Application of Online Transportation Mode Recognition in Games

• It is widely accepted that human activities largely contribute to global emissions and thus, greatly impact climate change. Awareness promotion and adoption of green transportation mode could make a difference in the long term. To achieve behavioural change, we investigate the use of a persuasive game utilising online transportation mode recognition to afford bonuses and penalties to users based on their daily choices of transportation mode. To facilitate an easy identification of transportation mode, classification predictive models are built based on accelerometer and gyroscope historical data. Preliminary results show that the classification true-positive rate for recognising 10 different transportation classes can reach up to 95% when using a historical set (66% without). Results also reveal that the random tree classification model is a viable choice compared to random forest in terms of sustainability. Qualitative studies of the trained classifiers and measurements of Android-device gravity also raise several issues that could be addressed in future work. This research work could be enhanced through acceleration normalisation to improve device and user ambiguity.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

accelerometer

android

gyroscope

history set

machine-learning algorithms

random tree

random forest

transportation mode recognition

green transportation

Pervasive Mobile Computing

Distribuerade datorsystem

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