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Magic wand gesture recognition using Tensorflow and SensiML

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During the last decade, IoT devices have become very popular. Their small factor size made they optimal for all kind of applications. Their technology has also improve in the last decade and now a days they are are able to do machine learning in the edge.

I recently received a QuickFeather microcontroller from a Hackster.IO contest. One of the main features of this device is its built-in eFPGA, which can optimize parallel computations on the edge.

This post will explore the capabilities of this little beast and show how to run a machine learning model that was trained using Tensorflow. The use case will be focused for gesture recognition, so the device will be able to detect if the movement correspond to one alphabet letter.

QuickFeather

The QuickFeather is a very powerful device with a small form factor (58mm x 22mm). It’s the first FPGA-enabled microcontroller to be fully supported with Zephyr RTOS. Additionally it includes a MC3635 accelerometer, a pressure, a microphone and an integrated Li-Po battery charger.

Unlike other development kits which are based on proprietary hardware and software tools, QuickFeather is based on open source hardware and is built around 100% open source software. QuickLogic provides a nice SDK to flash some FreeRTOS software and get started. There is a bunch of documentation and examples in their github repository.

Since the QuickFeather is optimized for battery saving use cases, it doesn’t include neither Wi-Fi nor Bluetooth connectivity. Therefore, the data can be only transferred using UART serial connection.

Capture data

The on-board accelerometer is the main sensor for this use case. I use a USB-serial converter in order to read data directly from the accelerometer and transfer it to another host that is connected to the other end of the usb cable.

Data is captured and analysed using another machine. I personally connected a raspberry pi, which has also a small form factor, in order to have flexibility when performing the different gestures.

SensiML provides a web application to visualize and save data. This application is a python application that runs a flask webserver and provides nice functionalities such as capturing video at the save time in order to correlate to saved data. The code is available on github, so one can see how the code works and even propose some modifications, like I did.

I captured data from O, W and Z gestures as you can see in the following picture:


Data capture using open-gateway application

Label data with Label Studio

Once data is collected one need to label it so that one can teach a machine learning model how to associate a certain movement with a gesture. I used Label Studio, which is a open source data labelling tool. It can be used to label different kind of data such as image, audio, text, time series and a combination of all the precedent.

It can be deployed on-premise using a docker image, which is very handy if you want to go fast.

Setup

Once Label Studio stars, it has to be configured for a label task. For this case, the label task corresponds to a time series data. One can chose a graphical configuration using preconfigured templates or you can customized your self with some kind of html code. Here is the code I use to configure the data coming from X, Y and Z accelerometers.

<View>
  <!-- Control tag for labels -->
  <TimeSeriesLabels name="label" toName="ts">
    <Label value="O" background="red"/>
    <Label value="Z" background="green"/>
    <Label value="W" background="blue"/>
  </TimeSeriesLabels>
  <!-- Object tag for time series data source -->
  <TimeSeries name="ts" valueType="url" value="$timeseriesUrl" sep="," >
    <Channel column="AccelerometerX" strokeColor="#1f77b4" legend="AccelerometerX"/>
    <Channel column="AccelerometerY" strokeColor="#ff7f0e" legend="AccelerometerY"/>
    <Channel column="AccelerometerZ"  strokeColor="#111111" legend="AccelerometerZ"/>
  </TimeSeries>
</View>

Label Studio has a nice preview feature, which shows how the labelling task will look with the supplied configuration. The following screenshot shows how the interface looks like for the setup process.


Label Studio setup configuration

Labelling

One of the nicest things from Label Studio is the fact that one can go really fast using the keyboard shortcuts. It also provides some machine learning plugins which make predictions with the partial labelled data. The following screenshot shows the interface for some labelled data.


