  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.
