Rishikanth Chandrasekaran
Xiaofan Jiang
ABSTRACT
With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of their auditory senses that would have provided important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. In this demonstration we present SEUS, a wearable system aimed at Sense Enhancement for Urban Safety. SEUS uses a three-stage architecture, consisting of headset mounted audio sensors, an embedded front-end for signal processing and feature extraction, and machine learning based classification on a smartphone, to provide early danger detection for pedestrians in real-time.
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- SEUS: A Wearable Multi-Channel Acoustic Headset Platform to Improve Pedestrian Safety: Demo Abstract
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