Findings seen in pilot study that combines measurements from a depth camera, a forceplate, and an interface board
By Lori Solomon HealthDay Reporter
TUESDAY, March 18, 2025 (HealthDay News) — The combination of machine learning and a portable system can effectively measure multiple aspects of motor function to identify mild cognitive impairment (MCI) in older adults, according to a pilot study published online in the October-December 2024 issue of Alzheimer Disease & Associated Disorders.
Jamie B. Hall, P.T., D.P.T., Ph.D., from the University of Missouri in Columbia, and colleagues examined the feasibility of building a machine learning model to identify individuals with MCI using motor function data obtained from an inexpensive, portable device. The analysis included 28 healthy older adults and 19 older adults with MCI with assessments from a portable machine combining a depth camera, a forceplate, and an interface board.
The researchers reported that three machine learning models (support vector machine, decision trees, and logistic regression) were trained and tested with the goal of classification of MCI based on static balance, gait, and sit-to-stand activities in both single- and dual-task conditions. The best model was decision trees, which demonstrated an accuracy of 83 percent, a sensitivity of 0.83, a specificity of 1.00, and an F1 score of 0.83.
“This study demonstrates the feasibility of building a machine learning model capable of identifying individuals with mild cognitive impairment using motor function data obtained with a portable, inexpensive, multimodal device,” the authors write.
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