Shen Zichao, Nunez-Yanez Jose, Dahnoun Naim
School of Electrical, Electronic and Mechanical Engineering, University of Bristol, Bristol BS8 1UB, UK.
Department of Electrical Engineering, Linköping University, 581 83 Linköping, Sweden.
Sensors (Basel). 2024 Jun 5;24(11):3660. doi: 10.3390/s24113660.
This study explored an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments. Compared to wearables and camera methods, Millimeter-Wave radar is not plagued by mobility inconveniences, lighting conditions, or privacy issues. We conducted an initial evaluation of radar characteristics, covering aspects such as interference between radars and coverage area. Then, we established a real-time framework to integrate signals received from these radars, allowing us to track the position and body status of human targets non-intrusively. Additionally, we introduced innovative strategies, including dynamic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering based on signal SNR levels, a probability matrix for enhanced target tracking, target status prediction for fall detection, and a feedback loop for noise reduction. We conducted an extensive evaluation using over 300 min of data, which equated to approximately 360,000 frames. Our prototype system exhibited a remarkable performance, achieving a precision of 98.9% for tracking a single target and 96.5% and 94.0% for tracking two and three targets in human-tracking scenarios, respectively. Moreover, in the field of human fall detection, the system demonstrates a high accuracy rate of 96.3%, underscoring its effectiveness in distinguishing falls from other statuses.
本研究探索了一种利用德州仪器的三款毫米波雷达来跟踪多个人并检测跌倒的室内系统。与可穿戴设备和摄像头方法相比，毫米波雷达不受行动不便、光照条件或隐私问题的困扰。我们对雷达特性进行了初步评估，涵盖了雷达之间的干扰和覆盖区域等方面。然后，我们建立了一个实时框架来整合从这些雷达接收到的信号，从而能够以非侵入方式跟踪人体目标的位置和身体状态。此外，我们引入了创新策略，包括基于信号信噪比水平的动态基于密度的带噪声空间聚类（DBSCAN）聚类、用于增强目标跟踪的概率矩阵、用于跌倒检测的目标状态预测以及用于降噪的反馈回路。我们使用超过300分钟的数据（约相当于360,000帧）进行了广泛评估。我们的原型系统表现出卓越的性能，在人体跟踪场景中，跟踪单个目标的精度达到98.9%，跟踪两个和三个目标的精度分别为96.5%和94.0%。此外，在人体跌倒检测领域，该系统的准确率高达96.3%，凸显了其在区分跌倒与其他状态方面的有效性。
