Research Papers

A Wearable Device to Detect in Real-Time Bimanual Gestures of Basketball Players During Training Sessions

[+] Author and Article Information
Marco Mangiarotti

School of Industrial and
Information Engineering,
Politecnico di Milano,
Milano 20133, Italy
e-mail: marco1.mangiarotti@mail.polimi.it

Francesco Ferrise

Politecnico di Milano,
Department of Mechanical Engineering,
Via La Masa 1,
Milano 20156, Italy
e-mail: francesco.ferrise@polimi.it

Serena Graziosi

Politecnico di Milano,
Department of Mechanical Engineering,
Via La Masa 1,
Milano 20156, Italy
e-mail: serena.graziosi@polimi.it

Francesco Tamburrino

Politecnico di Milano,
Department of Mechanical Engineering,
Via La Masa 1,
Milano 20156, Italy
e-mail: francesco.tamburrino@polimi.it

Monica Bordegoni

Politecnico di Milano,
Department of Mechanical Engineering,
Via La Masa 1,
Milano 20156, Italy
e-mail: monica.bordegoni@polimi.it

1Corresponding author.

Manuscript received February 15, 2018; final manuscript received October 1, 2018; published online November 19, 2018. Assoc. Editor: Rahul Rai.

J. Comput. Inf. Sci. Eng 19(1), 011004 (Nov 19, 2018) (10 pages) Paper No: JCISE-18-1041; doi: 10.1115/1.4041704 History: Received February 15, 2018; Revised October 01, 2018

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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Fig. 1

Chest pass sequence: the ball is kept with two hands in front of the player's chest (a) and pushed forward with two hands (b) and (c)

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Fig. 2

Overhead pass sequence: the ball is brought with two hands above the player's head (a) and pushed forward to perform the pass (b) and (c)

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Fig. 3

Example of shots attempts

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Fig. 4

Sensors position and orientation

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Fig. 5

Schematic of the connections among the HC-06 Bluetooth module (left), the TinyDuino stack (center) and the GY-521 board (right). The universal serial bus TinyShield, the TinyShield proto board, the TinyDuino processor and the lithium-ion polymer battery are all embedded in the TinyDuino stack. Image created using Fritzing.

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Fig. 6

A complete unit comprising the TinyDuino stack (i.e., TinyDuino processor board + USB TinyShield + TinyShield proto board + lithium-ion polymer battery), the GY521 board and the Bluetooth HC-06 module

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Fig. 7

Complete unit on the player's wrist

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Fig. 8

The graphical user interface of the matlab program created for training the system. Once the program is connected to the devices of the player, the four classes of movements (i.e., passing, shooting, dribbling, and null, see Sec. 4.2) can be acquired, each one repeated at least twelve times.

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Fig. 9

Shot acquisition phase during the training. At the end of the training, the player will have his model.

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Fig. 10

Flowchart of the recognition algorithm performed on the data related to each 1 s window

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Fig. 11

The testing of the system performance performed in a public basketball court



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