The Magic Wand Example

Introduction

• The ADXL345 is a tree axis digital accelerometer by Analog Devices
  I wrote a pure MircoPython driver for the ADXL345 available at https://github.com/uraich/IoT-Course/tree/master/exercises/solutions/exercise_16/adxl345.
  
  
• The MPU6050 is a six axis device (Gyro and Accelerometer) from Ivensense. It is described by its datasheet and the register description document.
  Jeff Rowberg has written a very complete MPU6050 driver for the Arduino in C++, which includes access to its Digital Motion Processor, whose description cannot be found anywhere on the WEB. I ported this driver to MicroPython together with a few example programs drafted after Jeff's C++ examples: https://github.com/uraich/IoT-Course/tree/master/exercises/solutions/exercise_16/mpu6050.
  
  .
  
  

• 0,0,1g for the measured acceleration in x, y and z, z pointing in the direction of gravity.
• and 0,0,0 for the gyroscope

The Magic Wand Example

Introduction

• The ADXL345 is a tree axis digital accelerometer by Analog Devices
  I wrote a pure MircoPython driver for the ADXL345 available at https://github.com/uraich/IoT-Course/tree/master/exercises/solutions/exercise_16/adxl345.
  
  
• The MPU6050 is a six axis device (Gyro and Accelerometer) from Ivensense. It is described by its datasheet and the register description document.
  Jeff Rowberg has written a very complete MPU6050 driver for the Arduino in C++, which includes access to its Digital Motion Processor, whose description cannot be found anywhere on the WEB. I ported this driver to MicroPython together with a few example programs drafted after Jeff's C++ examples: https://github.com/uraich/IoT-Course/tree/master/exercises/solutions/exercise_16/mpu6050.
  
  .
  
  
• The MPU9250 is very similar to he MPU6050 but has a magnetometer in addition. Here is its data sheet and the register map. The MPU92/66 has the necessary pins to be accessed either through I2C or SPI.
  

• The LIS2DH MEMS digital output motion sensor from ST Microelectronics is an ultra low power high performance 3 axis accelerometer.

Hardware

The Magic Wand Example

Introduction

• The ADXL345 is a tree axis digital accelerometer by Analog Devices
  I wrote a pure MircoPython driver for the ADXL345 available at https://github.com/uraich/IoT-Course/tree/master/exercises/solutions/exercise_16/adxl345.
  
  
• The MPU6050 is a six axis device (Gyro and Accelerometer) from Ivensense. It is described by its datasheet and the register description document.
  Jeff Rowberg has written a very complete MPU6050 driver for the Arduino in C++, which includes access to its Digital Motion Processor, whose description cannot be found anywhere on the WEB. I ported this driver to MicroPython together with a few example programs drafted after Jeff's C++ examples: https://github.com/uraich/IoT-Course/tree/master/exercises/solutions/exercise_16/mpu6050.
  
  .
  
  

• The LIS2DH MEMS digital output motion sensor from ST Microelectronics is an ultra low power high performance 3 axis accelerometer.

Hardware

The Magic Wand Example

Introduction

The magic wand example shows how to detect gestures using an accelerometer. Different types of accelerometers are easily available:

• The ADXL345 is a tree axis digital accelerometer by Analog Devices
  I wrote a pure MircoPython driver for the ADXL345 available at https://github.com/uraich/IoT-Course/tree/master/exercises/solutions/exercise_16/adxl345.
  
  

• The LIS2DH MEMS digital output motion sensor from ST Microelectronics is an ultra low power high performance 3 axis accelerometer.

Hardware

The Magic Wand Example

Introduction

Providing a training data set

The Magic Wand Example

Introduction

• The ADXL345 is a tree axis digital accelerometer by Analog Devices
  I wrote a pure MircoPython driver for the ADXL345 available at https://github.com/uraich/IoT-Course/tree/master/exercises/solutions/exercise_16/adxl345.
  
  
• The MPU6050 is a six axis device (Gyro and Accelerometer) from Ivensense. It is described by its datasheet and the register description document.
  Jeff Rowberg has written a very complete MPU6050 driver for the Arduino in C++, which includes access to its Digital Motion Processor, whose description cannot be found anywhere on the WEB. I ported this driver to MicroPython together with a few example programs drafted after Jeff's C++ examples: https://github.com/uraich/IoT-Course/tree/master/exercises/solutions/exercise_16/mpu6050.
  
  .

Providing a training data set

In order to provide a training data set we must be able to record gestures. We must therefore provide a program that recognizes the start and the end of a gesture (movement and inactivity detection) and which records the accelerometer data of the gesture onto a file. Gesture detection is of course also needed to be able to feed the accelerometer data into the trained model, which will then recognize the gesture.

The Magic Wand Example

Introduction

The magic wand example shows how to detect gestures using an accelerometer. Different types of accelerometers are easily available:

• The LIS2dH MEMS digital output motion sensor from ST Microelectronics is an ultra low power high performance 3 axis accelerometer.

Providing a training data set

The Magic Wand Example

Introduction

• The ADXL345 is a tree axis digital accelerometer by Analog Devices
  
• The MPU6050 is a six axis device (Gyro and Accelerometer) from Ivensense. It is described by it datasheet and the register description.
  
• The LIS2dH MEMS digital output motion sensor from ST Microelectronics is an ultra low power high performance 3 axis accelerometer.

Hardware connections

The MPU6050 is connected to the WeMos D1 bus as follows:

Providing a training data set

In order to provide a training data set we must be able to record gestures. Therefore a driver for the accelerometer is needed as well as a program that recognizes movement and writes the accelerometer data on a file.

The Magic Wand Example

Introduction

• The ADXL345 is a tree axis digital accelerometer by Analog Devices
  
• The MPU6050 is a six axis device (Gyro and Accelerometer) from Ivensense. It is described by it datasheet and the register description.
  
• The LIS2dH MEMS digital output motion sensor from ST Microelectronics is an ultra low power high performance 3 axis accelerometer.

Providing a training data set

In order to provide a training data set we must be able to record gestures. Therefore a driver for the accelerometer is needed as well as a program that recognizes movement and writes the accelerometer data on a file.

The Magic Wand Example

Introduction

The magic wand example shows how to detect gestures using an accelerometer. Different types of accelerometers are easily available:

Uli Raich - 2022-02-02

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The Magic Wand Example

Introduction

Uli Raich - 2022-02-02

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