├── LICENSE ├── README.md ├── downloads └── model.h ├── gesture-data ├── flex.csv └── punch.csv ├── images ├── 3d-accelerometer.png ├── classifier-output.png ├── flex.png ├── gyroscope.png ├── model-h.png ├── model-head.png ├── plot.png ├── punch-data.png └── serial-data.png ├── model.h ├── nano-gesture recognition ├── nano-classifier │ ├── model.h │ └── nano-classifier.ino └── nano-data-collect.ino └── tinyml_on_the_edge.ipynb /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Tinyml-on-the-edge 2 | Arduino Nano 33 BLE Sense can be used to recognize gestures using machine learning. A tensorflow model is designed, built and trained using accelerometer and gyroscope sensors data on Google Colaboratory, which can handle jupyter notebooks online. The model is converted to tensorflow-lite model and later encoded on Arduino Nano 33 BLE Sense header file. A a gesture classifier uses the model to classify the data from both sensor types. IMU has numerous sensors including accelerometer, gyroscope and magnetometer. In this project, we are going to use accelerometer and gyroscope data to predict punch and flex gesture. This is based on [arduino tutorials](https://github.com/arduino/ArduinoTensorFlowLiteTutorials). 3 | 4 | This project will follow the following procedure. 5 | 1. Revisit [setting up arduino IDE](https://github.com/billiyz/nano-33-ble-sense) without going through the programming part of sensor data collection. 6 | 2. Look into accelerometer and gyroscope operation 7 | 3. Write a sketch to collect accelerometer and gyroscope data 8 | 4. Upload the data to the google colaboratory platform 9 | 5. Train a neural network 10 | 6. Convert the trained model into tensorflow lite model 11 | 7. Encode the model in arduino header file 12 | 13 | # Accelerometer and gyroscope operation. 14 | IMU sensor on the Arduino Nano 33 BLE Sense has three sensors; accelerometer, gyroscope and magretometer. These sensors measure forces acting on the body. They give acceleration and rotation information on the x, y and Z. 15 | 16 | Many accelerometers and gyroscopes are fabricated using micro-electromechanical systems (MEMS). The production process for the MEMS sensor combines silicon and mechanical functions on the same micrometer silicon substrate. The major components in these devices are the mechanical elements, the sensing mechanism and the application specific integrated circuit (ASIC) as explained in [digi-key](https://www.digikey.com/en/articles/techzone/2018/jan/apply-sensor-fusion-to-accelerometers-and-gyroscopes). 17 | 18 | ## Application 19 | * Mobile Smart Phones 20 | * Drones 21 | * Aeroplanes 22 | * Mobile IoT Devices 23 | * Anti-theft/Asset Tracking/Security Devices 24 | 25 | ## Operation 26 | ### Accelerometer 27 | In IMU 3d accelerometer sensor, the sensing mechanism for all three accelerometers is capacitive. The accelerometer measures linear acceleration along the x, y, and z axis. It has three sensors. The three sensors are mounted orthogonally. 28 | 29 | ![photo 1](images/3d-accelerometer.png) 30 | 31 | ### Gyroscope 32 | The gyroscpe measures the rate of change of angular velocity over time (angular changes). Many of these sensors are also manufactured using MEMS. Gyroscope also relies on the varying capacitance between silicon and mechanical elements, but with this configuration, the sensor generates capacitive changes with angular velocity changes. A 3D gyroscope also has 3 gyroscopic sensors mounted orthogonally to each other. 33 | 34 | ![photo 2](images/gyroscope.png) 35 | 36 | Accelerometer and gyrometer sensors face position and angular errors respectively, but fussion process in which they are made and manufactured, aims at perfecting on each other errors thus accuracy is reached. 