تقرير
Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data Modalities
| العنوان: | Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data Modalities |
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| المؤلفون: | Van Der Donckt, Jeroen, Van Der Donckt, Jonas, Van Hoecke, Sofie |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: Accepted at HASCA workshop - SHL challenge (UbiComp 2024) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.11048 |
| رقم الأكسشن: | edsarx.2407.11048 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |