Use this 81.2% accuracy model to classify Neurosity Kinesis data across 8 classes.
The dataset it build on over 100,000 Kinesis trials. These are covaraince matricies, so 8x8 arrays.
Yes the data is 100% available to you here.
The dataset contains 8 classes:
0,2,4,6,7,8,22,34 which is Rest, Left Arm, Tongue, Jumping Jacks, Left Foot, Push, Disappear
Each class has over 10,000 trials.
Each Trial was recorded with a Neurosity Crown in the Developer Console while training a real-time LDA model. The feature is an 8x8 covariance matrix over about a 2 second period.
Download the dataset here: Dataset
We had to remove many rest trials because half of the 455,100 trials were rest trials. We capped the rest trials to the next largest class.
You'll need to create a .env file to store four environment variables:
NEUROSITY_EMAIL="email" NEUROSITY_PASSWORD="password" NEUROSITY_DEVICE_ID="ID" RUNPOD_API_KEY="KEY"
Run Crown processing code with with python main.py
You'll need to deploy the model on runpod. Here's a guide on how to get started with runpod.
Find the docker image at https://hub.docker.com/repository/docker/andrewjaykeller/sw-kinesis-ai/ In this image, you'll find the model joblib file
You may get the latest model from my google drive until we figure out a more efficent way.
- model - random_forest_model2.joblib
I've been running training on an A100 but I think to run the grid search i may need more memory so am looking at alternativs.
Checkout the notebook src/notebooks/train.ipynb to see how to train the model. You'll need to download the data set and add it to the data folder, make the data directory if it's not there.