Transportation Mode Detection based on smartphone sensors.

1. Accelerometer-Based Transportation
Mode Detection on Smartphones
Alice Valentini
alice.valentini7@studio.unibo.it
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2. Transportation mode detection
The capability to capture transportation behaviour on smartphones would have an
impact on:
❏ Urban planning
❏ Monitoring and addressing the spread of diseases
❏ Localization and positioning algorithms
❏ Calculating CO2 footprint or level of physical activity
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3. Transportation mode detection
Previous studies on transportation mode detection used GPS.
❏ Accurate when signal’s available but not energy-efficient.
❏ Unavailable when view to satellites is obstructed.
❏ Can not make accurate distinction between motorised transportation.
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4. Transportation mode detection
Variation in GSM and WiFi signal could be an alternative to GPS.
❏ More energy-efficient.
❏ Susceptible to varying of WiFi access point density and GSM cell size.
❏ Not reliable outside urban areas.
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5. Transport mode detection
This study focuses on the use of accelerometer for transport mode detection
because of its properties:
❏ Very low power consumption.
❏ Measures user’s movements directly and does not depend on external sources
❏ Highly detailed information about phone movement.
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6. Transportation mode detection
❏ This method uses horizontal and vertical acceleration data.
❏ Different type of vehicles can be identified from their acceleration and braking
periods.
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7. How does an accelerometer work?
❏ An accelerometer is a sensor that measures proper acceleration.
❏ They are incorporated in smartphones for detecting the device orientation.
❏ It behaves as a mass on a spring.
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8. Preprocessing and gravity estimation
❏ Gravity affects accelerometer signals.
❏ Preprocessing include calculating gravity
eliminated projections of vertical and horizontal
acceleration.
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9. Preprocessing and gravity estimation
❏ The algorithm used for estimating the gravity component uses a threshold for
detecting periods where the device is stationary.
❏ The threshold is dynamically adjusted according to the movement patterns as
during activities like walking or bicycling the measurements contain large
variations for a sustained period.
❏ To reduce the influence of orientation changes, the estimate of the gravity
component is reseted when a large shift in orientation is observed.
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10. Feature extraction
Features are extracted on three levels of granularity:
❏ Frame-based
❏ Peak-based
❏ Segment-based
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11. ❏ The frame-based features are able to capture characteristics of
high-frequency motion, e.g., physical movements during pedestrian activity.
❏ From each frame, 27 features are extracted from both vertical and horizontal
representations.
Frame-based features
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12. Peak-based features
❏ They capture movements with lower frequencies such as acceleration and
braking periods of motorised vehicles.
❏ These are essential for distinguishing between different motorised
transportation modalities.
❏ To extract these features, peak-areas that correspond to acceleration and
braking periods are identified in the horizontal acceleration projection.
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13. Peak-based features
❏ The beginning of a peak-area is identified by first detecting significant
changes in the horizontal acceleration.
❏ The end of a peak-area corresponds in a significant decrease in the horizontal
acceleration.
❏ Once the boundaries have been identified 10 peak features are extracted.
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14. Peak-based features
Peak areas detected from gravity eliminated horizontal acceleration during a metro
ride. 14

15. Segment-based features
❏ Segment-based features characterize patterns of acceleration and
deceleration periods.
❏ Features are:
❏ frequency of acceleration and breaking periods,
❏ frequency and duration of the intermittent stationary periods,
❏ In total 14 segment-based features are considered.
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16. Segment-based features
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17. Segment-based features
Peak and segment-based features describe the movement patterns of vehicles
instead of those of the users.
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18. Classification
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Full list of the 78 features considered.

19. Classification
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The transportation mode detection is decomposed hierarchically into 3 classifiers:

20. Classification
For classifying a frame classifiers use the machine learning algorithm AdaBoost.
It combines more weak classifiers into one strong classifier.
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21. Segment-based classification
❏ It is assumed that within a non-pedestrian segment the transportation modality
remains unchanged.
❏ Therefore, transition between consecutive stationary or motorised
transportation modes requires pedestrian activity.
❏ The transportation modality of the segment is predicted based on its history
using frame and peak features observed.
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22. Kinematic Motion classifier
❏ Uses frame-based accelerometer features to distinguish between pedestrian
and other modalities.
❏ The most effective features are variance for both horizontal and vertical
representations.
❏ Accuracy is over 99%.
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23. Stationary Classifier
❏ Uses peak and frame-based features for distinguishing between stationary
and motorised periods.
❏ Its precision is over 95%.
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24. Motorised Classifier
❏ Distinguishes between transportation modalities.
❏ The correct modality is detected with
approximately 80% precision.
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25. Evaluation
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26. Evaluation
❏ 67 hours of transportation data were collected among two scenarios.
❏ The mean precision is over 80%. The variance is relatively small.
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27. Evaluation
❏ Power consumption
Continuous transportation mode detection
requires minimal power consumption.
The average power consumption of the system is
85 mW.
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28. Discussion and improvements
❏ Detection latency is the main limitation and can be reduced using also other
sensors (GPS, GSM or WiFi).
❏ The application could be switched off during extended stationary behaviour.
❏ The system is susceptible to interference from extraneous kinematic events
(user interaction, orientation changes…).
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29. References
[1] Hemminki, Samuli, Petteri Nurmi, and Sasu Tarkoma. "Accelerometer-based
transportation mode detection on smartphones." Proceedings of the 11th ACM
Conference on Embedded Networked Sensor Systems. ACM, 2013.
[2] Mizell, David. "Using gravity to estimate accelerometer orientation." Proc. 7th
IEEE Int. Symposium on Wearable Computers (ISWC 2003). Vol. 252. 2003.
[3] Figo, Davide, et al. "Preprocessing techniques for context recognition from
accelerometer data." Personal and Ubiquitous Computing 14.7 (2010): 645-662.
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