# Magnitude and Rotation Invariant Detection of Transportation Modes with Missing Data Modalities

arXiv: https://arxiv.org/abs/2407.11048 · HTML: https://mlsanity.com/paper/2407.11048/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [tsflex: flexible time series processing & feature extraction](https://mlsanity.com/paper/2111.12429/) (2021)
- [Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring](https://mlsanity.com/paper/2207.07753/) (2022)
- [Plotly-Resampler: Effective Visual Analytics for Large Time Series](https://mlsanity.com/paper/2206.08703/) (2022)

## Abstract (arXiv)

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.