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JointAnglePrediction_JOB

Official code repository for the paper:
Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework
Eric Rapp*, Soyong Shin*, Wolf Thomsen, Reed Ferber, and Eni Halilaj
Journal of Biomechanics, 2021

Installation:

1. Download the source code via git clone

# Clone the repository
 git clone https://github.com/CMU-MBL/JointAnglePrediction_JOB.git

​

2. Set up the data folder structure

cd JointAnglePrediction_JOB && mkdir Data
mv <your extracted data folder> ./Data/

or you can place your data at SomeDir and soft-link to the directory by:

cd JointAnglePrediction_JOB
ln -s <dir to SomeDir> ./Data
# Desired data folder structure
JointAnglePrediction_JOB | Data  | 1_Extracted           | walking/running_meta.h5
                                 | 2_Processed           | walking/running_data.h5

The scripts will automatically generate a result folders under Data. But, if you have your own preferred data structure, please modify each path directly in the script.

3. Install the required libraries

You can install all the required libraries by running:

pip install -r requirements.txt

However, we recommend that you create a new virtual environment (such as conda) and install those requirements. In the case that your workstation has a problem running or installing the libraries, please report it using the Issues tab.

How to run (Demo version):

This demo version of the code allows you to run the framework using your own IMU data. The following steps are required.

1. Preparation

IMU data (acceleration and angular velocity) from two segments and ground truth joint angle (optional), as well as the trained model for predicting joint angles and segment orientations are needed. In the model folder, model.pt (pretrained model), model_kwargs.pkl (model key arguments), and norm_dict.pt (normalization dictionary) should exist. This means that your custom data structure should look as follows:

Your root-path          | <Left or Right>_seg1_acc.npy
                        | <Left or Right>_seg2_acc.npy
                        | <Left or Right>_seg1_gyr.npy
                        | <Left or Right>_seg2_gyr.npy
                        | <Left or Right>_mocap_angle.npy (optional)

Your angle model-path   | model.pt
                        | model_kwargs.pkl
                        | norm_dict.pt
                        
Your orient model-path  | model.pt
                        | model_kwargs.pkl
                        | norm_dict.pt

2. Run the code

python demo.py --joint <the type of joint ('Knee', 'Hip', 'Ankle') \
               --activity <the type of activity ('Walking', 'Running') \
               --root-path <path to the folder containing your data> \
               --angle-model-fldr <folder path of angle prediction model> \
               --ori-model-fldr <folder path of orientation prediction model> \
               --result-fldr <folder to save the result files> \
               --use-cuda <cuda configuration (True, False)>

How to run (Entire Framework):

1. Data preprocessing

# Given marker cluster data, this piece of code will create coordinate systems and generate simulated inertial data
python 0_preprocessing/01_preproc_dataset.py
# Given inertial data, this script checks for unusual features in the data and excludes those subjects 
# Refer to Calgary_issue_report.pdf for examples of data quality checks
python 0_preprocessing/02_check_dataset.py

2. Get the best neural network model

# Given the cleaned dataset, this script trains both CNNs and LSTMs utilizing hyperparameter optimization to predict joint kinematics
# Hyperopt sweeps over given sets of parameters, and each evaluation tries a different combination of those parameters.
python _1_nn_hyperopt_training/11_optimize_hyperparams.py
# This script compiles the model results from optimizing the hyperparameters and outputs an Excel file that compare the different performances
python _1_nn_hyperopt_training/12_summarize_results.py
# This script compares the performances of the model results and saves the best performing model configuration in a separate directory for use in the framework
python _1_nn_hyperopt_training/13_get_best_results.py

3. Run optimization

# This script predicts the orientation of sensors with the fixed hyperparameters of neural networks. The result will be saved in 5_Optimization/NN_Prediction folder
python _2_optimization/21_predict_orientation.py
# This script calculates priors for the weighted sum (weight, scale factor) using validation data
python _2_optimization/22_get_optimization_parameters.py
# This script runs top-down optimization, which minimizes the reconstruction error of angular velocity data
python _2_optimization/23_run_optimization.py
# This script calculates the performance of optimization and displays it on the terminal screen
python _2_optimization/24_get_final_results.py