SBIR/STTR Research Award

STTR Phase I: Smartphone-based Biomechanical Analysis for Job Risk Assessment

Award identifier 4efa0bf70cf78b68a7a7c5e165813cdcd27009fe2a7b83dca333b70164deca24Awarding agency National Science Foundation

Award amount

$256,000

Program and phase

STTR · Phase I

Award year

2021

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Award details

Awarding agency
National Science Foundation
Branch
Not reported
Recipient company
KINETICA LABS, INC.
Proposal award date
2021-08-01
Contract end date
2022-07-31
UEI
NBWMFWT5JK23
Solicitation number
Not reported
Topic code
DH

Research abstract

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to prevent work-related musculoskeletal disorders (WMSDs), a leading cause of pain, suffering, and disability in the US workforce. The proposed technology estimates forces exerted on the human body and 3D motions solely by processing videos captured with a smartphone, and without needing to attach sensors to the workers or objects. This technological advancement will provide safety professionals with a rapid, easy and affordable ergonomic risk assessment tool without hindering activity at job sites, improving outcomes for potential patients. Among many industries that suffer from WMSDs, immediate targets are manufacturing and distribution that have high injury rates. This Small Business Technology Transfer (STTR) Phase I project aims to overcome the current hurdles posed by invasive, time-consuming, and cumbersome force measurement for ergonomic risk assessment at job sites. The proposed technology estimates forces exerted on key body parts (e.g., neck, shoulder, back, and knees) and their motions through videos captured with a smartphone and without interfering with workers’ ongoing work, thereby making ergonomic risk assessment non-invasive, rapid and easy. However, for application at real job sites where frequent occlusions (objects or structures obscuring the worker’s body) and complicated work environments pose a significant challenge to estimating forces and motions from videos, the algorithms used in the technology have to be improved and tested with a diverse range of motion and force data. This project aims to address these technical challenges through a new data augmentation approach, refinement and optimization of force estimation models, and extensive lab and real job site data collection and testing. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Technology keywords

Not reported

Agency intelligence

National Science Foundation

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