Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data Modalities
Pages 597 - 602
Abstract
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.
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Index Terms
- Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data Modalities
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Published: 05 October 2024
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UbiComp '24: The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing
October 5 - 9, 2024
Melbourne VIC, Australia
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