Data labelled using Label Studio

From a machine learning perspective, the exported data should be a csv file with four different columns. Even is Label Studio is able to export in csv, it didn’t have the right format for me, instead it looks like the following:

timeseriesUrl,id,label,annotator,annotation_id
/data/upload/W.csv,3,"[{""start"": 156, ""end"": 422, ""instant"": false, ""timeserieslabels"": [""W""]}, ... ]",admin@admin.com,3
/data/upload/Z.csv,2,"[{""start"": 141, ""end"": 419, ""instant"": false, ""timeserieslabels"": [""Z""]}, ...]",admin@admin.com,2
/data/upload/O.csv,1,"[{""start"": 77, ""end"": 389, ""instant"": false, ""timeserieslabels"": [""O""]}, ...]",admin@admin.com,1

So I decided to export labels in json format and then build a python script to transform and combine them all. The following script transforms three json files from Label Studio into a single file with 4 columns AccelerometerX, AccelerometerY, AccelerometerZ and Label.

import numpy as np
import pandas as pd

df_all = pd.DataFrame()
LABELS = ['W', 'Z', 'O']
sensor_columns = ['AccelerometerX','AccelerometerY', 'AccelerometerZ', 'Label']

for ind, label in enumerate(LABELS):
    df = pd.read_csv(f'{label}/{label}.csv')
    events = pd.DataFrame(pd.read_json('WOZ.json')['label'][ind])

    df['Label'] = 0
    for k,v in events.iterrows():
        for i in range(v['start'], v['end']):
            df['Label'].loc[i] = v['timeserieslabels'][0]
    df['LabelNumerical'] = pd.Categorical(df.Label)

    df[sensor_columns].to_csv(f'{label}/{label}_label.csv', index=False)
    df_all = pd.concat([df_all, df], sort=False)

df_all[sensor_columns].to_csv(f'WOZ_label.csv', index=False)

The resulting data can be directly used as a time series data and a machine learning model can be trained in order to recognise the patterns automatically. The following picture shows data for W, O and Z patterns.


Labelled data for W, O and Z gestures

Training a model using SensiML

SensiML provides a python package to build a data pipeline which can be used to train a machine learning model. One need to create a free account in order to use it. There is a lot of documentation and examples available online.

Pipeline

Pipelines are a key component of the SensiML workflow. Pipelines store the preprocessing, feature extraction, and model building steps.

Model training can be done using either SensiML cloud or using Tensorflow to train the model locally and the uploading it to SensiML in order to obtain the firmware code to run on the embedded device.

In order to train the model locally, one needs to build a data pipeline to process data and calculate the feature vector. This is done using the following pipeline:

  • The Input Query function which specifies what data is being fed into the model
  • The Segmentation which specifies how the data should be feed to the classifier.
  • Windowing segmented which captures data depending on gesture expected length.
  • The Feature Generator which specify which features should be extracted from the raw time-series data
  • The Feature Selector which selects the best features. In this case, we are using the custom feature selector to downsample the data.
  • The Feature Transform which specifies how to transform the features after extraction. In this case, it is to scale them to 1 byte each

Here is the python code for the pipeline

dsk.pipeline.reset()
dsk.pipeline.set_input_data('wand_10_movements.csv', group_columns=['Label'], label_column='Label', data_columns=sensor_columns)

dsk.pipeline.add_segmenter("Windowing", params={"window_size": 350, "delta": 25, "train_delta": 25, "return_segment_index": False})

dsk.pipeline.add_feature_generator(
    [
        {'subtype_call': 'Statistical'},
        {'subtype_call': 'Shape'},
        {'subtype_call': 'Column Fusion'},
        {'subtype_call': 'Area'},
        {'subtype_call': 'Rate of Change'},
    ],
    function_defaults={'columns': sensor_columns},
)

dsk.pipeline.add_feature_selector([{'name':'Tree-based Selection', 'params':{"number_of_features":12}},])

dsk.pipeline.add_transform("Min Max Scale") # Scale the features to 1-byte

TensorFlow model

I use the TensorFlow Keras API to create a neural network. This model is very simplified because not all Tensorflow functions and layers are available in the microcontroller version. I use a fully connected network to efficiently classify the gestures. It takes in input the features vectors created previously with the pipeline (12).

