Affiliation:
1. HTC, Taiwan
2. National Taiwan University
Abstract
Sensors on mobile phones and wearables, and in general sensors on IoT (Internet of Things), bring forth a couple of new challenges to big data research. First, the power consumption for analyzing sensor data must be low, since most wearables and portable devices are power-strapped. Second, the velocity of analyzing big data on these devices must be high, otherwise the limited local storage may overflow.
This paper presents our hardware-software co-design of a classifier for wearables to detect a person's transportation mode (i.e., still, walking, running, biking, and on a vehicle). We particularly focus on addressing the big-data small-footprint requirement by designing a classifier that is low in both computational complexity and memory requirement. Together with a sensor-hub configuration, we are able to drastically reduce power consumption by 99%, while maintaining competitive mode-detection accuracy. The data used in the paper is made publicly available for conducting research.
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Cited by
77 articles.
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