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STTR Phase II: Dynamic Robust Hand Model for Gesture Intent Recognition

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1738888
Agency Tracking Number: 1738888
Amount: $750,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: IT
Solicitation Number: N/A
Solicitation Year: 2015
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-09-01
Award End Date (Contract End Date): 2019-08-31
Small Business Information
10570 Whitney Way
Cupertino, CA 95014-4442
United States
DUNS: 078658977
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Raja Jasti
 (408) 489-8622
Business Contact
 Raja Jasti
Phone: (408) 489-8622
Research Institution
 Purdue University
 Karthik Ramani
Young Hall 155 South Grant Street
West Lafayette, IN 47907-2114
United States

 Nonprofit College or University

The broader impact/commercial potential of this project stems from addressing the important hand gesture based input challenges of VR/AR (expected to grow to $150B by 2020), Robotics and IoT ($135B by 2019 and $1.7T by 2020 respectively). This technology, if successful in mitigating the high technical risks, represents a huge leap in the state of the art in 3D hand models for gesture recognition and has the potential to be the industry standard for AR, VR, Robotics and IoT applications with broad societal impact in education, medical and healthcare. Its broader impact is further amplified by the potential in serving the needs of the disabled community in improving their quality of life by being better able to communicate, learn and adapt to their interaction needs. This Small Business Technology Transfer (STTR) Phase 2 project aims to significantly advance current 3D hand gesture recognition technology by developing a dynamic hand tracking model for gesture intent recognition. It is robust against occlusion and tolerant to variations in camera orientation and position. This research will result in a transformative leap above the current state of academic and commercial hand models and overcome key technical hurdles that have so far proven difficult to overcome. It solves the following key challenges and involves very high technical risks: 1) robust hand tracking while holding objects and 2) robust tangible interactions using objects without using any fiducial markers 2) low profile hand wearable for touch interaction detection. This Phase 2 project will achieve these objectives by 1) data acquisition and hand-object pose estimation, 2) understanding user intents with enhanced tangible interactions, and 3) system validation and user testing.

* Information listed above is at the time of submission. *

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