Hand Gesture Controller With Bluetooth

by The Tactigon in Circuits > Wearables

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Hand Gesture Controller With Bluetooth

Gesture Recognize Sample App

In this video we will show how to build a simple gesture controller with 9 DoF IMU (3D accelerometer, 3D gyroscope, 3D magnetometer) and Bluetooth Low Energy 4.0.

In this project you can learn and practice :

a) The wireless communication through Bluetooth

b) how to use quaternion with IMU sensors

c) how to use Processing for display and capture the hand gesture

Bluetooth and IMU With the Tactigon Board - Arduino

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board product.png

Source code available here on GitHub In this article we’re going to learn how to use simple Tactigon’s functions to send accelerometer data and quaternions over Bluetooth Low Energy (BLE from now on).

We’ll look at, in details, to:

a) Used Libraries

b) UUID and Characteristic

c) Buffer

Equipment

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1. The Tactigon Board (www.thetactigon.com)

2. T-SKIN is a handband accessory for gesture control and motion capture applications.

3- Smartphone Android

Programming Arduino

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Upload the codes to Arduino used for glove.

https://github.com/TheTactigon/Gesture-Controller

Arduino Used Library

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This project we used few essential libraries:

  1. tactigon_led.h
  2. tactigon_imu.h
  3. IMU_main.h
  4. tactigon_BLE.h

First three libraries, have been analyzed on first part of this series, we now discover tactigon_BLE.h library. tactigon_BLE.h This library is dedicated to integrated BLE module. It provides few types needed to create a communication between two devices.

T_BLE is BLE Manager (bleManager instance in this project), it handles UUID, Characteristics and their buffer. UUID is the universally unique identification, which, associated with a characteristic, allows to identify it by external devices.

T_BLE_Characteristic is the characteristic class. It needs an UUID and length in bytes of the payload it will transmit.

Conclusion

In this project i showed how to build gesture recognition and interface with Android. The Arduino code is able to recognize pattern : Tap, Horizontal and Vertical Swipe.

The next step is associate to this pattern robotic rover movement for example.