Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:6978-6981. doi: 10.1109/EMBC46164.2021.9630154.
In the era of Internet of Things (IoT), an increasing amount of sensors is being integrated into intelligent wearable devices. These sensors have the potential to produce a large quantity of physiological data streams to be analyzed in order to produce meaningful and actionable information. An important part of this processing is usually located in the device itself and takes the form of embedded algorithms which are executed into the onboard microcontroller (MCU). As data processing algorithms have become more complex due to, in part, the disruption of machine learning, they are taking an increasing part of MCU time becoming one of the main driving factors in the energy budget of the overall embedded system. We propose to integrate such algorithms into dedicated low-power circuits making the power consumption of the processing part negligible to the overall system. We provide the results of several implementations of a pre-trained physical activity classifier used in smartwatches and wristbands. The algorithm combines signal processing for feature extraction and machine learning in the form of decision trees for physical activity classification. We show how an in-silicon implementation decreases up to 0.1 µW the power consumption compared to 73 µW on a general-purpose ARM's Cortex-M0 MCU.
在物联网（IoT）时代，越来越多的传感器被集成到智能可穿戴设备中。这些传感器有可能产生大量的生理数据流，需要进行分析，以生成有意义且可操作的信息。这一处理过程的一个重要部分通常位于设备本身，并采用嵌入式算法的形式在板载微控制器（MCU）上执行。由于机器学习的干扰，数据处理算法变得越来越复杂，因此它们占用了越来越多的 MCU 时间，成为整个嵌入式系统能源预算的主要驱动因素之一。我们建议将这些算法集成到专用的低功耗电路中，从而使处理部分的功耗对整个系统来说可以忽略不计。我们提供了在智能手表和腕带中使用的预训练活动分类器的几个实现结果。该算法结合了信号处理，用于特征提取和机器学习，采用决策树的形式进行活动分类。我们展示了在硅片上实现如何与通用的 ARM Cortex-M0 MCU 相比，将功耗降低了 0.1µW（至 73µW）。
