Machine Learning: Gesture classification

This code example demonstrates how to perform gesture classification based on motion sensor (accelerometer and gyroscope) data. The code example comes with a pre-trained model that classifies the following gestures: circle, square, and side-to-side.

For more details, see the ModusToolbox™ Machine Learning solution.

View this README on GitHub.

Provide feedback on this code example.

Disclaimer: The model provided is an example and may need customization for generalization or to meet certain performance criteria. If you require large-scale production, contact your sales representative.

Requirements

ModusToolbox™ v3.1 or later (tested with v3.2)
ModusToolbox™ Machine Learning Pack v2.0 or later (tested with v2.0)
Board support package (BSP) minimum required version: 4.0.0
Programming language: C
Other tools: Python 3.8.10 or later
Associated parts: All PSoC™ 6 MCU parts

Supported toolchains (make variable 'TOOLCHAIN')

GNU Arm® Embedded Compiler v11.3.1 (GCC_ARM) – Default value of TOOLCHAIN
Arm® Compiler v6.16 (ARM)
IAR C/C++ Compiler v9.30.1 (IAR)

Note: tflm is not supported with IAR

Supported kits (make variable 'TARGET')

PSoC™ 62S2 Wi-Fi Bluetooth® Pioneer Kit (CY8CKIT-062S2-43012) - Default value of TARGET
PSoC™ 64 "Secure Boot" Wi-Fi Bluetooth® Pioneer Kit (CY8CKIT-064B0S2-4343W)

Hardware setup

Connect the CY8CKIT-028-TFT shield to the baseboard header compatible with Arduino.

The code example also works with the CY8CKIT-028-SENSE shield. See the Operation section for more information.

Software setup

Install a terminal emulator if you don't have one. Instructions in this document use Tera Term.

Install the Machine Learning pack using this link.

Install the Python interpreter and add it to the top of the system path in environmental variables. This code example is tested with Python v3.8.10.

Use the ModusToolbox™-ML Configurator tool (from {ModusToolbox™ install directory}/packs/ModusToolbox-Machine-Learning-Pack/tools/ml-configurator/) to load a pre-trained NN model and generate C files to be used with this code example. Alternatively, you can launch the configurator tool in Eclipse IDE for ModusToolbox™ from the Quick Launch window. For more information, see the ModusToolbox™ Machine Learning user guide.

By default, the Makefile uses a model that comes with the code example. The pre-trained neural net (NN) model is located in the pretrained_models folder. The output files location is set to mtb_ml_gen; the project name is set to magic_wand. You can use the ModusToolbox™-ML Configurator tool to open the design.mtbml model to evaluate the model.

By default, the output files location is set to mtb_ml_gen. The project name is set to MAGIC_WAND. If you change any of these default settings, edit the following Makefile parameters of this code example:

Makefile parameter	Description
`NN_TYPE=`	Defines the input data format and NN weights. It can be `float`, `int16x16`, `int16x8`, or `int8x8`.
`NN_MODEL_NAME=`	Defines the name of the model. The name comes from the project name defined in the ML Configurator tool. No quotes are used when changing the name of the model.
`NN_MODEL_FOLDER=`	Sets the name where the model files will be placed. The name comes from the output file location defined in the ModusToolbox™-ML Configurator tool.
`NN_INFERENCE_ENGINE`	Defines the inference engine to run. It has three options: `tflm`, `tflm_less`, and `ifx`

For information on available inference engines, see the ModusToolbox™ Machine Learning user guide.

Note: The tflm and tflm_less inference engines only support float and int8x8.

Using the code example

Create the project

The ModusToolbox™ tools package provides the Project Creator as both a GUI tool and a command line tool.

Use Project Creator GUI

Open the Project Creator GUI tool.

There are several ways to do this, including launching it from the dashboard or from inside the Eclipse IDE. For more details, see the Project Creator user guide (locally available at {ModusToolbox™ install directory}/tools_{version}/project-creator/docs/project-creator.pdf).
On the Choose Board Support Package (BSP) page, select a kit supported by this code example. See Supported kits.

