Author pages are created from data sourced from our academic publisher partnerships and public sources.
Share This Author
Monitoring stress with a wrist device using context
Accelerometer Placement for Posture Recognition and Fall Detection
- H. Gjoreski, M. Luštrek, M. Gams
- Computer ScienceSeventh International Conference on Intelligent…
- 25 July 2011
Chest and waist accelerometers proved best at both tasks, with the chest accelerometer having a slight advantage in posture recognition.
An Agent-Based Approach to Care in Independent Living
- Bostjan Kaluza, Violeta Mirchevska, E. Dovgan, M. Luštrek, M. Gams
- Computer ScienceAmI
- 10 November 2010
A multi-agent system for the care of elderly people living at home on their own, with the aim to prolong their independence, composed of seven groups of agents providing a reliable, robust and flexible monitoring.
Transforming arbitrary tables into logical form with TARTAR
Continuous stress detection using a wrist device: in laboratory and real life
A method for continuous detection of stressful events using data provided from a commercial wrist device that consists of three machine-learning components: a laboratory stress detector that detects short-term stress every 2 minutes; an activity recognizer that continuously recognizes user's activity and thus provides context information; and a context-based stress detectors that exploits the output of the laboratorystress detector and the user's context in order to provide the final decision on 20 minutes interval.
Automatic Detection of Perceived Stress in Campus Students Using Smartphones
- Martin Gjoreski, H. Gjoreski, M. Luštrek, M. Gams
- EducationInternational Conference on Intelligent…
- 15 July 2015
The findings show that the perceived stress is highly subjective and that only person-specific models are substantially better than the baseline.The goal is to develop a machine-learning model that can unobtrusively detect the stress level in students using data from several smartphone sources.
How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?
A thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets showed that the left wrist performs better compared to the dominant right one, and also better than the elbow and the chest, but worse than the ankle, knee and belt.
Context-based ensemble method for human energy expenditure estimation
Telehealth using ECG sensor and accelerometer
- H. Gjoreski, A. Rashkovska, S. Kozina, M. Luštrek, M. Gams
- Computer Science37th International Convention on Information and…
- 26 May 2014
A system that monitors the user by combining an ECG sensor and two accelerometers, which recognizes the user's activities and detects falls using the accelerometer data and could contribute significantly to the quality, unobtrusiveness and robustness of the health care and patient safety.
Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques
The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation.