Understanding and Improving Deep Neural Network for Activity Recognition

Li, Xue; Si, Xiandong; Li, Jiazhen; Ding, Ronggui; Zhan, Dechen; Chu, Dianhui

doi:10.48550/arxiv.1805.07020

Cited by 1 publication

(2 citation statements)

References 21 publications

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“…However, this is largely insufficient since deep models still may encode some noise such as irrelevant modalities [175]. Some researchers [19,163] visualized the features extracted by neural networks. Salient features are sent to the subsequent models after the authors find out their relationships to the activities from the visualization [163].…”

Section: Interpretability Of Deep Learning Models In Sensory Datamentioning

confidence: 99%

“…Some researchers [19,163] visualized the features extracted by neural networks. Salient features are sent to the subsequent models after the authors find out their relationships to the activities from the visualization [163]. Nutter et al [103] transformed sensory data to images so that visualization tools can be applied to the sensory data for more direct interpretability.…”

Section: Interpretability Of Deep Learning Models In Sensory Datamentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities

Chen,

Zhang,

Yao

et al. 2020

Preprint

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The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that could influence the performance of the recognition system in practical scenarios. Recently, as deep learning has demonstrated its effectiveness in many areas, plenty of deep methods have been investigated to address the challenges in activity recognition. In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets that can be used for evaluation in different challenge tasks. We then propose a new taxonomy to structure the deep methods by challenges. Challenges and challenge-related deep methods are summarized and analyzed to form an overview of the current research progress. At the end of this work, we discuss the open issues and provide some insights for future directions. CCS Concepts: • General and reference → Surveys and overviews; • Hardware → Sensor devices and platforms; • Computer systems organization → Neural networks.

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