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In the paper a human activity recognition system has been presented based on the data gathered with the smartphone sensors. The acceleration, magnetic field and sound have been registered and four different activities of daily living has been recognized i.e. riding a bike, driving in a car, walking and sitting. Two version of Support Vector Machine (SVM) classifier have been employed and the obtained results are promising. Keywords Human activity recognition · Smartphones · Support vector machineThe Human Activity Recognition (HAR) systems aim to automatically determine what people do on the basis of signals recorded from different sensors. These systems can be divided into two main types: external and wearable [11].In the first approach the sensors are placed in a fixed position so the people have to interact with the system and they are confined to a certain area. The most popular are video cameras [20]. Typically they can register a 2D images but it can also record a 3D sequences when two or more devices are employed [1,17]. In order to track the human activity also the depths sensors (especially Time-of-Flight cameras) are introduced. The solutions are based on tracking both the whole body [7,14] as well as only it certain parts, in particular joints [19]. Although this approach allows obtaining a full information about the human activity and his/her environment. The image processing methods are very time consuming and not resource-efficient. Moreover, many people are not willing to be monitored permanently, except for the

Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%.

The paper presents a system that recognizes the make, colour and type of the vehicle. The classification has been performed using low quality data from real-traffic measurement devices. For detecting vehicles’ specific features three methods have been developed. They employ several image and signal recognition techniques, e.g. Mamdani Fuzzy Inference System for colour recognition or Scale Invariant Features Transform for make identification. The obtained results are very promising, especially because only on-site equipment, not dedicated for such application, has been employed. In case of car type, the proposed system has better performance than commonly used inductive loops. Extensive information about the vehicle can be used in many fields of Intelligent Transport Systems, especially for traffic supervision.

Summary.The Bluetooth standard is a low-cost, very popular communication protocol offering a wide range of applications in many fields. In this paper, a novel system for road traffic estimation using Bluetooth sensors has been presented. The system consists of three main modules: filtration, statistical analysis of historical, and traffic estimation and prediction. The filtration module is responsible for the classification of road users and detecting measurements that should be removed. Traffic estimation has been performed on the basis of the data collected by Bluetooth measuring devices and information on external conditions (e.g., temperature), all of which have been gathered in the city of Bielsko-Biala (Poland). The obtained results are very promising. The smallest average relative error between the number of cars estimated by the model and the actual traffic was less than 10%.