The aim of this project is to evaluate student's dance movements. Students will be holding their iPhone during their performance and generated accelerometer and gyroscope data will be feed to a sensor-based HAR model to check the quality of the dance.
- English Valse
- Chasse bg
- Chasse fw
- Natural turn bw 1-3
- Natural turn fw 1-3
- Natural turn starting bw 1-6
- Natural turn starting fw 1-6
- Tango
- 2 walking steps bw
- 2 walking steps fw
- Rock turn starting bw
- Rock turn starting fw
To install the dependencies in python3
environment, run:
pip install -r requirements.txt
The saved_model
directory contains pretrained models for various feature sets (apart from sensor data). It was found that feeding the length of each sample, as well as basic statistics such as mean and std. significatly boosted the accuracy. Three different such models could be found here.
These models can be used directly for inference and performance evaluation as described in the following section.
Python script main.py
will be used for model training, inference and performance evaluation. The arguments for this
script are as follows:
-h, --help show this help message and exit
--train Training Mode
--test (Testing / Evaluation) Mode
--epochs EPOCHS Number of Epochs for Training
--dataset DATASET Name of Dataset for Model Training or Inference
For example, in order to train model for 75
epochs on PAMAP2
dataset and evaluate model performance, run the
following command:
TF_CPP_MIN_LOG_LEVEL=3 python main.py --train --test --epochs 75 --dataset pamap2
If the pretrained weights are stored in saved_model
directory and to infer with that, run the following command:
TF_CPP_MIN_LOG_LEVEL=3 python main.py --test --dataset pamap2
Make sure to tune the feature_columes
in config/data.yaml
accordingly
24th European Conference on Artificial Intelligence, ECAI 2020 by Saif Mahmud and M. Tanjid Hasan Tonmoy et al.