Attila Reiss

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This paper addresses the lack of a commonly used, standard dataset and established benchmarking problems for physical activity monitoring. A new dataset — recorded from 18 activities performed by 9 subjects, wearing 3 IMUs and a HR-monitor — is created and made publicly available. Moreover, 4 classification problems are benchmarked on the dataset, using a(More)
Physical activity monitoring has recently become an important field in wearable computing research. However, there is a lack of a commonly used, standard dataset and established benchmarking problems. In this work, a new dataset for physical activity monitoring --- recorded from 9 subjects, wearing 3 inertial measurement units and a heart rate monitor, and(More)
In this paper, the idea of a modular activity monitoring system is introduced. By using different combinations of the system's three modules, different functionality becomes available: 1) a coarse intensity estimation of physical activities 2) different features based on HR-data and 3) the recognition of basic activities and postures.(More)
With recent progress in wearable sensing it becomes reasonable for individuals to wear different sensors all day, thus global activity monitoring is establishing. The goals in global activity monitoring systems are amongst others to tell the type of activity that was performed, the duration and the intensity. With the information obtained this way, the(More)
This paper describes a competitive approach developed for an activity recognition challenge. The competition was defined on a new and publicly available dataset of human activities, recorded with smartphone sensors. This work investigates different feature sets for the activity recognition task of the competition. Moreover, the focus is also on the(More)
We present a novel sensor system for the support of nutrition monitoring. The system is based on smart table cloth equipped with a fine grained pressure textile matrix and a weight sensitive tablet. Unlike many other nutrition monitoring approaches, our system is unobtrusive, non privacy invasive and easily deployable in every day life. It allows the(More)
This paper addresses one of the main challenges in physical activity monitoring, as indicated by recent benchmark results: The difficulty of the complex classification problems exceeds the potential of existing classifiers. Therefore, this paper proposes the ConfAdaBoost.M1 algorithm. This algorithm is a variant of the AdaBoost.M1 that incorporates(More)
This paper presents a mobile and unobtrusive platform that enables the accurate monitoring of physical activities in daily life, and is integrated into a healthcare system supporting out-of-hospital services. The main focus of the paper is to describe and evaluate a complete data processing chain for recognizing different activities, and estimating their(More)