37 | 38 | # Collect accelerometer and gyroscope sensor data 39 | 40 | To capture the accelerometer and gyroscope data, A sketch is written on arduino IDE and uploaded to the board, or go to [arduinoTensorFlowLite Tutorials](https://github.com/arduino/ArduinoTensorFlowLiteTutorials) for the skethes. The sketch in this repository is labelled 41 | **nano-33-gesture**. Set up Arduino IDE. It helps both in uploading inference models to Arduino Nano 33 BLE Sense as well as download training data from it in **.csv** format. Incase you haven't installed and configured your arduiono desktop IDE, please check the repository on installing Arduino desktop IDE or use the arduino web IDE. 42 | 43 | Plug the Arduino Nano 33 BLE Sense board to the computer, select the board type and the port. Arduino boards run small applications (also called sketches) which are compiled from .ino format Arduino source code, and programmed onto the board using the Arduino IDE or Arduino Create. Its time to collect the training data. 44 | 45 | ## Visualize collected data. 46 | 47 | On Arduino desktop IDE open Files, click New and save the sketch as **nan0-data-collect.ino**. There is a filter that performs signal preprocessing and filtering on the device before the data is output to the log. The skech does the following; 48 | 49 | 1. Detect motion using accelerometer and gyroscope 50 | 2. Trigger a sample window on detecting significant linear acceleration of the board 51 | 3. Sample the data every second at a frequency of 119Hz 52 | 4. Format a CSV data output via serial port 53 | 54 | When the scketch is ready, save. Go to Sketch, and click Upload. The sketch will indicate **Done uploading**. Turn to Tools. and click Serial Monitor to see the accelerometer and gyroscope data. You can as well plot on serial plot. However, our main objective is to have our data output in .csv formated document to be used in model training later on. 55 | 56 | ![photo 3](images/serial-data.png) ![photo 4](images/plot.png) 57 | 58 | Make sure at the top of the serial monitor you have aX,aY,aZ,gX,gY,gZ. With the Arduino Nano 33 BLE Sense in your hand, make a fist to signify a punch. Stretch the hand with the punch pointing the computer or other direction you choose for consistency. Make 20 punches. On the Serial monitor, uncheck Autoscrol button and copy the data. Save the data on your pc as **punch.csv**. Flex your hand from the screen or the direction you chose towards your body 20 times too while checking Serial Monitor and copy the data again, but this time save as **flex.csv** on your PC. 59 | 60 | # Google colaboratory data upload 61 | 62 | Open [google Colaboratory platorm](https://colab.research.google.com/drive/1uefbFF_D5cxqgREjxsKVwR9kD4K7tjtZ#scrollTo=Y2gs-PL4xDkZ), Move to Files. Drag and drop your **punch.csv** and **flex.csv** files in the sample_data folder. The Google colaboratory provides a Jupyter notebook that allows us to run our TensorFlow training in a web browser. 63 | 64 | ![photo 3](images/flex.png) 65 | 66 | ![photo 4](images/punch-data.png) 67 | 68 | 69 | # Train neural network, build and train a model 70 | 71 | While on the Google colaboratory platform, go trough the notebook till the end. Convertion of the trained model into tensorflow lite model is done near the end. When the **model.h** file is created under Files, click on the **model.h** file to download to your pc. If you open the model.h file, it will look like the one below. 72 | 73 | ![photo 5](images/model-h.png) 74 | 75 | # Encode the model in arduino header file 76 | 77 | Open accelerometer and gyroscope clasifier sketch on this repository named as **nano-classifier.ino**. It was created by Don Coleman, Sandeep Mistry and modified by Dominic Pajak, Sandeep Mistry. On the far top right, click on the drop down button and click New Tab. Name the file as **model.h** when you save it. Open the downloaded **model.h** file on your favourite editor and copy the content. Paste in your new **model.h** file you created alongside the classifier sketch and save. Upload the sketch and turn to Serial Monitor on on Tools to view result. The confidence of each gesture will be printed to the Serial Monitor showing (0 = low confidence, 1 = high confidence) 78 | 79 | ![photo 6](images/model-head.png) 80 | ![photo 7](images/classifier-output.png) 81 | 82 | Thank you. 