In recent years, the use of wearable accelerometers for accurate and continuous assessment of physical activity has become widespread. Wearable accelerometers offer a precise and objective assessment to determine different types of movement such as walking, standing, sitting, leisure time movements and sleeping.  With this project, the daily movement patterns of elderly individuals will be objectively determined and the effects of physical activity and sedentary behavior on motor control, balance mechanisms and general health will be evaluated more comprehensively. In order to monitor the 24-hour movement behavior of the participants, data will be recorded with an AX3 accelerometer that the participants will wear on their wrists for seven consecutive days without interruption. With the algorithms to be developed for movement analysis and sleep analysis, features will be obtained and these features will then be classified as sedentary, light, moderate, intense activity according to certain thresholds with machine learning models. In this way, more reliable data on the physical activity and sedentary behavior of the elderly will be obtained and evidence will be provided for future studies.