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Energy-efficient Human Activity Recognition for Self-powered Wearable Devices

机译:自供电可穿戴设备的节能人类活动识别

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摘要

Advances in energy harvesting hardware have created an opportunity to realise self-powered wearables for continuous and pervasive Human Activity Recognition (HAR). Unfortunately, the power requirements of continuous activity sensing using accelerometer sensors and burdensome on-node classification are relatively high compared to the amount of power that can be practically harvested, which limits the usefulness of energy harvesting. This thesis makes three fundamental contributions. First, we propose HARKE, Human Activity Recognition from Kinetic Energy, a novel approach to HAR that does not use an accelerometer. Instead, HARKE employs and infers human physical activities directly from the Kinetic Energy Harvesting (KEH) patterns generated from a device that harvests kinetic energy to power the wearable device. We also show the ability of HARKE to detect related details such as the steps taken by the user in a walking activity. By not using an accelerometer, a significant percentage of the limited harvested energy can be saved. Second, we introduce a novel framework that reduces the on-node classification overhead and guarantees energy neutrality. The proposed framework transmits an unmodulated signal, called an activity pulse, and uses only the received signal strength of the activity pulse to classify human activities. Neither accelerometer nor classifier is required on the wearable device, which therefore, guarantees energy neutrality. Finally, we validate the feasibility of using KEH patterns generated from human speech as a potential new source of information for detecting hotwords, such as ``OK Google", which are used by voice control applications to differentiate user commands from background conversations. Unlike methods that use existing sensors like microphones or accelerometers, our proposal enables pervasive voice control and HAR at a minimum energy cost.We believe that the findings in this thesis will open the door for a new direction of research and development to realise the vision of pervasive self-powered HAR, moving us closer towards self-powered autonomous wearables.
机译:能量收集硬件的进步创造了实现自我供电的可穿戴设备的机会,从而实现了持续不断的人类活动识别(HAR)。不幸的是,与可以实际收集的电量相比,使用加速度传感器和连续繁琐的节点分类进行连续活动感测的电力需求相对较高,这限制了能量收集的实用性。本文提出了三个基本的贡献。首先,我们提出HARKE,一种来自动能的人类活动识别,这是一种不使用加速计的新型HAR方法。取而代之的是,HARKE直接从设备产生的动能收集(KEH)模式中利用和推断人类的身体活动,该设备收集动能来为可穿戴设备供电。我们还展示了HARKE能够检测相关细节的能力,例如用户在步行活动中采取的步骤。通过不使用加速度计,可以节省相当一部分有限的采集能量。其次,我们引入了一种新颖的框架,可减少节点上的分类开销并确保能量中立。所提出的框架发送称为活动脉冲的未调制信号,并且仅使用活动脉冲的接收信号强度对人类活动进行分类。可穿戴设备上既不需要加速度计也不需要分类器,因此保证了能量中立。最后,我们验证了使用人类语音生成的KEH模式作为检测热门单词(如“ OK Google”)的潜在新信息源的可行性,语音控制应用程序使用这些KEH模式将用户命令与后台对话区分开来。使用现有的传感器(如麦克风或加速计),我们的建议可以以最低的能源成本实现普及的语音控制和HAR。我们相信,本文的发现将为实现普遍自我的愿景开辟新的研发方向动力的可穿戴设备,使我们更接近自供电的自主可穿戴设备。

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