Deep Dilation on Multimodality Time Series for Human Activity Recognition

Xi, Rui; Li, Ming; Hou, Mengshu; Fu, Mingsheng; Qu, Hong; Liu, Daibo; Haruna, Charles Roland

doi:10.1109/access.2018.2870841

Cited by 33 publications

(20 citation statements)

References 25 publications

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“…The authors of [11] proposed a network with four temporal convolutional layers and two Long-Short Term Memory (LSTM) layers followed by a softmax layer. The authors of [88] proposed to use dilated temporal ConvLSTMs. The network consists of one initial convolutional layer followed by three dilated temporal convolutional layers with different dilated factors.…”

Section: Deep Learningmentioning

confidence: 99%

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

et al. 2019

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This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding the observed activities, sensor attachment, utilised datasets, sensor technology and the applied methods of HAR. This review is focused on applications that use marker-based Motion Capturing or Inertial Measurement Units. The analysed methods can be deployed in industrial application of Production and Logistics or transferred from related domains into this field. The findings provide an overview of the specifications of state-of-the-art HAR approaches, statistical pattern recognition and deep architectures and they outline a future road map for further research from a practitioner's perspective.

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Section: Deep Learningmentioning

confidence: 99%

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

et al. 2019

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“…A semi-supervised model using a DeepLSTM based approach with temporal ensembling for activity recognition using inertial sensors. (Xi, et al, 2018) OPPORTUNITY, PAMAP2 CNN, RNN ADL They used dilated convolutional layers to automatically extract intersensor and intra-sensor features. They also proposed a novel dilated SRU (Simple Recurrent Unit) approach to capture the latent time dependencies among features.…”

Section: Cnn Adlmentioning

confidence: 99%

Deep Architectures for Human Activity Recognition using Sensors

Baloch¹,

Shaikh²,

Unar³

2019

3C Tecnología

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Human activity recognition (HAR) is a renowned research field in recent years due to its applications such as physical fitness monitoring, assisted living, elderly-care, biometric authentication and many more. The ubiquitous nature of sensors makes them a good choice to use for activity recognition. The latest smart gadgets are equipped with most of the wearable sensors i.e. accelerometer, gyroscope, GPS, compass, camera, microphone etc. These sensors measure various aspects of an object, and are easy to use with less cost. The use of sensors in the field of HAR opens new avenues for machine learning (ML) researchers to accurately recognize human activities. Deep learning (DL) is becoming popular among HAR researchers due to its outstanding performance over conventional ML techniques. In this paper, we have reviewed recent research studies on deep models for sensor-based human activity recognition. The aim of this article is to identify recent trends and challenges in HAR.

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“…Usage of the network, e.g., WiFi signal and radio-frequencies were also reported to be used in human activity recognition Wang et al [26], Wenyuan et al [27] Activity recognition on the basis of smart-based data are discussed by Wang et al [28]. Other unique research implementations are also witnessed in work of Yao et al [29] Yang et al [30] and a unique recurrent modeling is presented using dilation operation for activity recognition system (Xi et al [31]). The next section discusses the research problem.…”

Section: Introductionmentioning

confidence: 99%

A simplified machine learning approach for recognizing human activity

S¹,

Eranna²

2019

IJECE

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With the wide ranges of real-time event feed capturing devices, there has been significant progress in the area of digital image processing towards activity detection and recognition. Irrespective of the presence of various such devices, they are not adequate to meet dynamic monitoring demands of the visual surveillance system, and their features are highly limited towards complex human activity recognition system. Review of existing system confirms that still there is a large scope of enhancement as they lack applicability to real-life events and also doesn't offer optimal system performance. Therefore, the proposed manuscript presents a model for activity recognition system where the accuracy of recognition operation and system performance are retained with good balance. The study presents a simplified feature extraction process from spatial and temporal traits of the event feeds that is further subjected to the machine learning mechanism for boosting recognition performance

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Deep Dilation on Multimodality Time Series for Human Activity Recognition

Cited by 33 publications

References 25 publications

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

Deep Architectures for Human Activity Recognition using Sensors

A simplified machine learning approach for recognizing human activity

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