SBIR-STTR Award

An Automated Early Motor Development Risk Screener from Observational Video Recordings of Infants and Toddlers
Award last edited on: 9/24/2022

Sponsored Program
SBIR
Awarding Agency
NIH : NIMH
Total Award Amount
$2,567,509
Award Phase
2
Solicitation Topic Code
242
Principal Investigator
Bharath Modayur

Company Information

BSolutions Inc (AKA: Launch Bottle)

2151 Nw 97th Street
Seattle, WA 98117
   (206) 769-9179
   bharath@launchbottle.com
   www.launchbottle.com
Location: Single
Congr. District: 07
County: King

Phase I

Contract Number: 1R43MH107063-01A1
Start Date: 8/20/2015    Completed: 5/31/2017
Phase I year
2015
Phase I Amount
$360,563
?Early detection of autism leads to earlier treatment, which is proven to have a major impact on outcomes. In spite of recent advances in early Autism Spectrum Disorders (ASD) detection, the average age of diagnosis in the US is still around five. ASD diagnosis is currently performed via behavioral assessment, which requires highly specialized training, is not widely available in rural areas, and may be applied inconsistently. The need for specialized training for the administration of behavioral assessment and the effort involved in individual assessments preclude large scale deployment of these diagnostic methods in clinics and pediatricians' offices as well as large scale population studies. The Infant Brain Imaging Study (IBIS) is an early detection study at the University of Washington Autism Center which assesses behavioral and brain development in infants at high familial risk for ASD. Behavioral assessments include specialized observations of gross motor function, an area of development that is uniquely highlighted in the first year of life. This study along with others, highlight atypical motor development as the first step in the emergence of autism-related symptoms. Analyzing behavioral video data in order to assess/score individual subjects is a process that is time-intensive, subjective, and requires extensive training to attain reliability. We will build a Human Action Recognition Engine (HARE) that leverages computer vision tools to automatically extract, quantify and classify known motor actions - from video datasets - adding a significantly more efficient and standardized method to augment the current diagnostic standard of care. In this Phase I proposal, we will: 1. Develop the HARE prototype: automatic segmentation of subject of interest; determination of 3D orientation; extraction of features that are used in classification of actions from a predefined set defined in the IBIS behavioral assessment battery; 2. Leverage the intermediate outputs of the AR engine in establishing techniques to detect and de-identify faces of multiple, closely-interacting human subjects in video toward further processing and data sharing; 3. Explore early markers to classify subjects, based on actions detected, into ASD and non-ASD groups and evaluate the sensitivity and specificity of the classification engine. This Phase I effort will pave the way forthe creation of an action-annotated video repository from HARE's action recognition output. The repository will provide a rich source of highly-accessible data toward training and further research discoveries. Finally, the HARE system can systematically identify new, previously unidentified motor actions that may relate to increased risk for later developmental difficulties, particularly ASD. These novel early risk markers - in combination with existing assessments - would allow reliable, earlier identification of ASD.

Public Health Relevance Statement:


Public Health Relevance:
We propose to build an action-annotated video repository that will aid objective behavioral assessment of infants at risk for Autism Spectrum Disorders (ASD) and serve as a valuable training tool. We offer a methodology that can systematically reveal salient early motor markers that can result in efficacious screening of high risk infants.

NIH Spending Category:
Autism; Behavioral and Social Science; Brain Disorders; Clinical Research; Intellectual and Developmental Disabilities (IDD); Mental Health; Pediatric; Prevention

Project Terms:
Age; Age-Months; Alberta province; Archives; Area; Attention; autism spectrum disorder; Autistic Disorder; base; Beds; Behavior; Behavior assessment; Behavioral; Brain; Brain imaging; Classification; Clinic; Clinical; Code; commercialization; Computer Vision Systems; Computer-Assisted Image Analysis; computerized data processing; cost effective; Data; Data Set; design; Detection; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Early Diagnosis; Early identification; Early treatment; Face; Goals; Head; high risk infant; Human; human subject; improved; Individual; infancy; Infant; interest; Learning; Life; Measures; Methodology; Methods; Motor; Movement; neurobehavioral; novel; Outcome; Output; pediatrician; Phase; Physiology; Population Study; Process; prototype; public health relevance; Reaction Time; repository; Research; Resources; Risk; Risk Marker; rural area; Savings; screening; Sensitivity and Specificity; Services; sharing data; Siblings; social communication; Source; standard of care; Standardization; Symptoms; System; Techniques; Testing; Time; tool; Training; Universities; Washington; Work

