Objective: To examine the influence of age and neurological status on adaptive balance strategies using big data analytics and systematic review methodologies.

Background: Balance control is influenced by age-related and neurological changes, affecting motor adaptability and fall risk. While traditional studies provide insights into balance adaptation, big data techniques enable large-scale pattern recognition, enhancing our understanding of age- and disease-related balance strategies. A systematic review incorporating machine learning and big data methodologies is essential for identifying key trends and optimizing rehabilitation strategies.

Method: A systematic review of studies employing big data techniques to analyze adaptive balance strategies was conducted using PubMed, Web of Science, Scopus, and IEEE Xplore databases up to March 2024. Inclusion criteria encompassed studies assessing balance adaptation in aging and neurological conditions (e.g., Parkinson’s disease, stroke) using large datasets and computational models. Extracted data included sensor-based metrics, machine learning approaches, and clinical outcomes. Meta-analysis was conducted to evaluate the predictive accuracy of big data-driven models.

Results: A total of 32 studies (n = 1,450 participants) met inclusion criteria. Machine learning algorithms such as decision trees and neural networks classified adaptive balance responses with an accuracy of 89%. Age was significantly correlated with prolonged postural reaction times (p < 0.001), while neurological impairments further reduced compensatory stepping responses. Big data analysis revealed that older adults with neurological conditions exhibited a 43% increase in fall risk compared to healthy older adults. Wearable sensor data integrated with big data analytics improved fall risk prediction by 35%, highlighting the potential for real-time monitoring and intervention.

Conclusion: Big data analytics provides novel insights into the role of age and neurological status on balance adaptation, supporting precision rehabilitation strategies. Findings suggest that adaptive balance strategies deteriorate with age and neurological impairment, necessitating targeted interventions. Future research should integrate real-time big data processing to develop predictive models for personalized balance training and fall prevention programs.

« Back to 2025 International Congress

MDS Abstracts - https://www.mdsabstracts.org/abstract/investigating-the-role-of-age-and-neurological-status-on-adaptive-balance-strategies-a-systematic-review-utilizing-big-data-techniques/