Emad-Ud-Din Muhammad, Hasan Mohammad H, Jafari Roozbeh, Pourkamali Siavash, Alsaleem Fadi
Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States.
Department of Earth and Space Sciences, Columbus State University, Columbus, OH, United States.
Front Digit Health. 2021 Sep 22;3:731076. doi: 10.3389/fdgth.2021.731076. eCollection 2021.
This paper presents an energy-efficient classification framework that performs human activity recognition (HAR). Typically, HAR classification tasks require a computational platform that includes a processor and memory along with sensors and their interfaces, all of which consume significant power. The presented framework employs microelectromechanical systems (MEMS) based Continuous Time Recurrent Neural Network (CTRNN) to perform HAR tasks very efficiently. In a real physical implementation, we show that the MEMS-CTRNN nodes can perform computing while consuming power on a nano-watts scale compared to the micro-watts state-of-the-art hardware. We also confirm that this huge power reduction doesn't come at the expense of reduced performance by evaluating its accuracy to classify the highly cited human activity recognition dataset (HAPT). Our simulation results show that the HAR framework that consists of a training module, and a network of MEMS-based CTRNN nodes, provides HAR classification accuracy for the HAPT that is comparable to traditional CTRNN and other Recurrent Neural Network (RNN) implantations. For example, we show that the MEMS-based CTRNN model average accuracy for the worst-case scenario of not using pre-processing techniques, such as quantization, to classify 5 different activities is 77.94% compared to 78.48% using the traditional CTRNN.
本文提出了一种用于执行人类活动识别（HAR）的节能分类框架。通常，HAR分类任务需要一个计算平台，该平台包括处理器、内存以及传感器及其接口，所有这些都会消耗大量电力。所提出的框架采用基于微机电系统（MEMS）的连续时间递归神经网络（CTRNN）来非常高效地执行HAR任务。在实际物理实现中，我们表明，与微瓦级的现有硬件相比，MEMS-CTRNN节点在以纳瓦级消耗功率的同时能够执行计算。我们还通过评估其对高引用的人类活动识别数据集（HAPT）进行分类的准确性，确认了这种大幅的功率降低并没有以性能降低为代价。我们的仿真结果表明，由训练模块和基于MEMS的CTRNN节点网络组成的HAR框架，为HAPT提供的HAR分类准确率与传统CTRNN和其他递归神经网络（RNN）实现相当。例如，我们表明，在不使用诸如量化等预处理技术来对5种不同活动进行分类的最坏情况下，基于MEMS的CTRNN模型平均准确率为77.94%，而使用传统CTRNN时为78.48%。