Note: To use this code example for a kit not listed here, you may need to update the source files. If the kit does not have the required resources, the application may not work.
On the Select Application page:

a. Select the Applications(s) Root Path and the Target IDE.

Note: Depending on how you open the Project Creator tool, these fields may be pre-selected for you.

b. Select this code example from the list by enabling its check box.

Note: You can narrow the list of displayed examples by typing in the filter box.

c. (Optional) Change the suggested New Application Name and New BSP Name.

d. Click Create to complete the application creation process.

Use Project Creator CLI

The 'project-creator-cli' tool can be used to create applications from a CLI terminal or from within batch files or shell scripts. This tool is available in the {ModusToolbox™ install directory}/tools_{version}/project-creator/ directory.

Use a CLI terminal to invoke the 'project-creator-cli' tool. On Windows, use the command-line 'modus-shell' program provided in the ModusToolbox™ installation instead of a standard Windows command-line application. This shell provides access to all ModusToolbox™ tools. You can access it by typing "modus-shell" in the search box in the Windows menu. In Linux and macOS, you can use any terminal application.

The following example clones the "Gesture Classification" application with the desired name "GestureClassification" configured for the CY8CKIT-062S2-43012 BSP into the specified working directory, C:/mtb_projects:

project-creator-cli --board-id CY8CKIT-062S2-43012 --app-id mtb-example-ml-gesture-classification --user-app-name GestureClassification --target-dir "C:/mtb_projects"

The 'project-creator-cli' tool has the following arguments:

Argument	Description	Required/optional
`--board-id`	Defined in the field of the BSP manifest	Required
`--app-id`	Defined in the field of the CE manifest	Required
`--target-dir`	Specify the directory in which the application is to be created if you prefer not to use the default current working directory	Optional
`--user-app-name`	Specify the name of the application if you prefer to have a name other than the example's default name	Optional

Note: The project-creator-cli tool uses the git clone and make getlibs commands to fetch the repository and import the required libraries. For details, see the "Project creator tools" section of the ModusToolbox™ tools package user guide (locally available at {ModusToolbox™ install directory}/docs_{version}/mtb_user_guide.pdf).

Open the project

After the project has been created, you can open it in your preferred development environment.

Eclipse IDE

If you opened the Project Creator tool from the included Eclipse IDE, the project will open in Eclipse automatically.

For more details, see the Eclipse IDE for ModusToolbox™ user guide (locally available at {ModusToolbox™ install directory}/docs_{version}/mt_ide_user_guide.pdf).

Visual Studio (VS) Code

Launch VS Code manually, and then open the generated {project-name}.code-workspace file located in the project directory.

For more details, see the Visual Studio Code for ModusToolbox™ user guide (locally available at {ModusToolbox™ install directory}/docs_{version}/mt_vscode_user_guide.pdf).

Keil µVision

Double-click the generated {project-name}.cprj file to launch the Keil µVision IDE.

For more details, see the Keil µVision for ModusToolbox™ user guide (locally available at {ModusToolbox™ install directory}/docs_{version}/mt_uvision_user_guide.pdf).

IAR Embedded Workbench

Open IAR Embedded Workbench manually, and create a new project. Then select the generated {project-name}.ipcf file located in the project directory.

For more details, see the IAR Embedded Workbench for ModusToolbox™ user guide (locally available at {ModusToolbox™ install directory}/docs_{version}/mt_iar_user_guide.pdf).

Command line

If you prefer to use the CLI, open the appropriate terminal, and navigate to the project directory. On Windows, use the command-line 'modus-shell' program; on Linux and macOS, you can use any terminal application. From there, you can run various make commands.

For more details, see the ModusToolbox™ tools package user guide (locally available at {ModusToolbox™ install directory}/docs_{version}/mtb_user_guide.pdf).

Operation

If using a PSoC™ 64 "Secure" MCU kit (like CY8CKIT-064B0S2-4343W), the PSoC™ 64 device must be provisioned with keys and policies before being programmed. Follow the instructions in the "Secure Boot" SDK user guide to provision the device. If the kit is already provisioned, copy-paste the keys and policy folder to the application folder.

