This paper aims to design and implement a system capable of distinguishing between different activities carried out during a tennis match. The goal is to achieve the correct classification of a set of tennis strokes. The system must exhibit robustness to the variability of the height, age or sex of any subject that performs the actions. A new database is developed to meet this objective. The system is based on two sensor nodes using Bluetooth Low Energy (BLE) wireless technology to communicate with a PC that acts as a central device to collect the information received by the sensors. The data provided by these sensors are processed to calculate their spectrograms. Through the application of innovative deep learning techniques with semi-supervised training, it is possible to carry out the extraction of characteristics and the classification of activities. Preliminary results obtained with a data set of eight players, four women and four men have shown that our approach is able to address the problem of the diversity of human constitutions, weight and sex of different players, providing accuracy greater than 96.5% to recognize the tennis strokes of a new player never seen before by the system.

In this paper we present an integrated system for face detection, tracking and recognition in complex scenes. The face detector is based on colour skin models, with adaptation to cope with non-stationary colour distributions over time. The face model is tracked along the sequence with a particle filter by comparing its colour histogram with the colour histogram of the sample position by means of the Bhattacharyya coefficient. Face identification is based on statistical deformable models, as Active Shape Models (ASM) and Active Appearance Models (AAM) for feature extraction and a multiclass Support Vector Machine as classifier. We have tested both models with a database of 100 faces verifying the best performance of the AAM model compared with the ASM model.