This paper reviews the works found in the literature in the field of Transportation Mode Detection (TMD) which is a subfield of Activity Recognition aiming at indentifying (i.e. classifying) the mean of transportation a person is using. The solutions found in literature have different characteristics according to the device for which the solution was tailored (smartphones or other systems such as, e.g., GPS loggers) and to the algorithm used for the classification task. This may vary a lot according to the number and type of input used (e.g. accelerations, GPS, maps information or GIS-Geographical Information System information) and to the identified classes of transportation mode. These two aspects are the most relevant to consider when evaluating and comparing the accuracies claimed by each work. A comparison of the works is proposed taking into account the characteristics discussed above. In general the accelerometer is the most widely used sensor for TMD applications, as it limits battery consumption and captures relevant features for detecting motion. Indeed a key challenge in TMD is to detect different motorized classes such as bus, car, train and metro because they share common characteristics (such as e.g. the average speed and accelerations) which make hard identifying suitable features for the classification algorithm. Identifying the "walk" and "stationary" transportation modes is a simpler task because they are characterized by distinct features.

The number of telemetry parameters in a typical spacecraft is constantly increasing. At the same time the number of operators allocated to each spacecraft to check those parameters is constantly decreasing. Techniques such as limit checking are well known but they take time and effort to define, enter and manage as the mission evolves. The result is that the vast majority of telemetry parameters are not limit checked in real-time. In 2014, the Advanced Operation Concepts Office at ESA/ESOC decided to see if we could change this by employing Big Data type techniques on the data. The idea was simple, we asked our partner, SATE of Italy, to define future checks for all telemetry parameters given one year's worth of historical data. No engineering knowledge was provided and the derivation of the checks had to be completely automatic i.e. the checks had to be derived solely on the data itself with no human intervention. The mission we choose was Venus express (VEX) and the learning period ended just before the aero-braking activities started. We then applied these checks to the following three months of data which included interesting activities such as aero-braking preparation and aero-braking itself. This test data was not provided to SATE until after they had submitted their checks to us for validation. This paper describes SATE's response to this challenge. SATE decided to take a very pragmatic, engineering view of the problem and defined algorithms to search for anything that could be classed as constant in the data. This could be simple features of the data such as average or more exotic features such as harmonic mean, FFT coefficients and features

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Can self-management of emotion help on safety driving of diabetic patients? Fluctuant emotions in driving can lead to very critical situations. In particular for diabetic drivers experiencing hypoglycemic events it is inevitable to provide an intelligent alerting/recommendation system that assesses continuously driver's affective and metabolic states and predicts sudden hypoglycemic events, in order for avoiding dangerous situations during driving. In this paper we introduce an innovative approach to in-vehicle emotion monitoring system conceived in the EU project METABO. The system aims for providing the drivers a self-management opportunity to monitor/control their emotional states and apt recommendations according to detected hypoglycemic symptoms.

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