Connect the CY8CKIT-028-TFT shield to the baseboard.

If using the CY8CKIT-028-SENSE shield, change SHIELD_DATA_COLLECTION=CY_028_TFT_SHIELD in the Makefile based on the shields revision.

To check the version of CY8CKIT-028-SENSE, locate the sticker on the bottom of the shield's box which indicates the revision.
- If the shield is Rev "**", use SHIELD_DATA_COLLECTION=CY_028_SENSE_SHIELD_v1.
- If the shield is Rev "*B", use SHIELD_DATA_COLLECTION=CY_028_SENSE_SHIELD_v2.
Connect the board to your PC using the provided USB cable through the KitProg3 USB connector.
Open a terminal program and select the KitProg3 COM port. Set the serial port parameters to 8N1 and 115200 baud.
Program the board using one of the following:
Using Eclipse IDE
1. Select the application project in the Project Explorer.
2. In the Quick Panel, scroll down, and click <Application Name> Program (KitProg3_MiniProg4).
In other IDEs

Follow the instructions in your preferred IDE.
Using CLI

From the terminal, execute the make program command to build and program the application using the default toolchain to the default target. The default toolchain is specified in the application's Makefile but you can override this value manually:
```
make program TOOLCHAIN=<toolchain>
```
Example:
```
make program TOOLCHAIN=GCC_ARM
```
After programming, the application starts automatically. Confirm that "Gesture Classification Example" and some log data are printed on the UART terminal. The gesture classifications and confidence are updated continuously.
Hold the board with the orientation as shown below while moving your arm to complete a gesture:

Figure 1. Board orientation
Perform a counter-clockwise circle movement continuously. Confirm that the UART terminal prints the gesture as Circle, and the confidence of the circle shape increases past 60%. For best results, repeatedly perform circle movements each in one second, with a diameter of one foot.

Figure 2. Circle gesture
Perform a counter-clockwise square movement continuously. Confirm that the UART terminal prints the gesture as Square, and the confidence of the square shape increases past 60%. For best results, repeatedly perform square shape movements, each in two seconds, with a side length of one foot.

Figure 3. Square gesture
Perform a side-to-side movement (left <-> right) continuously. Confirm that the UART terminal prints the gesture as Side-to-side, and the confidence increases past 60%. For best results, repeatedly perform the one-foot-wide left-to-right movement in half-a-second each.

Figure 4. Side-to-side gesture
When not performing any of these gestures, confirm that the UART terminal prints the gesture as None.

Note: If the confidence is lower than 60%, the gesture will register as None.

Note: Figures 2-4 above may have low frame rates. Click on the embedded links in steps 7, 8, and 9 to redirect to GitHub for better quality.

Debugging

You can debug the example to step through the code.

In Eclipse IDE

Use the <Application Name> Debug (KitProg3_MiniProg4) configuration in the Quick Panel. For details, see the "Program and debug" section in the Eclipse IDE for ModusToolbox™ user guide.

Note: (Only while debugging) On the CM4 CPU, some code in main() may execute before the debugger halts at the beginning of main(). This means that some code executes twice – once before the debugger stops execution, and again after the debugger resets the program counter to the beginning of main(). See KBA231071 to learn about this and for the workaround.

In other IDEs

Follow the instructions in your preferred IDE.

Design and implementation

In this example, the firmware reads the data from a motion sensor (BMX160) to detect gestures.

The data consists of a 3-axis orientation data from the accelerometer and the gyroscope. A timer is configured to interrupt at 128 Hz. The interrupt handler reads all 6 axes through SPI and signals a task to process the batch of samples when the internal FIFO has 128 new samples. It performs an IIR filter and a min-max normalization on 128 samples at a time. This processed data is then fed to the inference engine. The inference engine outputs the confidence of the gesture for each of the four gesture classes. If the confidence passes a certain percentage, the gesture is printed to the UART terminal.

The code example also provides a ModusToolbox™-ML Configurator tool project file - design.mtbml, which points to the pre-trained NN model available in the pretrained_models folder.

