Deep Learning for Human Activity Recognition in Mobile Computing

Plötz, Thomas; Guan, Yu

doi:10.1109/mc.2018.2381112

Cited by 99 publications

(81 citation statements)

References 12 publications

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“…Recently, [33,34] examined the possibility of learning features automatically. Feature learning is a well-studied approach for static data (e.g., object recognition in computer vision).…”

Section: Survey Of Feature Extraction Pipelinesmentioning

confidence: 99%

A Comprehensive Study of Activity Recognition Using Accelerometers

et al. 2018

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This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there are performance trade-offs between prediction accuracy, which is not the sole system objective, and keeping the maintenance overhead at minimum levels. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions.

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“…Recently, [33,34] examined the possibility of learning features automatically. Feature learning is a well-studied approach for static data (e.g., object recognition in computer vision).…”

Section: Survey Of Feature Extraction Pipelinesmentioning

confidence: 99%

A Comprehensive Study of Activity Recognition Using Accelerometers

et al. 2018

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“…In recent years, some approaches such as principal component analysis (PCA) [10] and restricted Boltzmann machine (RBM) [11] were also applied to adapt the feature extraction over the dataset. The mapping from raw signals to features is not predefined, but automatically learned from the training dataset.…”

Section: Rrlated Workmentioning

confidence: 99%

Using Convolutional Layer Features for Indoor Human Activity Recognition based on Spatial Location Information

Li¹,

Zhao²,

Li³

et al. 2018

dtcse

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Identifying human actions has great importance for various applications, especially in the smart home, fitness tracking and health monitoring domains. However, human activity recognition still remains a challenging task. This is mainly due to the broad range of human activities as well as the rich variation of a given activity can be performed. In this paper, we dealt with the problem by making use of spatial location information of three different parts of a human body, which are derived via three UWB (ultrawide band) tags and an Ubisense positioning system. In order to improve the accuracy, we proposed a recognition method: convolutional layer features plus SVM (Support Vector Machine). We pre-process the raw spatial location data and transfer them into motion feature, frequency feature and statistic feature. These features are input into the CNN (Convolutional Neural Network) to generate the convolutional layer features, and then we use SVM to classify these features. By comparing the experimental results, the best recognition rate of different experimenters is 89.75%, which shows its feasibility.

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“…Common transformations that have been applied include FFTs and Discrete Cosine Transforms (DCTs) [6,13]. FFT is an efficient algorithm optimised for computing the discrete Fourier transform of a digital input by decomposing the input into its constituent sine waves.…”

Section: Frequency Transform Featuresmentioning

confidence: 99%

“…Deep approaches are able to stack multiple layers of operations to create a hierarchy of increasingly more abstract features [10]. Early work using Restricted Boltzmann Machines for HAR have only shown comparative performance to FFT and Principal Component Analysis [13]. More recent applications have used more of Convolutional Neural Networks (CNNs) due to their ability to model local dependencies that may exist between adjacent data points in the accelerometer data [18].…”

Section: Deep Featuresmentioning

confidence: 99%