Fall detection systems have primarily focused on elderly care, with limited attention given to student populations whose dynamic and high-intensity activities increase detection complexity. Movements such as running, jumping, and rapid posture transitions may generate motion patterns similar to fall events, making accurate discrimination challenging. This paper presents a methodological framework for fall detection using wearable Inertial Measurement Unit (IMU) sensors tailored to student environments. The proposed framework describes the complete system pipeline, including motion data acquisition from accelerometer and gyroscope signals, signal preprocessing, sliding-window segmentation, feature extraction, and classification strategies suitable for real-time wearable deployment. Emphasis is placed on computational efficiency and adaptability to highly active users. The study provides a structured design foundation for developing student-centered fall detection systems and supports future experimental validation and real-world implementation.