A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare

Uddin, Md. Zia; Hassan, Mohammed Mehedi; Alsanad, Ahmed; Savaglio, Claudio

doi:10.1016/j.inffus.2019.08.004

Cited by 147 publications

(64 citation statements)

References 18 publications

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“…Xu et al combined the CNN and the gated recurrent unit (GRU) to identify human activities, and verified the effectiveness of the proposed method on three public datasets [17]. Uddin et al proposed a multi-sensors data fusion network based on recurrent neural network (RNN) [18]. Firstly, the timefrequency domain features were extracted from the original sensor data.…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on Gramian Angular Field and Deep Convolutional Neural Network

Yuan

et al. 2020

IEEE Access

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With the development of the Internet of things (IoT) and wearable devices, the sensor-based human activity recognition (HAR) has attracted more and more attentions from researchers due to its outstanding characteristics of convenience and privacy. Meanwhile, deep learning algorithms can extract high-dimensional features automatically, which makes it possible to achieve the end-to-end learning. Especially the convolutional neural network (CNN) has been widely used in the field of computer vision, while the influence of environmental background, camera shielding, and other factors are the biggest challenges to it. However, the sensor-based HAR can circumvent these problems well. Two improved HAR methods based on Gramian angular field (GAF) and deep CNN are proposed in this paper. Firstly, the GAF algorithm is used to transform the one-dimensional sensor data into the two-dimensional images. Then, through the multi-dilated kernel residual (Mdk-Res) module, a new improved deep CNN network Mdk-ResNet is proposed, which extracts the features among sampling points with different intervals. Furthermore, the Fusion-Mdk-ResNet is adopted to process and fuse data collected by different sensors automatically. The comparative experiments are conducted on three public activity datasets, which are WISDM, UCI HAR and OPPORTUNITY. The optimal results are obtained by using the indexes such as accuracy, precision, recall and F-measure, which verifies the effectiveness of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Based on Gramian Angular Field and Deep Convolutional Neural Network

Yuan

et al. 2020

IEEE Access

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“…In modern platforms like CIoTVID, this is done taking care of ownership, security, and privacy. The reasons why these platforms give a large marginal value to society is because the proliferation of IoT devices in smart environments is happening in the form of smartphones, smart speakers, security sensors (as part of home alarms), comfort sensors, wearable devices for recognizing and monitoring behavior [ 7 ] and so forth. This implies the generation of a variety of methodologies and tools taking the form of integrated IoT platforms in more mature fields such as energy management, home security, or smart entertainment or healthcare.…”

Section: Related Workmentioning

confidence: 99%

CIoTVID: Towards an Open IoT-Platform for Infective Pandemic Diseases such as COVID-19

Ramallo-González

González-Vidal

Skarmeta

2021

Sensors

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The factors affecting the penetration of certain diseases such as COVID-19 in society are still unknown. Internet of Things (IoT) technologies can play a crucial role during the time of crisis and they can provide a more holistic view of the reasons that govern the outbreak of a contagious disease. The understanding of COVID-19 will be enriched by the analysis of data related to the phenomena, and this data can be collected using IoT sensors. In this paper, we show an integrated solution based on IoT technologies that can serve as opportunistic health data acquisition agents for combating the pandemic of COVID-19, named CIoTVID. The platform is composed of four layers—data acquisition, data aggregation, machine intelligence and services, within the solution. To demonstrate its validity, the solution has been tested with a use case based on creating a classifier of medical conditions using real data of voice, performing successfully. The layer of data aggregation is particularly relevant in this kind of solution as the data coming from medical devices has a very different nature to that coming from electronic sensors. Due to the adaptability of the platform to heterogeneous data and volumes of data; individuals, policymakers, and clinics could benefit from it to fight the propagation of the pandemic.

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“…Activity recognition using a smartphone aims to recognize a user’s behavior using the built-in inertial sensor. The target activities range from work that recognizes daily movements, such as walking and running, to work that recognizes focused movements, such as falling movements [ 10 , 11 , 12 ]. However, activity recognition has the problem that recognition accuracy decreases, depending on the position of the smartphone on the body [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

New Position Candidate Identification via Clustering toward an Extensible On-Body Smartphone Localization System

Saito

Fujinami

2021

Sensors

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On-body device position awareness plays an important role in providing smartphone-based services with high levels of usability and quality. Traditionally, the problem assumed that the positions that were supported by the system were fixed at the time of design. Thus, if a user stores his/her terminal into an unsupported position, the system forcibly classifies it into one of the supported positions. In contrast, we propose a framework to discover new positions that are not initially supported by the system, which adds them as recognition targets via labeling by a user and re-training on-the-fly. In this article, we focus on a component of identifying a set of samples that are derived from a single storing position, which we call new position candidate identification. Clustering is applied as a key component to prepare a reliable dataset for re-training and to reduce the user’s burden of labeling. Specifically, density-based spatial clustering of applications with noise (DBSCAN) is employed because it does not require the number of clusters in advance. We propose a method of finding an optimal value of a main parameter, Eps-neighborhood (eps), which affects the accuracy of the resultant clusters. Simulation-based experiments show that the proposed method performs as if the number of new positions were known in advance. Furthermore, we clarify the timing of performing the new position candidate identification process, in which we propose criteria for qualifying a cluster as the one comprising a new position.

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A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare

Cited by 147 publications

References 18 publications

Human Activity Recognition Based on Gramian Angular Field and Deep Convolutional Neural Network

Human Activity Recognition Based on Gramian Angular Field and Deep Convolutional Neural Network

CIoTVID: Towards an Open IoT-Platform for Infective Pandemic Diseases such as COVID-19

New Position Candidate Identification via Clustering toward an Extensible On-Body Smartphone Localization System

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