The increasing elderly population has heightened the effectiveness, they are also accompanied by limitations. Wear - need for efficient and continuous fall detection systems. Camera - able devices can be uncomfortable, require regular charging or based monitoring solutions, while effective, pose signif - icant maintenance, and are often forgotten or deliberately not worn privacy concerns. This study addresses these concerns by by the elderly. Manual monitoring, o n the other hand, is labor - evaluating video anonymization techniques — stick figures and 3D avatars — that obscure identifiable visual features while preserv - intensive and prone to human error or delays in response. As an ing essential movement information for accurate fall detection. alternative, camera - based monitoring systems offer several Simulated scenarios conducted by volunteers wearing elderly advantages. They enable continuous, real - time observation and simulation suits fac ilitated realistic data collection. Analysis can facilitate rapid detection and response to fall events. More revealed the advantages and limitations of each anonymization advanced systems integrate artificial itelligence (AI) to detect technique, with 3D avatars preferred due to their superior contextual clarity and privacy balance, though stick figures abnormal movements or body postures automatically, signifi - presented notable benefits for resource - constrained environments. cantly reducing reliance on human supervision and improving The study highlights crucial insights into balancing privacy with the speed of emergency intervention. fall detection effectiveness, offering guidance for context - specific implementations. However, the adoption of camera - based monitoring is not without challenges. One of the most critical concerns relates to