  In wearable-based human activity recognition (HAR) research, one of the major
challenges is the large intra-class variability problem. The collected activity
signal is often, if not always, coupled with noises or bias caused by personal,
environmental, or other factors, making it difficult to learn effective
features for HAR tasks, especially when with inadequate data. To address this
issue, in this work, we proposed a Behaviour Pattern Disentanglement (BPD)
framework, which can disentangle the behavior patterns from the irrelevant
noises such as personal styles or environmental noises, etc. Based on a
disentanglement network, we designed several loss functions and used an
adversarial training strategy for optimization, which can disentangle activity
signals from the irrelevant noises with the least dependency (between them) in
the feature space. Our BPD framework is flexible, and it can be used on top of
existing deep learning (DL) approaches for feature refinement. Extensive
experiments were conducted on four public HAR datasets, and the promising
results of our proposed BPD scheme suggest its flexibility and effectiveness.
This is an open-source project, and the code can be found at
http://github.com/Jie-su/BPD
