Slapničar Gašper, Wang Wenjin, Luštrek Mitja
Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia.
Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia.
Sensors (Basel). 2021 Mar 6;21(5):1836. doi: 10.3390/s21051836.
Contact-free sensors offer important advantages compared to traditional wearables. Radio-frequency sensors (e.g., radars) offer the means to monitor cardiorespiratory activity of people without compromising their privacy, however, only limited information can be obtained via movement, traditionally related to heart or breathing rate. We investigated whether five complex hemodynamics scenarios (resting, apnea simulation, Valsalva maneuver, tilt up and tilt down on a tilt table) can be classified directly from publicly available contact and radar input signals in an end-to-end deep learning approach. A series of robust k-fold cross-validation evaluation experiments were conducted in which neural network architectures and hyperparameters were optimized, and different data input modalities (contact, radar and fusion) and data types (time and frequency domain) were investigated. We achieved reasonably high accuracies of 88% for contact, 83% for radar and 88% for fusion of modalities. These results are valuable in showing large potential of radar sensing even for more complex scenarios going beyond just heart and breathing rate. Such contact-free sensing can be valuable for fast privacy-preserving hospital screenings and for cases where traditional werables are impossible to use.
与传统可穿戴设备相比，非接触式传感器具有重要优势。射频传感器（如雷达）提供了一种在不侵犯人们隐私的情况下监测其心肺活动的方法，然而，通过传统上与心率或呼吸率相关的运动只能获得有限的信息。我们研究了能否通过端到端深度学习方法，直接从公开可用的接触式和雷达输入信号中对五种复杂的血液动力学情况（静息、模拟呼吸暂停、瓦尔萨尔瓦动作、在倾斜台上向上倾斜和向下倾斜）进行分类。进行了一系列稳健的k折交叉验证评估实验，其中对神经网络架构和超参数进行了优化，并研究了不同的数据输入模式（接触式、雷达式和融合式）以及数据类型（时域和频域）。我们在接触式、雷达式和模态融合方面分别取得了88%、83%和88%的较高准确率。这些结果很有价值，表明即使对于超越心率和呼吸率的更复杂情况，雷达传感也具有很大潜力。这种非接触式传感对于快速保护隐私的医院筛查以及传统可穿戴设备无法使用的情况可能很有价值。
