This is the project of manuscript If-ConvTransformer: A Framework for Human Activity Recognition Using IMU Fusion and ConvTransformer, Ye Zhang, Longguang Wang, Huiling Chen, et. al, IMWUT/UbiComp 2022 [Paper]. Our team will release more interesting works and applications on sensor based time series (including HAR) soon. Please keep following our repository.

This paper proposes an HAR framework composed of an IMU fusion block and a ConvTransformer subnet. Extensive experiments on eight benchmark datasets demonstrate the superior performance of our framework. The contributions of this paper are summarized as follows:

• We propose an HAR framework, which comprises an IMU fusion block and a ConvTransformer subnet.
• We introduce an IMU fusion block for multi-sensor modal fusion. By taking the physical prior knowledge of different sensors into account, our IMU fusion block fuses the extracted features from multiple modalities effectively.
• With explicit motivations, we apply ConvTransformer as the backbone network. Moreover, the in-depth analyses of ConvTransformer are also provided, which also support our motivations.
• Our proposed HAR framework achieves superior performance on five smartphone based datasets and three wearable device based datasets.

@article{zhang2022if,
  title={IF-ConvTransformer: A framework for human activity recognition using IMU fusion and ConvTransformer},
  author={Zhang, Ye and Wang, Longguang and Chen, Huiling and Tian, Aosheng and Zhou, Shilin and Guo, Yulan},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume={6},
  number={2},
  pages={1--26},
  year={2022},
  publisher={ACM New York, NY, USA}
}

Tested on Windows 10, with Python 3.6, PyTorch 1.1, CUDA 10.0, and 1x NVIDIA 2080Ti.

On windows:

Click on main.py and run it. Adjust the hyperparameters in the constant file.

Or:

(1) Train.

HAPT dataset

python main.py --PATTERN TRAIN --DATASETS HAPT --CLASSIFIERS If_ConvTransformer_torch --BATCH_SIZE 128 --EPOCH 30 --LR 0.0002 --test_split 15

Opportunity dataset

python main.py --PATTERN TRAIN --DATASETS Opportunity --CLASSIFIERS If_ConvTransformer_W_torch --BATCH_SIZE 64 --EPOCH 100 --LR 0.0005 --test_split 50

(2) Test.

HAPT dataset

python main.py --PATTERN TEST --DATASETS HAPT --CLASSIFIERS If_ConvTransformer_torch --test_split 15

Opportunity dataset

python main.py --PATTERN TEST --DATASETS Opportunity --CLASSIFIERS If_ConvTransformer_W_torch --test_split 50

The 'If_ConvTransformer_torch' classifier is prepared for smartphone based datasets.

The 'If_ConvTransformer_W_torch' classifier is prepared for wearable device based datasets.

The project is just a shorthand version, since we will expand this conference work into a journal article. However, we absolutely have plans to release the whole HAR framework (including codes of data preprocessing, SOTA HAR methods, evaluation metrics et. al). It won't take long! Please keep following our repository!