<![CDATA[Wearable Internet of Things systems increasingly depend on inertial measurement units (IMUs) to capture human motion, yet continuous high-frequency sensing, on-device processing, and long-range (LoRa) communication impose significant energy and latency challenges for battery-powered devices. This study formulates a practical scheduling framework that optimizes IMU sampling, quaternion-based feature extraction, and transmission decisions within the wearable/LoRa architecture. The framework operates in discrete time windows of W=0.5−1 s, within which sensing, processing, and communication decisions are updated at the window level to balance energy consumption and responsiveness. The method models energy consumption, accuracy degradation at lower sampling rates, and communication constraints to define feasible operating modes and determine optimal configurations under varying activity levels. An empirical accuracy–frequency mapping and component-wise energy model support both offline optimization and lightweight online scheduling. The results show that the proposed framework can balance accuracy, responsiveness, and battery life by dynamically shifting between high-performance, balanced, and low-power surveillance states. This scheduling strategy extends operational lifetime while preserving motion-detection reliability and ensuring timely event transmission. The findings demonstrate the importance of energy-aware IMU management in long-range wearable systems and provide a foundation for adaptive sensing strategies in real-world deployments.]]>
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