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Grant: Alliance Grants

Université d'Ottawa

$99,990.00

Artificial Intelligence Based Movement Analysis within Hospital Hallways

FY 2024-25

Summary
The Smart Hallway project was initiated to solve an ongoing issue in clinical and elderly-care facilities, namely the need to measure how people move where time, facility, and human resources barriers prevent daily movement analysis. Institutional hallways are often used for visual evaluation of patients or to perform walking tests. Ideally, movement measurement and assessment can occur in hallways near patient care areas as part of daily clinical practice, use unobtrusive equipment, not require patient preparation that takes time and may influence movement, have automated analysis, and provide immediate outcome reporting to the clinician. This project will develop software for assessing features related to a patient's clinical status. Markerless and contactless technologies will automatically digitize the person's movements as they walk through an institutional hallway. With an improved multi-camera approach, we will generate 3D data, perform movement calculations, and generate a report, all with minimal or no human intervention (i.e., the system is not a drain on staff time and does not require specialized staff). This information then becomes the gateway for Artificial Intelligence (AI) models to enhance knowledge extracted from this data; for example, providing indicators for fall risk, dementia progression, prosthesis alignment issues, etc. The new research on mobility assessment AI models was previously near-impossible to create and apply since the input human movement data could not be collected due to human resource and motion-lab-space requirements. This engineering research will enhance our understanding of how to best apply AI-models for movement related applications and provide better understanding of which data features are most relevant to clinically required outcomes. Successful research will enable a new range of outcome measurement opportunities in health and eldercare that are of global importance and could improve clinical decision-making for Canadians. A validated Smart Hallway system will revolutionize how quantitative movement assessment is integrated into regular healthcare workflows.
Program
Alliance Grants
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
581903-2023
Name nom
Lemaire, Edward ED
Department département
Medicine, Faculty of
Organization id
28
Institution établissement
Université d'Ottawa
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2023
Award amount
99990
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1102
Area of application group en
Health, education and social services
Area of application group fr
Santé publique, éducation et services sociaux
Area of application en
Biomedical engineering
Area of application fr
Génie biomédical
Research subject code
1900
Research subject group en
Biomedical engineering
Research subject group fr
Génie biomédical
Research subject en
Biomedical engineering
Research subject fr
Génie biomédical
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Artificial Intelligence Based Movement Analysis within Hospital Hallways
Keywords
artificial intelligence; biomechanics; markerless; mobility; movement analysis; physical rehabilitation; smart hallway
Application summary
The Smart Hallway project was initiated to solve an ongoing issue in clinical and elderly-care facilities, namely the need to measure how people move where time, facility, and human resources barriers prevent daily movement analysis. Institutional hallways are often used for visual evaluation of patients or to perform walking tests. Ideally, movement measurement and assessment can occur in hallways near patient care areas as part of daily clinical practice, use unobtrusive equipment, not require patient preparation that takes time and may influence movement, have automated analysis, and provide immediate outcome reporting to the clinician. This project will develop software for assessing features related to a patient's clinical status. Markerless and contactless technologies will automatically digitize the person's movements as they walk through an institutional hallway. With an improved multi-camera approach, we will generate 3D data, perform movement calculations, and generate a report, all with minimal or no human intervention (i.e., the system is not a drain on staff time and does not require specialized staff). This information then becomes the gateway for Artificial Intelligence (AI) models to enhance knowledge extracted from this data; for example, providing indicators for fall risk, dementia progression, prosthesis alignment issues, etc. The new research on mobility assessment AI models was previously near-impossible to create and apply since the input human movement data could not be collected due to human resource and motion-lab-space requirements. This engineering research will enhance our understanding of how to best apply AI-models for movement related applications and provide better understanding of which data features are most relevant to clinically required outcomes. Successful research will enable a new range of outcome measurement opportunities in health and eldercare that are of global importance and could improve clinical decision-making for Canadians. A validated Smart Hallway system will revolutionize how quantitative movement assessment is integrated into regular healthcare workflows.
Université d'Ottawa - $99,990 Grant (2024-25) | Canada Spends