Sussex-Huawei Locomotion Transportation (SHL) recognition challenge 2021 aims at differentiating eight transportation modes with mobile phone signal sensor data, including Wifi, Cells, Location, GPS access and detail.
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Multiple Tree Model Integration for Transportation Mode Recognition
Yan Ren, Renmin University of China
MethodPipeline

Introduction
Sussex-Huawei Locomotion Transportation (SHL) recognition
challenge 2021 aims at differentiating 8 transportation modes
with 4 mobile phone signal sensor data, including Wifi, Cells,Location, GPS access and detail.
1234

567

8StillWalking

RunBikeCarBus

Train

SubwayResult & Discussion
OverviewDiscussion

The method gets 0.65 accuracy 1. More semantic featuresscore on validation dataset,should be derived withwhich can set a baseline for the specialized knowledge inproblem. The confusion matrix signal field. Currently, onlyis shown as below.
conventional statisticalmethods are applied.
2. A more intelligent postprocessing method is needed.
3. The method does notinclude deep learning
framework. The accuracy is
not as high as expected.
4. The model can not handle
individual differences well.

Data Pre-Processing
Data pre-processing includes timestamp alignment, settling NaN value etc. A 0-1
feature is added for each signal source to discriminate between no signal received.

Feature Extraction
1. Basic features are classified into point features and window features. Both ofthem are in statistical domain.
2. Then a series of descriptive statistics are applied to screen out features that do
not apparently discriminating.
3. Possible data transformations are also explored. e.g. log transformation is used
for 'speed' to handle right-skew problem.
Finally, 32 discriminative features are extracted as listed in table.

Model Integration
Three tree based models (Random Forest, LightGBM, DT Tree Bagging) aretrained separately. The results of them
are combined to get the raw prediction.

Data Pre-Processing
To keep the continuity of transportation mode for each timestamp, original
point predictions are replaced by mean values within the window (size of 120s).

Reference[1] H. Gjoreski, M. Ciliberto, L. Wang, F.J.O. Morales, S. Mekki, S. Valentin, and D. Roggen, “TheUniversity of Sussex-Huawei locomotion and transportation dataset for multimodal analytics withmobile devices,” IEEE Access 6 (2018): 42592-42604. [2] L. Wang, H. Gjoreski, M. Ciliberto, S. Mekki, S. Valentin, and D. Roggen, “Enablingreproducible research in sensor-based transportation mode recognition with the Sussex-Huawei dataset,” IEEE Access 7 (2019): 10870-10891.

Contact
Github: Ryanna-github
Email: renyan_ruc@163.com