2335226
Cooperative Agreement
Overview
Grant Description
SBIR Phase II: AI-driven personalization for scalable custom-fit footwear.
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project addresses the limitations of mass-produced footwear sizing by introducing size-inclusive bespoke custom-fit shoes.
Poorly fitted footwear is an increasingly costly and painful problem with growing human, economic, and environmental implications.
Incorrect footwear fit is a significant driver of foot pain and disorders, including toe deformities, corns, foot ulceration, and ankle pain.
Additionally, e-commerce is on the rise, wherein up to 40% of shoes purchased online are returned with poor fit as the biggest driver.
These reduce retail margins and increase footwear’s carbon footprint.
Custom-fit shoes can solve the problem of poor fit; however, traditional custom-fit is a labor-intensive process.
Advancements in artificial intelligence can modernize and scale custom-fit shoe manufacturing, potentially reducing price points and lead times.
The proposed project aims to implement an automated solution for custom-fit footwear with three methods:
(1) Smartphone-based foot scanning and fit survey to obtain foot measurements and footwear construction preferences utilizing artificial intelligence,
(2) Automation of shoe last personalization, and
(3) Adaptive shoe componentry for custom-fit shoe construction.
The project utilizes artificial intelligence and machine learning, 3D modeling, computer vision, and 3D manufacturing to:
(I) Develop and deploy a highly accurate virtual foot image-to-measurements machine learning model;
(II) Expand a shoe last library to train and implement a machine learning model for foot measurement-to-shoe last prediction;
(III) Manufacture custom-fit shoes by combining personalized last with a compatible adaptive sole; and
(IV) Establish a customer feedback system for iterative shoe modification by incorporating user qualitative responses and sole wear patterns.
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.
Subawards are not planned for this award.
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project addresses the limitations of mass-produced footwear sizing by introducing size-inclusive bespoke custom-fit shoes.
Poorly fitted footwear is an increasingly costly and painful problem with growing human, economic, and environmental implications.
Incorrect footwear fit is a significant driver of foot pain and disorders, including toe deformities, corns, foot ulceration, and ankle pain.
Additionally, e-commerce is on the rise, wherein up to 40% of shoes purchased online are returned with poor fit as the biggest driver.
These reduce retail margins and increase footwear’s carbon footprint.
Custom-fit shoes can solve the problem of poor fit; however, traditional custom-fit is a labor-intensive process.
Advancements in artificial intelligence can modernize and scale custom-fit shoe manufacturing, potentially reducing price points and lead times.
The proposed project aims to implement an automated solution for custom-fit footwear with three methods:
(1) Smartphone-based foot scanning and fit survey to obtain foot measurements and footwear construction preferences utilizing artificial intelligence,
(2) Automation of shoe last personalization, and
(3) Adaptive shoe componentry for custom-fit shoe construction.
The project utilizes artificial intelligence and machine learning, 3D modeling, computer vision, and 3D manufacturing to:
(I) Develop and deploy a highly accurate virtual foot image-to-measurements machine learning model;
(II) Expand a shoe last library to train and implement a machine learning model for foot measurement-to-shoe last prediction;
(III) Manufacture custom-fit shoes by combining personalized last with a compatible adaptive sole; and
(IV) Establish a customer feedback system for iterative shoe modification by incorporating user qualitative responses and sole wear patterns.
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.
Subawards are not planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH PHASE II (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE II", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23516
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
New York,
New York
10019-4315
United States
Geographic Scope
Single Zip Code
Iambic was awarded
Cooperative Agreement 2335226
worth $990,900
from National Science Foundation in July 2024 with work to be completed primarily in New York New York United States.
The grant
has a duration of 2 years and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Cooperative Agreement was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase II Programs (SBIR/STTR Phase II).
SBIR Details
Research Type
SBIR Phase II
Title
SBIR Phase II: AI-DRIVEN PERSONALIZATION FOR SCALABLE CUSTOM-FIT FOOTWEAR
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II
project addresses the limitations of mass-produced footwear sizing by introducing size-inclusive bespoke custom-fit shoes. Poorly fitted footwear is an increasingly costly and painful problem with growing human, economic, and environmental implications.
Incorrect footwear fit is a significant driver of foot pain and disorders, including toe deformities, corns,
foot ulceration, and ankle pain. Additionally, e-commerce is on the rise, wherein up to 40% of shoes
purchased online are returned with poor fit as the biggest driver. These reduce retail margins and increase
footwear’s carbon footprint. Custom-fit shoes can solve the problem of poor fit; however, traditional
custom-fit is a labor-intensive process. Advancements in artificial intelligence can modernize and scale
custom-fit shoe manufacturing, potentially reducing price points and lead times.
The proposed project aims to implement an automated solution for custom-fit footwear with three methods: (1) smartphone-based foot scanning and fit survey to obtain foot measurements and footwear construction preferences utilizing artificial
intelligence, (2) automation of shoe last personalization, and (3) adaptive shoe componentry for custom-fit shoe construction.
The project utilizes artificial intelligence and machine learning, 3D modeling, computer vision, and 3D manufacturing to: (i) develop and deploy a highly accurate virtual foot image-to-measurements machine learning model; (ii) expand a shoe
last library to train and implement a machine learning model for foot measurement-to-shoe last
prediction; (iii) manufacture custom-fit shoes by combining personalized last with a compatible adaptive
sole; and (iv) establish a customer feedback system for iterative shoe modification by incorporating user
qualitative responses and sole wear patterns.
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.
Topic Code
DH
Solicitation Number
NSF 23-516
Status
(Ongoing)
Last Modified 7/23/24
Period of Performance
7/15/24
Start Date
6/30/26
End Date
Funding Split
$990.9K
Federal Obligation
$0.0
Non-Federal Obligation
$990.9K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2335226
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
MKPYLF5QJFK9
Awardee CAGE
8REE7
Performance District
NY-12
Senators
Kirsten Gillibrand
Charles Schumer
Charles Schumer
Modified: 7/23/24