Hristijan Gjoreski

TL;DR: This work explores the problem of stress detection using machine learning and signal processing techniques in laboratory conditions, and then applies the extracted laboratory knowledge to real-life data to propose a novel context-based stress-detection method.

TL;DR: Chest and waist accelerometers proved best at both tasks, with the chest accelerometer having a slight advantage in posture recognition.

TL;DR: This paper presents a highly versatile and precisely annotated large-scale data set of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users, and presents how a machine-learning system can use this data set to automatically recognize modes of transportations.

TL;DR: 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.

TL;DR: A systematic study of the relevance of statistical and frequency features based on the information theoretical criteria to inform recognition systems and systematically reports the reference performance obtained on all the identified recognition scenarios using a machine-learning recognition pipeline.