Deep Learning of Biomechanical Dynamics in Mobile Daily Activity and Fall Risk Monitoring

Qingxue Zhang1

  • 1Indiana University-Purdue University Indianapolis

Details

Category

Poster Session

Theme

Health and Wellness Across the Lifespan

Sessions

12:15 - 14:15 | Wed 20 Nov | Upper Foyer Balcony | A1P-B

Poster Session - Health and Wellness Across the Lifespan 1

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Abstract

Daily activity and fall risk monitoring is highly important. There are 7 million fall injuries per year, and appropriate exercise can lower the risk of death by up to 20 to 70%. However, it is very challenging to accurately identify an activity due to the diversity of the human biomechanical dynamics. We propose a new intelligent computational approach, leveraging biomechanical dynamics enhancement and deep learning technologies. The detection accuracy of a total of 11,770 activities and 17 activity types is as high as 93.9%. This research is expected to greatly advance mobile activity monitoring in smart health era.

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