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | -------------------------------------------------------------------------------- /gesture-data/punch.csv: -------------------------------------------------------------------------------- 1 | aX,aY,aZ,gX,gY,gZ 2 | 1.134,-1.803,0.960,135.681,64.392,-205.994 3 | 1.620,-2.044,0.945,144.409,92.346,-170.715 4 | 1.884,-2.040,0.843,135.803,110.352,-103.943 5 | 1.710,-1.835,0.711,97.778,120.422,-29.175 6 | 1.138,-1.357,0.601,55.664,113.159,39.490 7 | 0.433,-0.844,0.518,16.846,82.520,78.918 8 | -0.159,-0.419,0.492,-10.315,32.349,77.393 9 | -0.469,-0.178,0.498,-23.315,-21.301,46.753 10 | -0.525,-0.151,0.566,-21.240,-63.293,7.263 11 | 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/jaredmaks/tinyml-on-the-edge/5c7c8129af3c207594767a8de8a9b767d2dea745/images/serial-data.png -------------------------------------------------------------------------------- /nano-gesture recognition/nano-classifier/nano-classifier.ino: -------------------------------------------------------------------------------- 1 | /* 2 | IMU Classifier 3 | This example uses the on-board IMU to start reading acceleration and gyroscope 4 | data from on-board IMU, once enough samples are read, it then uses a 5 | TensorFlow Lite (Micro) model to try to classify the movement as a known gesture. 6 | Note: The direct use of C/C++ pointers, namespaces, and dynamic memory is generally 7 | discouraged in Arduino examples, and in the future the TensorFlowLite library 8 | might change to make the sketch simpler. 9 | The circuit: 10 | - Arduino Nano 33 BLE or Arduino Nano 33 BLE Sense board. 11 | Created by Don Coleman, Sandeep Mistry 12 | Modified by Dominic Pajak, Sandeep Mistry 13 | This example code is in the public domain. 14 | */ 15 | 16 | #include 17 | 18 | #include 19 | #include 20 | #include 21 | #include 22 | #include 23 | #include 24 | 25 | #include "model.h" 26 | 27 | const float accelerationThreshold = 2.5; // threshold of significant in G's 28 | const int numSamples = 119; 29 | 30 | int samplesRead = numSamples; 31 | 32 | // global variables used for TensorFlow Lite (Micro) 33 | tflite::MicroErrorReporter tflErrorReporter; 34 | 35 | // pull in all the TFLM ops, you can remove this line and 36 | // only pull in the TFLM ops you need, if would like to reduce 37 | // the compiled size of the sketch. 38 | tflite::ops::micro::AllOpsResolver tflOpsResolver; 39 | 40 | const tflite::Model* tflModel = nullptr; 41 | tflite::MicroInterpreter* tflInterpreter = nullptr; 42 | TfLiteTensor* tflInputTensor = nullptr; 43 | TfLiteTensor* tflOutputTensor = nullptr; 44 | 45 | // Create a static memory buffer for TFLM, the size may need to 46 | // be adjusted based on the model you are using 47 | constexpr int tensorArenaSize = 8 * 1024; 48 | byte tensorArena[tensorArenaSize]; 49 | 50 | // array to map gesture index to a name 51 | const char* GESTURES[] = { 52 | "punch", 53 | "flex" 54 | }; 55 | 56 | #define NUM_GESTURES (sizeof(GESTURES) / sizeof(GESTURES[0])) 57 | 58 | void setup() { 59 | Serial.begin(9600); 60 | while (!Serial); 61 | 62 | // initialize the IMU 63 | if (!IMU.begin()) { 64 | Serial.println("Failed to initialize IMU!"); 65 | while (1); 66 | } 67 | 68 | // print out the samples rates of the IMUs 69 | Serial.print("Accelerometer sample rate = "); 70 | Serial.print(IMU.accelerationSampleRate()); 71 | Serial.println(" Hz"); 72 | Serial.print("Gyroscope sample rate = "); 73 | Serial.print(IMU.gyroscopeSampleRate()); 74 | Serial.println(" Hz"); 75 | 76 | Serial.println(); 77 | 78 | // get the TFL representation of the model byte array 79 | tflModel = tflite::GetModel(model); 80 | if (tflModel->version() != TFLITE_SCHEMA_VERSION) { 81 | Serial.println("Model schema mismatch!"); 82 | while (1); 83 | } 84 | 85 | // Create an interpreter to run the model 86 | tflInterpreter = new tflite::MicroInterpreter(tflModel, tflOpsResolver, tensorArena, tensorArenaSize, &tflErrorReporter); 87 | 88 | // Allocate memory for the model's input and output tensors 89 | tflInterpreter->AllocateTensors(); 90 | 91 | // Get pointers for the model's input and output tensors 92 | tflInputTensor = tflInterpreter->input(0); 93 | tflOutputTensor = tflInterpreter->output(0); 94 | } 95 | 96 | void loop() { 97 | float aX, aY, aZ, gX, gY, gZ; 98 | 99 | // wait for significant motion 100 | while (samplesRead == numSamples) { 101 | if (IMU.accelerationAvailable()) { 102 | // read the acceleration data 103 | IMU.readAcceleration(aX, aY, aZ); 104 | 105 | // sum up the absolutes 106 | float aSum = fabs(aX) + fabs(aY) + fabs(aZ); 107 | 108 | // check if it's above the threshold 109 | if (aSum >= accelerationThreshold) { 110 | // reset the sample read count 111 | samplesRead = 0; 112 | break; 113 | } 114 | } 115 | } 116 | 117 | // check if the all the required samples have been read since 118 | // the last time the significant motion was detected 119 | while (samplesRead < numSamples) { 120 | // check if new acceleration AND gyroscope data is available 121 | if (IMU.accelerationAvailable() && IMU.gyroscopeAvailable()) { 122 | // read the acceleration and gyroscope data 123 | IMU.readAcceleration(aX, aY, aZ); 124 | IMU.readGyroscope(gX, gY, gZ); 125 | 126 | // normalize the IMU data between 0 to 1 and store in the model's 127 | // input tensor 128 | tflInputTensor->data.f[samplesRead * 6 + 0] = (aX + 4.0) / 8.0; 129 | tflInputTensor->data.f[samplesRead * 6 + 1] = (aY + 4.0) / 8.0; 130 | tflInputTensor->data.f[samplesRead * 6 + 2] = (aZ + 4.0) / 8.0; 131 | tflInputTensor->data.f[samplesRead * 6 + 3] = (gX + 2000.0) / 4000.0; 132 | tflInputTensor->data.f[samplesRead * 6 + 4] = (gY + 2000.0) / 4000.0; 133 | tflInputTensor->data.f[samplesRead * 6 + 5] = (gZ + 2000.0) / 4000.0; 134 | 135 | samplesRead++; 136 | 137 | if (samplesRead == numSamples) { 138 | // Run inferencing 139 | TfLiteStatus invokeStatus = tflInterpreter->Invoke(); 140 | if (invokeStatus != kTfLiteOk) { 141 | Serial.println("Invoke failed!"); 142 | while (1); 143 | return; 144 | } 145 | 146 | // Loop through the output tensor values from the model 147 | for (int i = 0; i < NUM_GESTURES; i++) { 148 | Serial.print(GESTURES[i]); 149 | Serial.print(": "); 150 | Serial.println(tflOutputTensor->data.f[i], 6); 151 | } 152 | Serial.println(); 153 | } 154 | } 155 | } 156 | } 157 | -------------------------------------------------------------------------------- /nano-gesture recognition/nano-data-collect.ino: -------------------------------------------------------------------------------- 1 | /* 2 | IMU Capture 3 | 4 | This example uses the on-board IMU to start reading acceleration and gyroscope 5 | data from on-board IMU and prints it to the Serial Monitor for one second 6 | when the significant motion is detected. 7 | 8 | You can also use the Serial Plotter to graph the data. 9 | 10 | The circuit: 11 | - Arduino Nano 33 BLE or Arduino Nano 33 BLE Sense board. 12 | 13 | Created by Don Coleman, Sandeep Mistry 14 | Modified by Dominic Pajak, Sandeep Mistry 15 | 16 | This example code is in the public domain. 17 | */ 18 | 19 | #include 20 | 21 | const float accelerationThreshold = 2.5; // threshold of significant in G's 22 | const int numSamples = 119; 23 | 24 | int samplesRead = numSamples; 25 | 26 | void setup() { 27 | Serial.begin(9600); 28 | while (!Serial); 29 | 30 | if (!IMU.begin()) { 31 | Serial.println("Failed to initialize IMU!"); 32 | while (1); 33 | } 34 | 35 | // print the header 36 | Serial.println("aX,aY,aZ,gX,gY,gZ"); 37 | } 38 | 39 | void loop() { 40 | float aX, aY, aZ, gX, gY, gZ; 41 | 42 | // wait for significant motion 43 | while (samplesRead == numSamples) { 44 | if (IMU.accelerationAvailable()) { 45 | // read the acceleration data 46 | IMU.readAcceleration(aX, aY, aZ); 47 | 48 | // sum up the absolutes 49 | float aSum = fabs(aX) + fabs(aY) + fabs(aZ); 50 | 51 | // check if it's above the threshold 52 | if (aSum >= accelerationThreshold) { 53 | // reset the sample read count 54 | samplesRead = 0; 55 | break; 56 | } 57 | } 58 | } 59 | 60 | // check if the all the required samples have been read since 61 | // the last time the significant motion was detected 62 | while (samplesRead < numSamples) { 63 | // check if both new acceleration and gyroscope data is 64 | // available 65 | if (IMU.accelerationAvailable() && IMU.gyroscopeAvailable()) { 66 | // read the acceleration and gyroscope data 67 | IMU.readAcceleration(aX, aY, aZ); 68 | IMU.readGyroscope(gX, gY, gZ); 69 | 70 | samplesRead++; 71 | 72 | // print the data in CSV format 73 | Serial.print(aX, 3); 74 | Serial.print(','); 75 | Serial.print(aY, 3); 76 | Serial.print(','); 77 | Serial.print(aZ, 3); 78 | Serial.print(','); 79 | Serial.print(gX, 3); 80 | Serial.print(','); 81 | Serial.print(gY, 3); 82 | Serial.print(','); 83 | Serial.print(gZ, 3); 84 | Serial.println(); 85 | 86 | if (samplesRead == numSamples) { 87 | // add an empty line if it's the last sample 88 | Serial.println(); 89 | } 90 | } 91 | } 92 | } 93 | --------------------------------------------------------------------------------