Comprehensive assessments of motor function are crucial for effective decision-making in neuromuscular rehabilitation. This is particularly critical post-stroke, where personalized therapy drives an individual’s abilities to be ambulatory and return to their community in the short term and effectively use their upper limbs in the long term. Current clinical practice, however, relies on self-reporting, subjective observation, or basic scales, which are limited by inconsistency, low sensitivity, ceiling effects, and continuous reliance on clinical personnel. Also, such assessments are typically conducted in larger clinical centres, posing accessibility challenges for many living in rural areas or long-term care homes.
We propose MotionInsight, a cost-effective, accessible, and user-friendly smartphone-based technology for objectively assessing motor function at point-of-need. Using artificial intelligence (AI), MotionInsight can objectively drive the personalized rehabilitation of gross/fine motor function of the upper arm/hand as well as balance in acute, inpatient, outpatient, community-based, and at-home settings.
MotionInsight encompasses an app that guides users through personalized assessments or training tasks and computer vision techniques to quantitatively track body segments. It also includes a secure database and website for data management and progress tracking. Using the latter, clinicians can prescribe new assessment and training tasks or kinematic metrics; and stroke survivors can track their progress.
MotionInsight addresses an unmet clinical need by proposing an adoptable solution that only relies on an end-user smartphone. MotionInsight strengthens Canada’s digital health ecosystem by accelerating innovations in digitally-informed and evidence-based clinical decision-making for care personalization post-stroke.
Presented by:
Milad Nazarahari, Assistant Professor, Department of Mechanical Engineering, University of Alberta
Milad Nazarahari began his tenure-track position as an Assistant Professor in the Department of Mechanical Engineering at the University of Alberta in January 2023. He is internationally recognized for his contributions to human movement analysis with wearable technologies. His current research is focused on developing cost-effective and efficient robotic systems to deliver assessment and intervention with minimum supervision and markerless motion capture systems for human movement analysis for out-of-the-lab applications. His research has been supported by NSERC, CFI, Alberta Innovates, and Google Inc. He is a Research Affiliate with the Glenrose Rehabilitation Hospital in Edmonton and the Neuroscience and Mental Health Institute at the University of Alberta. Before his appointment at the University of Alberta, he was an AMTD Waterloo Global Talent Postdoctoral Fellow for almost a year and a half, investigating computational methods for human-in-the-loop training personalization. He received his Ph.D. in August 2021 from the Department of Mechanical Engineering at the University of Alberta.
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