These New Smart Headphones Keep Users Aware of Oncoming Traffic
Michael Hemsworth — December 12, 2019 — Tech
References: datascience.columbia.edu & newatlas
A set of new smart headphones have been developed by a team of scientists at the Columbia University's Data Science Institute in a bid to help protect pedestrians from avoidable accidents. Created in partnership with colleagues from the University of North Carolina and Barnard College in New York, the prototype headphones work by continually tracking the audio present in an environment. The accompanying app will utilize machine learning to differentiate between sounds, detect when a vehicle is closing in and sound an alarm to alert the user.
The new smart headphones are reported to use very power to make them as applicable for mass production and use as possible. The headphones are presently being tested in the lab and on New York City streets.
Image Credit: Columbia University
The new smart headphones are reported to use very power to make them as applicable for mass production and use as possible. The headphones are presently being tested in the lab and on New York City streets.
Image Credit: Columbia University
Trend Themes
1. Smart Headphones for Pedestrian Safety - Opportunity to develop smart headphones that use machine learning to detect oncoming vehicles and alert users to prevent accidents.
2. Audio Tracking Technology - Opportunity to advance audio tracking technology to differentiate between sounds and identify potential hazards.
3. Integration of Machine Learning and Wearable Technology - Opportunity to integrate machine learning algorithms into wearable technology to enhance safety features.
Industry Implications
1. Wearable Technology - Wearable tech companies can explore developing smart headphones with innovative safety features.
2. Automotive - Automotive manufacturers can collaborate with headphone companies to integrate pedestrian safety features into future vehicle models.
3. Artificial Intelligence - AI technology companies can research and develop machine learning algorithms for audio detection in wearable devices.
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