Transition-points-based Segmentation and Hierarchical Classification forLocomotion and Transportation recognition on Radio-data
Nhat Tan Le, Nazmun Nahid, Sozo InoueSozolab, Kyushu Institute of TechnologyIntroduction• The 2021 Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge aims to handle 8 transportation and locomotion modes (Still, Walk, Run, Bike, Car,Bus, Train, and Subway) based on Radio-data (GPS reception, GPS location, Wifi reception, and GSM cell tower scans).
• Commonly, transportation mode recognition is achieved by analyzing the accelerometer and gyroscope data. However, the combination of GPS, GIS, Wi-Fi, andCellular data can also solve that problem and enrich information about location and mobile connection.
• Our team (HuBi) proposed a pipeline that includes transition-points-based segmentation and hierarchical classification based on our deep analysis of this type ofdataset, which achieved an overall accuracy of 75% on the Validation dataset.

Methodology
Figure 1: The process of two classification layers and transitionpoints-based segmentation.
Our pipeline contains 2 main stages:• First classifier along with the transition-point-based
segmentation: classifying GPS points into 2 groups by SVM modeltrained on velocity and acceleration features, then relying onpoint-clusters of 2 groups (low-speed and high-speed) to handle
the segmentation.
• Second classifier: each group has different feature extraction
strategies to train the specific XGBoost model.

First Classifier

BA

Figure 3: Segmentation by detecting the mode transition point.
(black: high-speed point, white: low-speed point).

Figure 2. A: The bus frequently moves with a velocity not higher than the non- The smoothing process reduces the fake transition points in some cases:motorized transportation (such as Run, Bike) as the distance between two stops • The train or subway trips after classifying with SVM have many small low-speed
is not long enough to keep the high velocity within this short time. Consequently,segments due to their stop time. These problems can be solved by merging andwe considered the bus as a low-speed transportation mode.
smoothing (A).
B: The velocity and acceleration of low-speed (green) vs. high-speed modes • Similar to this concept, in bus trips the short high-speed segments are merged(red). With this distribution, we applied the SVM to handle the first classifier.
into the whole low-speed segment (B).

Second Classifier

Features
Distance*
Latitude**Longitude**
Velocity*
Acceleration*
GPS satellites**
Wifi connection
points **Cell Entries**

Low-speed
X

X
X

High-speed
Features
X
GPS satellites
change rate (CR)X
Wifi connection
X
points CR
X
Cell Entries CR
X
Stop rate
X
Velocity CR
X
Heading CR

Low-speed

High-speed
X

Table 1: All training features
•X
X

X
X
X

In high-speed group, features are directly extracted from thehigh-speed window after transition-point segmentation step.
Because high-speed mode can not transit to each other. Someadvance features related to connection points changes perdistance/time were applied in this case.

•In low-speed group, a time-window segmentation (4 minutes)
is implemented before extracting features.

XX•

XGBoost model was used to train these two group models
because of its high performance.

X* Including max, mean, standard deviation, percentile 50, and 75.
** Including max, mean, standard deviation

XResults
G0Table 2: Results of The First Classifier. Smoothing process
significantly increased the classification performance.
(G0: Low-speed group, G1: High-speed group)

12346578Precision

8993538192

100 10067Recall

9289578592

10067

100

F1 Score

9091558392

1008080

Table 3: Results of The Second Classifier. The overall
accuracy of both classifiers in high and low-speed
group was 88%.

Analysis
AG1

Figure 4: The confusion matrix of the final result inthe validation data. The overall accuracy was 75%.

Conclusion
BC

Figure 4: Some mis-prediction may be made due to high error in data collection (A),mis-labeling (B), and some exceptional vehicle states (C)• The initial classifier and segmentation based on transition
point significantly increase the final classification performance.
• The bus can be considered as low-speed transportation.
• Our work achieved an overall accuracy of 75% on thevalidation data, 94% in the first classifier, and 88% in thesecond classifier.
• Cases of false predictions are clearly analyzed in our study.