动态环境中受限设备上的边缘AI二值睡眠-清醒分类
Edge AI on Constrained Devices for Binary Sleep-Wake Classification in Dynamic Environments
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- Chemnitz University of Technology（开姆尼茨工业大学）
- University of Hagen（哈根大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
本文提出一种在ESP32-S3上运行的边缘AI多模态系统，结合惯性传感与视觉姿态分类，实现动态环境中二值睡眠-清醒检测，准确率达96.5%和89%，验证了隐私保护的本地睡眠监测可行性。
中文摘要 AI 辅助
本文提出了一种基于边缘AI的系统，用于在非平稳移动环境中利用资源受限的嵌入式硬件检测睡眠和清醒状态。依赖基于加速度计的活动度量的传统方法极易受到运动和振动伪影的影响，并且受限于可穿戴和物联网设备的严格计算和能源预算。为了解决这些挑战，设计并实现了一个多模态流水线，部署在ESP32-S3微控制器上。该系统结合了用于头部运动分析的惯性传感和视觉姿态分类。采用FreeRTOS的双核架构实现了实时数据采集和设备上推理的并行执行。睡眠检测遵循两阶段策略：首先在时间窗口内进行低运动检测，随后进行姿态的视觉验证。实验结果显示，基于运动的检测准确率为96.5%，姿态分类准确率为89%，从而实现了稳健的二值睡眠-清醒分类。现场测试证实了在代表性移动场景中的可行性。结果表明，通过仔细的协同设计，在边缘硬件上实现保护隐私的本地睡眠检测是可行的，同时也揭示了在传感侵入性、数据集规模和系统集成方面的局限性。
英文摘要
This paper presents an Edge AI-based system for detecting sleep and wake states in non-stationary mobile environments using resource-constrained embedded hardware. Conventional approaches relying on accelerometer-based activity metrics are highly susceptible to motion and vibration artifacts and are limited by strict compute and energy budgets of wearable and IoT devices. To address these challenges, a multimodal pipeline is designed and implemented on an ESP32-S3 microcontroller. The system combines inertial sensing for head movement analysis and visual pose classification. A dual-core architecture with FreeRTOS enables parallel execution of real-time data acquisition and on-device inference. Sleep detection follows a two-stage strategy: low-movement detection over a temporal window, followed by visual validation of poses. Experimental results show accuracies of 96.5% for motion-based detection and 89% for pose classification, yielding robust binary sleep-wake classification. Field tests confirmed feasibility in representative mobile scenarios. The results demonstrate that privacy-preserving, local sleep detection is achievable on edge hardware through careful co-design, while highlighting limitations in sensing intrusiveness, dataset scale, and system integration.