This application uses FreeRTOS with gesture task. The gesture task pre-processes all data and passes the data to the inference engine. FreeRTOS is used so that the code example can be expanded.

Figure 5. Block diagram

Gesture classification model

The convolutional neural network (CNN) model consists of two convolutional blocks and two full-connection layers. Each convolutional block includes convolutional operations, including rectified linear unit (ReLU) and max pooling, with the addition of a batch normalization layer after the first block.

The convolutional layers act as feature extractors and provide abstract representations of the input sensor data in feature maps. They capture short-term dependencies (spatial relationships) of the data. In CNN, features are extracted and then used as inputs to a fully connected network, using softmax activation for classification.

Figure 6. Model diagram

Model generation

This code example includes the scripts used to generate the "Magic_wand_model.h5" model. These scripts allow the user to collect data, train a model, and deploy the model to a PSoC™ device. These scripts are used as a starting place and show the flow you must go through to produce a model.

Collect data

The user collects the data to train a model. The gesture classification has code included to print the IMU data out to a terminal. That data is then stored in a text file with the use of a script.

Do the following to collect the data:

Open gesture.h in the project and change GESTURE_DATA_COLLECTION_MODE 0u to GESTURE_DATA_COLLECTION_MODE 1u.
Remove all files in your project's train/gesture_data folder. This is the data used to train the current model.
Program the device using the steps from the Operation section. Do not open a terminal window.
Open modus-shell and navigate to the train directory in the current project.
Install the requirements with the following command:
```
pip install -r requirements.txt
```
Run the following command:
```
./collect.sh <COM PORT> <GESTURE NAME> <PERSON NAME>
```
The port the device is connected to should be the COM port. The gesture name should be limited to 16 characters. The model is set up for a repeatable gesture such as a circle or square.
Perform the named gesture and press C to start capturing data. Continuously repeat the gesture until a few minutes of data is collected.
Press S to stop the data capture.
Repeat steps 5 through 7 for each gesture. Scripts support four distinct gestures; one of those gestures should be a "negative" or "no_gesture" classification. All data is stored in TXT files in your project's train/gesture_data folder.

Generate a model

After the data collection, generate a model using that data.

Do the following to generate a model:

Open modus-shell and navigate to the train directory in the current project.
Install the requirements with following command if not previously done in the Collect Data section:
```
pip install -r requirements.txt
```
Run the following command to run the different scripts that process the data, train the model, and validate the model:
```
./generate_model.sh
```
The following are generated:
- Generated model called User_generated_model.h5/tflite is stored in pretrained_models.
- Pruned model called User_generated_model_pruned.h5 is generated with 50 percent pruning and allows for the use of sparsity in the ML Configurator.
- gesture_names.h is generated in your project's train folder that stores the names of the user-defined gestures. This is used when printing gesture info out to a terminal.
- calibration.npz file is generated in your project's train folder that stores the data used for model calibration or validation.

Model deployment

Do the following to deploy the generated model to the device.

Open the gesture.h file and change GESTURE_DATA_COLLECTION_MODE 1u to GESTURE_DATA_COLLECTION_MODE 0u.
Open ML Configurator from the Quick Panel.
For the pretrained model parameter, import the newly generated model from pretrained_models/User_generated_model.h5/tflite.
If the User_generated_model_pruned.h5 model is used, select Enable Sparsity in ML Configurator.
If the selected inference engine is TFLM and Enable quantization is selected, under Model Calibration, set the Dataset structure to NPZ and the Path to train/calibration.npz. The calibration.npz data can also be used for model validation.
Click Generate Source.
Program the device using the steps from the Operation section.
Perform the trained gestures and validate the output in a terminal window.

Note: This code example uses the same flow as described in this section to function. When you run the ./generate_model.sh command, the code example will not operate as intended. The data used to train the current model is stored in the gesture_data file and can be used to train a model.

