DeepPostures

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This repository contains code artifacts for CHAP posture classification work:

• MSSE-2021/: CHAP1.0 PyTorch implementation for hip accelerometer sitting pattern classification, released with The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study in Older Adults.
• CHAP2/: CHAP2.0 PyTorch code for CHAP zero-shot prediction and fine-tuning on wrist/hip accelerometer data, including iWatch and SOL/PASOS workflows.

We strongly suggest starting with our website and then using the folder that matches your workflow. Use MSSE-2021/ for the original CHAP pipeline and CHAP2/ for CHAP zero-shot/fine-tuning workflows.

The current codebase has been migrated to PyTorch. To access the previous TensorFlow implementation switch to tensorflow branch. The tensorflow branch contains implementation for both /JMPB-2021 and /MSSE-2021

Pre-Requisites

We recommend first installing Anaconda and then running the following commands to setup the environment. We also recommend using a machine that has GPU support, specially if you plan to train your own models. A CPU machine can be used if the goal is using pre-trained models to predict posture.

conda env create -f INFRA/CONDA/deep_postures_pt_gpu.yml # for cpu use INFRA/CONDA/deep_postures_pt_cpu.yml
conda activate deep_postures

Alternatively, you can use conda to install Python 3 and use pip to install the following rerquired packages.

conda create -n deep_postures_pytorch python=3.11
conda activate deep_postures_pytorch
pip install torch==2.4.1
pip install numpy
pip install pandas
pip install scipy
pip install h5py
pip install scikit-learn==1.5.2
pip install tqdm==4.66.6

For CHAP2.0 workflows, install the base environment above and then install the additional packages listed in CHAP2/requirements.txt.

Instructions

• Clone the repository using git git clone https://github.com/ADALabUCSD/DeepPostures.git
• Navigate to the code directory of the workflow you want to explore and follow the instructions in the README file there. The main branch contains the PyTorch implementation for MSSE-2021 and CHAP2.0 workflows. The tensorflow branch contains the previous TensorFlow implementation for both MSSE-2021 and JMPB-2021.
• If you face any problems/issues, please create an issue in GitHub issue tracker.

Acknowledgement

This work was supported by grant number R01DK114945 from the National Institute of Diabetes and Digestive and Kidney Diseases. It was also supported in part by a Hellman Fellowship, an NSF CAREER Award under award number 1942724, and a gift from VMware. The content is solely the responsibility of the authors and does not necessarily represent the views of any of these organizations. We thank the members of UC San Diego's Database Lab and Center for Networked Systems for their feedback on this work.