Phase II

Contract Number: 5R43MH107063-02
Start Date: 8/20/2015    Completed: 5/31/2017
Phase II year
2016
(last award dollars: 2021)
Phase II Amount
$2,206,946

Early detection of autism leads to earlier treatment, which is proven to have a major impact on outcomes. In spite of recent advances in early Autism Spectrum Disorders (ASD) detection, the average age of diagnosis in the US is still around five. ASD diagnosis is currently performed via behavioral assessment, which requires highly specialized training, is not widely available in rural areas, and may be applied inconsistently. The need for specialized training for the administration of behavioral assessment and the effort involved in individual assessments preclude large scale deployment of these diagnostic methods in clinics and pediatricians' offices as well as large scale population studies. The Infant Brain Imaging Study (IBIS) is an early detection study at the University of Washington Autism Center which assesses behavioral and brain development in infants at high familial risk for ASD. Behavioral assessments include specialized observations of gross motor function, an area of development that is uniquely highlighted in the first year of life. This study along with others, highlight atypical motor development as the first step in the emergence of autism-related symptoms. Analyzing behavioral video data in order to assess/score individual subjects is a process that is time-intensive, subjective, and requires extensive training to attain reliability. We will build a Human Action Recognition Engine (HARE) that leverages computer vision tools to automatically extract, quantify and classify known motor actions - from video datasets - adding a significantly more efficient and standardized method to augment the current diagnostic standard of care. In this Phase I proposal, we will: 1. Develop the HARE prototype: automatic segmentation of subject of interest; determination of 3D orientation; extraction of features that are used in classification of actions from a predefined set defined in the IBIS behavioral assessment battery; 2. Leverage the intermediate outputs of the AR engine in establishing techniques to detect and de-identify faces of multiple, closely-interacting human subjects in video toward further processing and data sharing; 3. Explore early markers to classify subjects, based on actions detected, into ASD and non-ASD groups and evaluate the sensitivity and specificity of the classification engine. This Phase I effort will pave the way forthe creation of an action-annotated video repository from HARE's action recognition output. The repository will provide a rich source of highly-accessible data toward training and further research discoveries. Finally, the HARE system can systematically identify new, previously unidentified motor actions that may relate to increased risk for later developmental difficulties, particularly ASD. These novel early risk markers - in combination with existing assessments - would allow reliable, earlier identification of ASD.

Public Health Relevance Statement:


Public Health Relevance:
We propose to build an action-annotated video repository that will aid objective behavioral assessment of infants at risk for Autism Spectrum Disorders (ASD) and serve as a valuable training tool. We offer a methodology that can systematically reveal salient early motor markers that can result in efficacious screening of high risk infants.

NIH Spending Category:
Autism; Behavioral and Social Science; Brain Disorders; Clinical Research; Intellectual and Developmental Disabilities (IDD); Mental Health; Pediatric; Prevention

Project Terms:
Age; Age-Months; Alberta province; Archives; Area; Attention; autism spectrum disorder; Autistic Disorder; base; Beds; Behavior assessment; Behavioral; Brain; Brain imaging; Classification; Clinic; Clinical; Code; commercialization; Computer Vision Systems; Computer-Assisted Image Analysis; computerized data processing; cost effective; Data; Data Set; design; Detection; Development; Diagnosis; Diagnostic; Diagnostic Procedure; early detection biomarkers; Early Diagnosis; Early identification; Early treatment; Face; Goals; Head; high risk infant; Human; human subject; improved; Individual; infancy; Infant; interest; Learning; Life; Measures; Methodology; Methods; Motor; Movement; neurobehavioral; novel; Outcome; Output; pediatrician; Phase; Physiology; Population; Process; prototype; public health relevance; Reaction Time; repetitive behavior; repository; Research; Resources; Risk; Risk Marker; rural area; Savings; screening; Sensitivity and Specificity; Services; sharing data; Siblings; social communication; Source; standard of care; Standardization; Symptoms; System; Techniques; Testing; Time; tool; Training; Universities; Washington; Work