AI-Driven Computer Vision for Fall Detection and Gait Assessment in Older Adults

In this CME, Steven M. Handler, MD, PhD, CMD discusses how to quantify the burden of falls in older adults and explain why current primary care approaches leave most at-risk patients unidentified, explain why gait speed is considered a "sixth vital sign" and describe its associations with falls, functional decline, cognitive impairment, and mortality, compare gait assessment methods (including stopwatch-based 4MWT, instrumented walkways, wearable sensors, and AI-based computer vision) on accuracy, feasibility, and primary care suitability, describe how AI-based computer vision systems using LiDAR technology measure gait parameters and articulate their advantages and limitations relative to conventional tools, and apply an evidence-based framework for coupling abnormal gait findings with targeted physical therapy or community exercise referrals for older adults at risk for falls.

Educational Objectives 

Upon completion of this activity, participants should be able to:

• Quantify the burden of falls in older adults and explain why current primary care approaches leave most at-risk patients unidentified.
• Explain why gait speed is considered a "sixth vital sign" and describe its associations with falls, functional decline, cognitive impairment, and mortality.
• Compare gait assessment methods (including stopwatch-based 4MWT, instrumented walkways, wearable sensors, and AI-based computer vision) on accuracy, feasibility, and primary care suitability.
• Describe how AI-based computer vision systems using LiDAR technology measure gait parameters and articulate their advantages and limitations relative to conventional tools.
• Apply an evidence-based framework for coupling abnormal gait findings with targeted physical therapy or community exercise referrals for older adults at risk for falls.

Disclosures:

All individuals in a position to control the content of this education activity have disclosed all financial relationships with any companies whose primary business is producing, marketing, selling, re-selling, or distributing healthcare products used by or on patients. All of the relevant financial relationships for the individuals listed below have been mitigated. 

Dr. Steven M. Handler receives; 

• Consultant: VerusIQ

Accreditation Statement

In support of improving patient care, the University of Pittsburgh is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.

The University of Pittsburgh designates enduring material activity for a maximum of 0.5 AMA PRA Category 1 Credit[s]™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Other health care professionals will receive a certificate of attendance confirming the number of contact hours commensurate with the extent of participation in this activity.

For your credit transcript, please access our website 4 weeks post-completion at http://ccehs.upmc.com and follow the link to the Credit Transcript page. If you do not provide the last 5 digits of your SSN on the next page you will not be able to access a CME credit transcript. Providing your SSN is voluntary.

Release Date: 5/15/2026 | Last Modified On: 5/15/2026 | Expires: 5/15/2027