Abstract:
This paper aims to build a novel classification model that can distinguish the typical motion activities that a traveler would perform
in a metro-journey. Following motion activities are focused in this
work: waiting in a queue, traveling in a metro train, climbing-up,
climbing-down, walking and stationary. We aim to build a classifier
which can work on data sampled from smartphone sensors at a low
frequency (4Hz). However, it is non-trivial to do so as the mentioned
activities are not easily separable in data sampled at low frequency.
Current works focus on data sampled at high frequency (40Hz).
Also, they don’t consider metro-journey specific activities such
as queue. Our proposed model focuses on all the metro-journey
specific activities while using data sampled at low frequency (4Hz).
Experimental evaluation (datasets collected in Delhi Metro-rail
network) indicate superior performance of our classifier (mean
accuracy 92%) over the related work (mean accuracy 70%).
