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This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation.

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Locating points of interest (POIs) in cities is typically facilitated by visual aids such as paper maps, brochures, and mobile applications. However, these techniques require visual attention, which ideally should be on the surroundings. Non-visual techniques for navigating towards specific POIs typically lack support for free exploration of the city or more detailed guidance. To overcome these issues, we propose a multimodal, wearable system for alerting the user of nearby recommended POIs. The system, built around a tactile glove, provides audio-tactile cues when a new POI is in the vicinity, and more detailed information and guidance if the user expresses interest in this POI. We evaluated the system in a field study, comparing it to a visual baseline application. The encouraging results show that the glovebased system helps keep the attention on the surroundings and that its performance is on the same level as that of the baseline.

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