Access to performance data during matches and training sessions is important for coaches and players. Although there are many video tagging systems available which can provide such access, these systems require manual effort. Data from Inertial Measurement Units (IMU) could be used for automatically tagging video recordings in terms of players’ actions. However, the data gathered during volleyball sessions are generally very imbalanced, since for an individual player most time intervals can be classified as “non-actions” rather than “actions”. This makes automatic annotation of video recordings of volleyball matches a challenging machine-learning problem. To address this problem, we evaluated balanced and imbalanced learning methods with our newly proposed ‘super-bagging’ method for volleyball action modelling. All methods are evaluated using six classifiers and four sensors (i.e., accelerometer, magnetometer, gyroscope and barometer). We demonstrate that imbalanced learning provides better unweighted average recall, (UAR = 83.99%) for the non-dominant hand using a naive Bayes classifier than balanced learning, while balanced learning provides better performance (UAR = 84.18%) for the dominant hand using a tree bagger classifier than imbalanced learning. Our super-bagging method provides the best UAR (84.19%). It is also noted that the super-bagging method provides better averaged UAR than balanced and imbalanced methods in 8 out of 10 cases, hence demonstrating the potential of the super-bagging method for IMU’s sensor data. One of the potential applications of these novel models is fatigue and stamina estimation e.g., by keeping track of how many actions a player is performing and when these are being performed.

Nowadays, a variety ofhandheld mobile devices are capable of connecting to the Internet using multiple network interfaces. This is referred to as multi-homing. In addition to this, enriched computation resources allow them to host nomadic mobile services and provide these services to the clients located anywhere in the Internet. Potential advantages of multi-homing for nomadic mobile services typically includes: an increased service availability and improved service performance. However, applications running on the handheld mobile devices either do not, or cannot, exploit multi-homing. In this paper we address the problem ofproviding infrastructural support to the nomadic mobile services that can fully exploit multi-homing. To this end we propose to incorporate multi-homing functionality and support in a middleware layer to reduce the complexity of the design and maintenance of these services. The proposed solution uses a context-aware computing approach to realize this functionality. We report the initial experimental results in the remote telemonitoring of a patient equipped with a Body Area Network.

Abstract

Abstract-This paper addresses how Quality of Context (QoC) can be used to optimize end-to-end mobile healthcare (m-health) data delivery services in the presence of alternative delivery paths, which is quite common in a pervasive computing and communication environment. We propose min-max-plus based algebraic QoC models for computing the quality of delivered data impeded by the QoS of the resources along the alternative delivery paths. The constructed algebraic structures in those models directly relate to the resource configurations represented as directed graphs. The properties of the applied algebras correspond to the properties of the operations of the addressed QoS dimensions. To rank all the possible resource configurations and therewith select from those the most optimal one(s) we introduce a workflow management metric based on the quality dimensions like freshness and availability. We focus on the preestablishment phase of m-health data delivery services; dynamic QoC issues existing during service execution are not considered.Index Terms-algebraic computational models, mobile healthcare, QoC, QoS, service composition.

Physical inactivity is increasingly becoming part of today’s lifestyle, leading to a rapid increase in the incidence of diseases including cardiovascular disease, diabetes, and obesity. These chronic diseases are, for the most part, preventable by adopting a healthy lifestyle including regular physical activity. To help people maintain appropriate physical activity levels, researchers are developing interventions based on concepts from social science and ICT solutions. In this line, we investigate virtual communities (or social networks) as a candidate solution to support people in achieving their daily physical activity goals. This study observes and explores the differences between using the virtual community and a physical activity monitoring system on the physical activity level. We designed an exploratory study with a duration of 9 weeks in which an intervention group used a virtual community with a physical activity monitoring system and a control group used only a physical activity monitoring system. The results of this exploratory study demonstrate that using virtual communities may motivate and support people in their daily physical activity; in particular, we observed a decrease in the use of the system later than was observed in previous studies. Future investigations are needed to confirm the effect of the virtual community on physical activity.