Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models
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发表时间：
2019-01
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通讯作者：
Randy Ardywibowo;Guang Zhao;Zhangyang Wang;B. Mortazavi;Shuai Huang;Xiaoning Qian
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作者：
Randy Ardywibowo;Guang Zhao;Zhangyang Wang;B. Mortazavi;Shuai Huang;Xiaoning Qian
新兴的可穿戴传感器已经实现了前所未有的能力，可以持续监测人类活动以实现医疗保健目的。然而，由于有如此多的环境传感器收集不同的测量值，因此不仅要保持良好的监测精度，而且要保持低功耗以确保可持续监测，这一点变得非常重要。这种节能感测方案可以通过决定在给定时间使用哪组传感器来实现，需要准确表征传感器能量使用与监控时忽略某些传感器信号的不确定性之间的权衡。为了在活动监控的背景下解决这一挑战，我们设计了一个自适应活动监控框架。我们首先提出了一个开关高斯过程来模拟所观察到的传感器信号发射的底层活动状态。为了有效地计算高斯过程模型的似然性和量化上下文预测的不确定性，我们提出了一种块循环嵌入技术，并使用快速傅立叶变换（FFT）进行推理。通过计算切换高斯过程的贝叶斯损失函数，自适应监测程序的开发，以选择功能，从可用的传感器，优化传感器功耗和状态预测熵量化的预测性能之间的权衡。我们证明了我们的框架上流行的基准UCI人类活动识别使用智能手机的有效性。
Emerging wearable sensors have enabled the unprecedented ability to continuously monitor human activities for healthcare purposes. However, with so many ambient sensors collecting different measurements, it becomes important not only to maintain good monitoring accuracy, but also low power consumption to ensure sustainable monitoring. This power-efficient sensing scheme can be achieved by deciding which group of sensors to use at a given time, requiring an accurate characterization of the trade-off between sensor energy usage and the uncertainty in ignoring certain sensor signals while monitor- ing. To address this challenge in the context of activity monitoring, we have designed an adaptive activity monitoring framework. We first propose a switching Gaussian process to model the observed sensor signals emitting from the underlying activity states. To efficiently compute the Gaussian process model likelihood and quantify the context prediction uncertainty, we propose a block circulant embedding technique and use Fast Fourier Transforms (FFT) for inference. By computing the Bayesian loss function tailored to switching Gaussian processes, an adaptive monitoring procedure is developed to select features from available sensors that optimize the trade-off between sensor power consumption and the prediction performance quantified by state prediction entropy. We demonstrate the effectiveness of our framework on the popular benchmark of UCI Human Activity Recognition using Smartphones.