A Review of Balance Compensation Strategies Using Wearable Sensors in the Elderly
Keywords:
Elderly, wearable sensors, balance, fall, machine learning, sensory feedback, augmented realityAbstract
This study aims to examine balance compensation strategies in the elderly using wearable sensors and explore their application in motion monitoring, fall prediction, and intelligent interventions. This research is a narrative review using a descriptive analysis method. Articles published between 2020 and 2025 were retrieved from PubMed, Scopus, Google Scholar, and IEEE Xplore. Eligible studies were selected based on relevance to elderly populations, wearable sensor technologies, and their practical applications in balance-related interventions. The results indicate that wearable sensors are effective tools for biomechanical assessment, precise measurement of gait parameters, and detection of abnormal movement patterns. These devices have the potential to predict fall risks through machine learning algorithms and are applied in real-time feedback systems for posture correction. Augmented and virtual reality were also identified as innovative training platforms that can be integrated with sensor data for balance rehabilitation. The application of wearable sensors in elderly balance rehabilitation represents a novel and multidimensional approach that enables personalized interventions, accurate data collection, and real-life monitoring. This technology holds significant promise in fall prevention and enhancing the quality of life for older adults.
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