Background/Objectives: Sarcopenia and cognitive decline (CD) are prevalent in aging populations, impacting functionality and quality of life. The early detection of these diseases is challenging, often relying on in-person screening, which is difficult to implement regularly. This study aims to develop artificial intelligence algorithms based on gait analysis, integrating sensor and computer vision (CV) data, to detect sarcopenia and CD. Methods: A cross-sectional case-control study was conducted involving 42 individuals aged 60 years or older. Participants were classified as having sarcopenia if they met the criteria established by the European Working Group on Sarcopenia in Older People and as having CD if their score in the Mini-Mental State Examination was ≤24 points. Gait patterns were assessed at usual walking speeds using sensors attached to the feet and lumbar region, and CV data were captured using a camera. Several key variables related to gait dynamics were extracted. Finally, machine learning models were developed using these variables to predict sarcopenia and CD. Results: Models based on sensor data, CV data, and a combination of both technologies achieved high predictive accuracy, particularly for CD. The best model for CD achieved an F1-score of 0.914, with a 95% sensitivity and 92% specificity. The combined technologies model for sarcopenia also demonstrated high performance, yielding an F1-score of 0.748 with a 100% sensitivity and 83% specificity. Conclusions: The study demonstrates that gait analysis through sensor and CV fusion can effectively screen for sarcopenia and CD. The multimodal approach enhances model accuracy, potentially supporting early disease detection and intervention in home settings.
