Showing papers by "Hristijan Gjoreski published in 2016"

Continuous stress detection using a wrist device: in laboratory and real life

Abstract: Continuous exposure to stress is harmful for mental and physical health, but to combat stress, one should first detect it. In this paper we propose a method for continuous detection of stressful events using data provided from a commercial wrist device. The method 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 detector that exploits the output of the laboratory stress detector and the user's context in order to provide the final decision on 20 minutes interval. The method was evaluated in a laboratory and a real-life setting. The accuracy on 55 days of real-life data, for a 2-class problem, was 92%. The method is currently being integrated in a smartphone application for managing mental health and well-being.

How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls

Abstract: Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity) dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject's daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data).

Using Smartwatch as Telecare and Fall Detection Device

Abstract: The constant increase of the elderly population and the need to prolong the independent live, is driving the development of telecare systems Two of the key factors of such systems are their simplicity of usage and minimal obtrusiveness Therefore, in this paper we propose a practical, intuitive and simple smartwatch-based telecare system The system has several functionalities, including: automatic fall detection, activity analysis, SOS red button, location information, and reminders The proposed system can work indoors as well as outdoors, and is completely independent, ie, it requires only a SIM card to function and does not depend on base stations, internet connection, Bluetooth and similar The evaluation of the fall detection algorithm showed that it detects all of the fast falls and minimizes the false positives, achieving 85% accuracy

e-Turist: An Intelligent Personalised Trip Guide

Abstract: We present e-Turist, an intelligent system that helps tourists plan a personalised itinerary to a tourist area, taking into account individual’s preferences and limitations. After creating the route, e -Turist also offers real-time GPS guidance and audio description of points of interest visited. Here we focus on two main components, the recommender system and the route planning algorithm. We also present some use cases to highlight e-Turist functionalities in different configurations.