Investigating balance compensation strategies using wearable sensors in the elderly

Abstract

The aim of this study is to investigate balance compensation strategies in the elderly using wearable sensors and analyze their applications in movement monitoring, fall prediction, and providing smart interventions. This study is a narrative review with descriptive analysis method and was conducted using scientific sources published between 2020 and 2025. The articles were searched in PubMed, Scopus, Google Scholar, and IEEE Xplore databases. The selected articles were selected and analyzed based on their relevance to the topic, target population (elderly), type of wearable technology, and their application goals. The findings showed that wearable sensors are effective tools in biomechanical assessment of body movements, accurate measurement of gait parameters, and identification of movement abnormalities. These sensors have the ability to predict fall risk through machine learning algorithms and can be used in feedback interventions for rapid correction of body posture. Augmented and virtual reality were also introduced as new educational platforms for balance training in combination with sensor data. The use of wearable sensors in the field of balance rehabilitation for the elderly is a new and multifaceted approach that, by providing accurate data, personalized interventions, and the possibility of monitoring in the natural living environment, can play an effective role in preventing falls and improving the quality of life of the elderly.