from tensorflow.keras import layers
import tensorflow as tf

tf_model = tf.keras.Sequential()

tf_model.add(layers.Dense(12, activation='relu',kernel_regularizer='l1', input_shape=(x_train.shape[1],)))
tf_model.add(layers.Dropout(0.1))
tf_model.add(layers.Dense(8, activation='relu', input_shape=(x_train.shape[1],)))
tf_model.add(layers.Dropout(0.1))
tf_model.add(layers.Dense(y_train.shape[1], activation='softmax'))

# Compile the model using a standard optimizer and loss function for regression
tf_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Training

The training is performed by feeding the neural network with the dataset by batches of data. For each batch of data a loss function is computed and the weights of the network are adjusted. Each time it loops through the entire training set, then is called an epoch. In the following picture:

  • at the top left we can see the evolution of the loss function, it decreased, meaning that it converges to a optimal solution.
  • at the bottom left we can see the evolution of the accuracy of the model, it increases!
  • at the right we have the confusion matrix for the validation and train set.

Model training performance

The confusion matrix provides information not only about the accuracy but also about the kind of errors of the model. It’s often the best way to understand which classes are difficult to distinguish.

Once you are satisfied with the model results, it can be optimized using Tensorflow quantize function. The quantization reduces the model size by converting the network weights from 4-byte floating point values to 1-byte unsigned int8. Tensorflow provides the following built-in tool:

# Quantized Model
converter = tf.lite.TFLiteConverter.from_keras_model(tf_model)
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
converter.representative_dataset = representative_dataset_generator
tflite_model_quant = converter.convert()

There are more benefits by quantizing the model for Cortex-M processors like the Quickfeather, which uses some instructions that gives a boost in performance.

The quantized model can be uploaded to SensiML in order to obtain a firmware to flash to the QuickFeather. One can download the model using the jupyter notebook widget or in sensiml cloud application. There are two available formats:

  • binary: this can be flashed directly to the QuickFeather. The results are transferred using serial output.
  • library: this is a knowledgepack form, which can be used in Qorc SDK to compile. There is more flexibility for this option, because one can modify source code before compiling.

Export model to Quickfeather

The knowledgepack can be customized in order to light the QuickFeather led with a different colour depending on the prediction made. This can be done by adding the following function to the src/sml_output.c file.

// src/sml_output.c
static intptr_t last_output;

uint32_t sml_output_results(uint16_t model, uint16_t classification)
{

    //kb_get_feature_vector(model, recent_fv_result.feature_vector, &recent_fv_result.fv_len);

    /* LIMIT output to 100hz */

    if( last_output == 0 ){
        last_output = ql_lw_timer_start();
    }

    if( ql_lw_timer_is_expired( last_output, 10 ) ){
        last_output = ql_lw_timer_start();

        if ((int)classification == 1) {
            HAL_GPIO_Write(4, 1);
        } else {
            HAL_GPIO_Write(4, 0);
        }

        if ((int)classification == 2) {
            HAL_GPIO_Write(5, 1);
        } else {
            HAL_GPIO_Write(5, 0);
        }

        if ((int)classification == 3) {
            HAL_GPIO_Write(6, 1);
        } else {
            HAL_GPIO_Write(6, 0);
        }
    	sml_output_serial(model, classification);
    }
    return 0;
}

Finally the model can be compiled using Qorc SDK and flashed again to the QuickFeather.

Test model using Quickfeather

One can use a Li-Po battery with the battery connector of the QuickFeather in order to have complete autonomy. Then using a nice spoon like the following one can improvise a magic wand πŸͺ„:


QuickFeather as a magic wand

The following video shows the recognition system in action, the colours mean he following:

  • red for O gesture
  • green for W gesture
  • blue for Z gesture

Your browser doesn't support HTML5 video.

Conclusions

QuickFeather is a device completely adapted for tiny machine learning models. This use case provides a simple example to demystify the whole workflow for implementing machine learning algorithms to microcontrollers, but it can be extended for more complex use cases, like the one provided in the Hackster.io Climate Change Challenge.

SensiML provides provides nice tools to simplify machine learning implementation for microcontrollers. They provide software like Data Capture Lab, which capture data and also provides a labelling module. However, for this case I prefer to use Label Studio, which is more generic tool, that works for most use cases.

The notebook with the complete details about the model training can be found in this gist.

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