A Novel Human Activity Recognition Scheme for Smart Health Using Multilayer Extreme Learning Machine

Chen, Maojian; Liu, Ying; Luo, Xiong; Wang, Weiping; Wang, Long; Zhao, Wenbing

doi:10.1109/jiot.2018.2856241

Cited by 97 publications

(39 citation statements)

References 29 publications

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“…1 These hardware systems are made smart by various computational intelligence algorithms and sensing technologies. 2 This development has resulted in many emerging highly multidisciplinary research areas typically termed as smart-* technologies, including smart-healthcare, [3][4][5][6] smart-home, 7,8 smart-grid, 9 as well as smart vehicles and intelligent transportation systems. 10 These new technologies are transforming our society and have enormous economic impact.…”

Section: Intelligent Sensing and Decision Making In Smart Technologiesmentioning

confidence: 99%

Intelligent sensing and decision making in smart technologies

Zhao

Shi

et al. 2018

International Journal of Distributed Sensor Networks

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Section: Intelligent Sensing and Decision Making In Smart Technologiesmentioning

confidence: 99%

Intelligent sensing and decision making in smart technologies

Zhao

Shi

et al. 2018

International Journal of Distributed Sensor Networks

Self Cite

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“…The report from Cisco shows that the amount of data generated by the Internet of Things accounted for 69% of the total Internet traffic in 2014, 30 times the amount of data in 2000 [1,14], and the growth of data is still in the state of acceleration. With the explosive growth of the number of sensor devices and the amount of data generated [15][16][17], extensive research has been conducted on the sensing and algorithms aspects [18][19][20], which can improve efficiency [21,22], reduce cost [23,24], and or make our life much more convenient [25,26]. However, two key issues for WSNs are still not well resolved.…”

Section: Introductionmentioning

confidence: 99%

A low redundancy data collection scheme to maximize lifetime using matrix completion technique

Tan

Liu

Xie

et al. 2019

J Wireless Com Network

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Sensor nodes equipped with various sensory devices can sense a wide range of information regarding human or things, thereby providing a foundation for Internet of Thing (IoT). Fast and energy-efficient data collection to the control center (CC) is of significance yet very challenging. To deal with this challenge, a low redundancy data collection (LRDC) scheme is proposed to reduce delay as well as energy consumption for monitoring network by using matrix completion technique. Due to the correlation of the location-dependent sensing data, some data without being collected can still be recovered by the matrix completion technology, thereby reducing the data amount for data collection and transmission, reducing the network energy consumption, and accelerating the process of data acquisition. Based on matrix completion technique, LRDC scheme can select only part of the nodes to sense data and transmit less data to CC. By doing so, the data collected by the network can be greatly reduced, which can effectively improve the network lifetime. In addition, LRDC scheme also proposes a method for quickly compensate sample data in cases of packet loss, whereby part of redundant data is sent in advance to the area closer to CC. If the data required for matrix completion is lost, these redundant data can be quickly obtained by CC, so the LRDC scheme has low delay characteristics. Simulation results demonstrate that LRDC scheme can achieve better performance than the traditional strategy, and it can reduce the maximum energy consumption of the network by 27.6-57.9% and reduce the delay by 0.7-17.9%.

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“…Vision-based fall detection has been an active research topic for a long time [11]. Recently, interest in wearable sensor-based systems has increased rapidly due to the emergence of low-cost physical sensors [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

Wang

et al. 2019

Electronics

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Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power.

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A Novel Human Activity Recognition Scheme for Smart Health Using Multilayer Extreme Learning Machine

Cited by 97 publications

References 29 publications

Intelligent sensing and decision making in smart technologies

Intelligent sensing and decision making in smart technologies

A low redundancy data collection scheme to maximize lifetime using matrix completion technique

Elderly Fall Detection with an Accelerometer Using Lightweight Neural Networks

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