Files and folders

|-- mtb_ml_gen/         	# Contains the model files
|-- pretrained_models/  	# Contains the H5 format model (used by the ML Configurator tool)
|-- source              	# Contains the source code files for this example
   |- gesture.c/h       	# Implements the gesture task
   |- processing.c/h    	# Implements the IIR filter and normalization functions
   |- control.c/h       	# Implements the control task
   |- sensor.c/h			# Sets up the IMU and collects data
|-- fifo                	# Contains a FIFO library
   |- cy_fifo.c/h       	# Implements a FIFO in firmware
|-- FreeRTOSConfig.h    	# FreeRTOS configuration file
|-- design.mtbml        	# ModusToolbox-ML Configurator tool project file
|--train					# Scripts for training and generating a model
   |- collect.sh			# Contains the script to collect user data and store it in a TXT file
   |- generate_model.sh		# Contains the scripts to generate a model based on the user data
   |- calibration_data.npz	# Contains data that can be used for calibration or validation of a model
   |- gesture_names.h		# Contains the names of the gestures, generated when generate_model.sh is used

Note: This code example supports CY8CKIT-028-TFT and the CY8CKIT-028-SENSE shields. These shields have different sensors; to support both, a change is made to the bmi160_defs.h file. When using CY8CKIT-028-TFT, BMI160_CHIP_ID is set to 0xD1. When using CY8CKIT-028-SENSE, BMI160_CHIP_ID is set to 0xD8. This is done through a series of PREBUILD commands in the Makefile.

Resources and settings

Table 1. Application resources

Resource	Alias/object	Purpose
UART (HAL)	cy_retarget_io_uart_obj	UART HAL object used by Retarget-IO for Debug UART port
SPI (HAL)	spi	SPI HAL object to interface with the BMX160 sensor
Timer (HAL)	sensor_timer	Timer HAL object to periodically trigger samples to the sensor

Related resources

Resources	Links
Application notes	AN228571 – Getting started with PSoC™ 6 MCU on ModusToolbox™ AN215656 – PSoC™ 6 MCU: Dual-CPU system design
Code examples	Using ModusToolbox™ on GitHub
Device documentation	PSoC™ 6 MCU datasheets [PSoC™ 6 technical reference manuals](https://documentation.infineon.com/html/psoc6/zrs1651212645947.html
Development kits	Select your kits from the Evaluation board finder.
Libraries on GitHub	mtb-pdl-cat1 – PSoC™ 6 Peripheral Driver Library (PDL) mtb-hal-cat1 – Hardware Abstraction Layer (HAL) library retarget-io – Utility library to retarget STDIO messages to a UART port
Middleware on GitHub	capsense – CAPSENSE™ library and documents psoc6-middleware – Links to all PSoC™ 6 MCU middleware
Tools	ModusToolbox™ – ModusToolbox™ software is a collection of easy-to-use libraries and tools enabling rapid development with Infineon MCUs for applications ranging from wireless and cloud-connected systems, edge AI/ML, embedded sense and control, to wired USB connectivity using PSoC™ Industrial/IoT MCUs, AIROC™ Wi-Fi and Bluetooth® connectivity devices, XMC™ Industrial MCUs, and EZ-USB™/EZ-PD™ wired connectivity controllers. ModusToolbox™ incorporates a comprehensive set of BSPs, HAL, libraries, configuration tools, and provides support for industry-standard IDEs to fast-track your embedded application development.

Other resources

Infineon provides a wealth of data at www.infineon.com to help you select the right device, and quickly and effectively integrate it into your design.

For PSoC™ 6 MCU devices, see How to design with PSoC™ 6 MCU - KBA223067 in the Infineon Developer Community.

Document history

Document title: CE233112 – Machine Learning: Gesture classification

Version	Description of change
1.0.0	New code example
1.1.0	Updated model, new CY_028_TFT support, better gesture description
2.0.0	Update includes support for MTBML 1.2 and support for machine learning middleware
3.0.0	Update to ModusToolbox™ 3.0 and Machine Learning 2.0
3.1.0	Update to support model pruning and sparsity Update to use the new mtb_ml_utils_quantize() function.
3.2.0	Update to use BMX160 script for changing chip ID. Add protobuf to requirements.txt and Updated to support ModusToolbox™ 3.2.

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mtb-example-ml-gesture-classification/README.md