Summary of SHL Challenge 2024: Motion Sensor-based Locomotion and Transportation Mode Recognition in Missing Data Scenarios
Bibliographic record
Abstract
The paper summarizes the contributions of participants to the sixth Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp/ISWC 2024. The goal of this machine learning/data science challenge is to recognize eight locomotion and transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the motion (accelerometer, gyroscope, magnetometer) sensor data of a smartphone in a way which is user-independent and smartphone position-independent, and as well robust to data missing during deployment. The training data of a 'train' user is available from smartphones placed at four body positions (Hand, Torso, Bag and Hips). The testing data originates from 'test' users with a smartphone placed at one of three body positions (Torso, Bag or Hips). In addition, the test data has one or multiple sensor modalities randomly missing from each time frame (5 seconds). Such a scenario may occur if a device turns on and off dynamically sensors to save power, or due to limited computational or memory capacity. We introduce the dataset used in the challenge and the protocol of the competition. We present a meta-analysis of the contributions from 7 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, one submission achieved an F1 score between 70% and 80%, two between 60% and 70%, three between 50% and 60%, and one below 50%. Finally, we present a baseline implementation addressing missing sensor modalities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.009 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.033 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".