and prevention system.
Information Society conference. [paper
in pdf]
Mirčevska, V., Luštrek, M., and Gams, M. (2009). Combining machine learning
and expert knowledge for classifying human posture.
ERK conference. [paper
in pdf]
González, N., Kämäräinen, A., Luštrek, M., Gams, M., and Vélez, I. (2009).
From users' needs to system specifications: A care system supporting older people's
independent living.
CITSA conference.
[paper
in pdf]
Mirčevska, V., Luštrek, M., Vélez, I., González Vega, N., and Gams, M. (2009).
Classifying Posture Based on Location of Radio Tags.
Ambient Intelligence Forum. [paper
in pdf]
Luštrek, M., Gams, M., and Vélez, I. (2009). Posture and movement monitoring
for ambient assisted living.
IST-Africa conference.
[paper
in pdf].
Luštrek, M., and Kaluža, B. (2009). Fall detection and activity recognition
with machine learning. Informatica
33 (2). [paper
in pdf]
Kaluža, B., and Luštrek, M. (2008). Fall detection and activity recognition
methods for the Confidence project: A survey. Information
Society conference. [paper
in pdf]
