Human Activity Recognition (HAR) is the task of detecting what activities a human is performing based on motion. This type of activity recognition is using an Inertial Measurement Unit (IMU) carried on the person. The IMU consists has at least one accelerometer, gyro or magnetometer - or a combination of these. It can be a smartphone, smartwatch, fitness tracker, or more specialized device. The activities can for example be general daily activities, domestic activities, excercises, et.c.
The same kind of approach can also be applied to animals (Animal Activity Recognition), which enables tracking of pets, lifestock and wild animals. This has been used for many kinds of animals - such as cats, dogs, diary cattle.
The same approach can be used for simple gesture recognition.
Working. Tested running on ESP32 with MicroPython 1.24.
NOTE: This is primarily example code for a Human Activity Recognition, not a generally-useful pretrained model. The dataset used is rather simple, and may not reflect the data you get from your device
- which will lead to poor classifications. For a real world usage you should probably replace the dataset with your own data, collected on your own device.
This example uses an approach based on the paper Are Microcontrollers Ready for Deep Learning-Based Human Activity Recognition?. For each time-window, time-based statistical features are computed, and then classified with a RandomForest model.
The example uses the UCI-HAR dataset. The classes are by default limited to the three static postures (standing, sitting, lying) plus three dynamic activities (walking, walking downstairs, walking upstairs). The data is from a waist-mounted smartphone. Samplerate is 50Hz. By default only the accelerometer data is used (not the gyro).
- Add an illustrative image
- Run the training + test/evaluation in CI
- Add demonstration on LilyGo T-Watch 2020 (BMA423 accelerometer)
To run the example on your PC using Unix port of MicroPython.
Make sure to have the Unix port of MicroPython setup. On Windows you can use Windows Subsystem for Linux (WSL), or Docker.
Download the files in this example directory
git clone https://github.com/emlearn/emlearn-micropython.git
cd emlearn-micropython/examples/har_trees
Install the dependencies
micropython -m mip install https://emlearn.github.io/emlearn-micropython/builds/master/x64_6.3/emlearn_trees.mpy
micropython har_run.pyResults should be around 85% accuracy.
Make sure to have MicroPython installed on device.
The fastest and easiest to to install on your device is to use Viper IDE.
This will install the library and the example code:
Make sure to have MicroPython installed on device.
Install the dependencies
mpremote mip install https://emlearn.github.io/emlearn-micropython/builds/master/xtensawin_6.3/emlearn_trees.mpy
mpremote mip install github:jonnor/micropython-npyfile
mpremote mip install github:jonnor/micropython-zipfileCopy example code
mpremote cp har_uci_trees.csv har_uci.testdata.npz timebased.py :
Run model evaluation on a test set
mpremote run har_run.py
Results should be around 85% accuracy.
Requires a M5StickC PLUS 2. Using a MPU6886 accelerometer connected via I2C.
Install dependencies. In addition to the above
mpremote mip install github:jonnor/micropython-mpu6886
mpremote cp windower.py :
Run the classification
mpremote har_live.py
This will train a new model. Uses CPython on the PC.
Install requirements
pip install -r requirements.txt
Download training data (4 GB space)
python -m leaf_clustering.data.har.uci
Run training process
python har_train.py
This will output a new model (named _trees.csv).
Can then be deployed to device following the steps above.