Automated Movement Screen: Using Smartphone Videos to Objectively Appraise Low Back Motor Function

Low back pain (LBP) is frequently classified as non-specific, which reduces the capacity of the healthcare system to offer target rehabilitation. Previous work has suggested a link between motor control and low back dysfunction. Identifying motor control phenotypes indicative of dysfunction often requires complex and costly laboratory equipment. However, recent advancements in computer vision and the widespread availability of smartphones have made human motion capture more accessible. This study aimed to answer the question: Can consumer-grade video inputs be used to identify motor control phenotypes associated with low back dysfunction? To address this question, a self-guided online questionnaire was employed to gather data from 448 participants. The participants completed participant-reported outcome measures and video-recorded themselves performing four functional movements in either public settings or their homes. By applying principal component analysis, it was identified that significant PC scores can distinguish differences within functional movements between healthy individuals and those experiencing low back pain. Specifically, the results between low vs high function participants depict biomechanically relevant differences (i.e. movement speed and range of motion) typically found in the LBP population. The study’s findings highlight the feasibility of using consumer-grade technology for large-scale biomechanical data collection, to enhance our understanding of LBP and support the development of more personalized rehabilitation strategies. This approach has the potential to lead to the creation of an automated movement screening tool that is both accessible and effective in identifying low back dysfunction.

Name thesis:degree_name

M.Sc. Applied Health Sciences

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Faculty of Applied Health Sciences

Department dc:contributor.department

Applied Health Sciences Program

Grantor

Brock University

Year dc:date.issued

2024

Author dc:creator

• Alano, Carl

• Low Back Pain
• Computer Vision
• Data Science
• Principal Component Analysis
• Disability
• Kinesiophobia
• Pain Catastrophizing
• Physical Activity

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10464/18931

OAI identifier oai:identifier

oai:brocku.scholaris.ca:10464/18931