  Although many deep learning (DL) algorithms have been proposed for the
IMU-based HAR domain, traditional machine learning that utilizes handcrafted
time series features (TSFs) still often performs well. It is not rare that
combinations among DL and TSFs show better accuracy than DL-only approaches.
However, there is a problem with time series features in IMU-based HAR. The
amount of derived features can vary greatly depending on the method used to
select the 3D basis. Fortunately, DL's strengths include capturing the features
of input data and adaptively deriving parameters. Thus, as a new DNN model for
IMU-based human activity recognition (HAR), this paper proposes rTsfNet, a DNN
model with Multi-head 3D Rotation and Time Series Feature Extraction. rTsfNet
automatically selects 3D bases from which features should be derived by
extracting 3D rotation parameters within the DNN. Then, time series features
(TSFs), based on many researchers' wisdom, are derived to achieve HAR using
MLP. Although rTsfNet is a model that does not use CNN, it achieved higher
accuracy than existing models under well-managed benchmark conditions and
multiple datasets: UCI HAR, PAMAP2, Daphnet, and OPPORTUNITY, all of which
target different activities.
