Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data Modalities
<|reference_start|>Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data Modalities: This work presents the solution of the Signal Sleuths team for the 2024 SHL recognition challenge. The challenge involves detecting transportation modes using shuffled, non-overlapping 5-second windows of phone movement data, with exactly one of the three available modalities (accelerometer, gyroscope, magnetometer) randomly missing. Data analysis indicated a significant distribution shift between train and validation data, necessitating a magnitude and rotation-invariant approach. We utilize traditional machine learning, focusing on robust processing, feature extraction, and rotation-invariant aggregation. An ablation study showed that relying solely on the frequently used signal magnitude vector results in the poorest performance. Conversely, our proposed rotation-invariant aggregation demonstrated substantial improvement over using rotation-aware features, while also reducing the feature vector length. Moreover, z-normalization proved crucial for creating robust spectral features.<|reference_end|>
@article{van der donckt2024magnitude,
    title={Magnitude and Rotation Invariant Detection of Transportation Modes with
  Missing Data Modalities},
    author={Jeroen Van Der Donckt, Jonas Van Der Donckt, Sofie Van Hoecke},
    journal={arXiv preprint arXiv:2407.11048},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11048},
    primaryClass={cs.LG cs.AI}
}