ArticleSensors (Basel, Switzerland)2026
Accelerator-Free Thermal Pose Estimation: Accuracy-Efficiency Trade-Offs on the Raspberry Pi 5.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Deploying human pose estimation on low-cost, accelerator-free hardware remains a key open question for smart home activity recognition, where affordability and wide availability often matter as much as raw inference speed. This work addresses that question by evaluating thermal-image pose estimation on a Raspberry Pi 5, a CPU-only single-board computer with no dedicated neural-network accelerator. Building on a previously collected single-person thermal dataset of 2500 images annotated with 17 body keypoints, YOLOv8-pose, YOLO11-pose, and YOLO26-pose models were trained across five model scales (
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