Machine vision system to reduces risk of injury.
December 23, 2014
by Nima Ziraknejad
AUTO21 Network of Centres of Excellence researchers have spent the past five years researching and developing a new position estimation system that would help spare vehicle occupants the pain – and the costs to society – incurred from whiplash and associated neck injuries. Integrated in an automatic restraint system, it would eliminate whiplash or lessen its severity by adapting the restraint to the proper position prior to a collision.
Capacitive proximity sensing and vision techniques are applied to estimate details of the occupant’s head. Machine vision and image processing algorithms allow automatic inspection and analysis of digital images acquired in real time from sensory devices such as cameras and optical transducers. The optical sensors are accompanied by other electronic components such as capacitive sensors and infrared lighting modules, making them more robust and accurate in, for example, automotive applications where temperature, humidity and lighting conditions are variable.
If the technology is proven to be effective, vehicle occupants won’t have to worry about properly positioning their headrests.
Field-testing the technology
NZ Technologies Inc., a Vancouver-based advanced technology start-up launched in 2009, is developing these solutions for various industries within the automotive, residential, and clinical sectors. It intends to commercialize the technology, but first it will collaborate with seat and head restraint manufacturers to field-test the performance and the accuracy of the final system in advanced automotive facilities.
The company has also joined the People and Planet Friendly Home Initiative (PPFH) headed by TELUS and the University of British Columbia (UBC) Institute for Computing, Information, and Cognitive Systems, which enables collaboration with UBC researchers to develop innovative, sustainable technologies. It’s adapting the vision and sensing technology for other uses, one being as an aid for seniors prone to falls. If successful, real-time machine vision would automatically detect the fall and make the necessary emergency call to obtain medical care.
Nima Ziraknejad is a former AUTO21 student researcher and founder and CEO of NZ Technologies Inc. AUTO 21 is a national research initiative supported by the Government of Canada through the Networks of Centres of Excellence Secretariat. Visit www.auto21.ca.
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This article appears in the Nov/Dec issue of PLANT.