The paper describes the design of a wearable and wireless system that allows the real-time identification of some gestures performed by basketball players. This system is specifically designed as a support for coaches to track the activity of two or more players simultaneously. Each wearable device is composed of two separate units, positioned on the wrists of the user, connected to a personal computer (PC) via Bluetooth. Each unit comprises a triaxial accelerometer and gyroscope, a microcontroller, installed on a TinyDuino platform, and a battery. The concept of activity recognition chain is investigated and used as a reference for the gesture recognition process. A sliding window allows the system to extract relevant features from the incoming data streams: mean values, standard deviations, maximum values, minimum values, energy, and correlations between homologous axes are calculated to identify and differentiate the performed actions. Machine learning algorithms are implemented to handle the recognition phase.

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