基于BLE的无源人体感知的深度学习改进方法
Improving BLE-Based Passive Human Sensing with Deep Learning
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中文总结 AI 辅助
本研究提出用深度卷积神经网络改进基于商用BLE设备的无源人体感知，仅少量收发器即可在非视距场景下检测人体，性能优于现有技术。
中文摘要 AI 辅助
无源人体感知（PHS）是一种无需被感知人员携带设备或主动参与感知过程，即可收集其存在、运动或活动数据的技术。现有研究中，PHS通常利用专用WiFi的信道状态信息变化实现，该变化由人体遮挡WiFi信号传播路径导致。但WiFi用于PHS存在功耗高、大规模部署成本高、易干扰周边其他网络等缺陷。蓝牙技术，尤其是其低功耗版本蓝牙低能耗（BLE），凭借自适应跳频（AFH）机制，可有效解决WiFi的上述缺陷。本研究提出应用深度卷积神经网络（DNN），改进基于商用标准BLE设备的PHS中BLE信号变形的分析与分类。该方法仅需少量收发器，即可在大型多隔间房间中可靠检测人体存在，且适用于人体未直接遮挡收发器间视距（LOS）的场景。实验表明，在相同实验数据下，该方法的性能显著优于现有文献中最准确的技术。
英文摘要
Passive Human Sensing (PHS) is an approach to collecting data on human presence, motion or activities that does not require the sensed human to carry devices or participate actively in the sensing process. In the literature, PHS is generally performed by exploiting the Channel State Information variations of dedicated WiFi, affected by human bodies obstructing the WiFi signal propagation path. However, the adoption of WiFi for PHS has some drawbacks, related to power consumption, large-scale deployment costs and interference with other networks in nearby areas. Bluetooth technology and, in particular, its low-energy version Bluetooth Low Energy (BLE), represents a valid candidate solution to the drawbacks of WiFi, thanks to its Adaptive Frequency Hopping (AFH) mechanism. This work proposes the application of a Deep Convolutional Neural Network (DNN) to improve the analysis and classification of the BLE signal deformations for PHS using commercial standard BLE devices. The proposed approach was applied to reliably detect the presence of human occupants in a large and articulated room with only a few transmitters and receivers and in conditions where the occupants do not directly occlude the Line of Sight between transmitters and receivers. This paper shows that the proposed approach significantly outperforms the most accurate technique found in the literature when applied to the same experimental data.
