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Distilling the Knowledge From Handcrafted Features for Human Activity Recognition

机译：从手工功能中提取知识以进行人类活动识别

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摘要

Human activity recognition is a core problem in intelligent automation systems due to its far-reaching applications including ubiquitous computing, health-care services, and smart living. Due to the nonintrusive property of smartphones, smartphone sensors are widely used for the identification of human activities. However, unlike applications in vision or data mining domain, feature embedding from deep neural networks performs much worse in terms of recognition accuracy than properly designed handcrafted features. In this paper, we posit that feature embedding from deep neural networks may convey complementary information and propose a novel knowledge distilling strategy to improve its performance. More specifically, an efficient shallow network, i.e., single-layer feedforward neural network (SLFN), with handcrafted features is utilized to assist a deep long short-term memory (LSTM) network. On the one hand, the deep LSTM network is able to learn features from raw sensory data to encode temporal dependencies. On the other hand, the deep LSTM network can also learn from SLFN to mimic how it generalizes. Experimental results demonstrate the superiority of the proposed method in terms of recognition accuracy against several state-of-the-art methods in the literature.
机译：人体活动识别是智能自动化系统中的一个核心问题，这是因为它具有广泛的应用，包括无处不在的计算，医疗保健服务和智能生活。由于智能手机的非侵入性，智能手机传感器被广泛用于识别人类活动。但是，与视觉或数据挖掘领域的应用不同，从深度神经网络嵌入的特征在识别准确度方面比正确设计的手工特征差得多。在本文中，我们认为深度神经网络的特征嵌入可以传达互补信息，并提出一种新颖的知识提取策略以提高其性能。更具体地说，利用具有手工制作特征的有效浅层网络，即单层前馈神经网络（SLFN），来辅助深长期短时记忆（LSTM）网络。一方面，深度LSTM网络能够从原始感官数据中学习特征，以对时间依赖性进行编码。另一方面，深度LSTM网络也可以从SLFN学习以模仿其推广方式。实验结果表明，相对于文献中的几种最新方法，该方法在识别精度方面具有优势。

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