An energy-efficient compressive sensing framework incorporating online dictionary learning for long-term wireless health monitoring

Xu, Kai; Li, Yixing; Ren, Fengbo

doi:10.1109/icassp.2016.7471786

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…This technique computes a small number of compressed samples before transmission by linear projection of a sparse or compressible signal with a random sensing matrix. ECG signals, like most biological signals, are not sparse in the time domain, so they can be made sparse like the work done by [20] and authors previous work [21] or using a deterministic or Adaptive Dictionary(AD) to sparsify the signals [22,23]. The wavelet basis or Gaussian dictionaries are examples of deterministic sparsifying matrices.…”

Section: Related Workmentioning

confidence: 99%

Redundancy cancellation of compressed measurements by QRS complex alignment

2022

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The demand for long-term continuous care has led healthcare experts to focus on development challenges. On-chip energy consumption as a key challenge can be addressed by data reduction techniques. In this paper, the pseudo periodic nature of ElectroCardioGram(ECG) signals has been used to completely remove redundancy from frames. Compressing aligned QRS complexes by Compressed Sensing (CS), result in highly redundant measurement vectors. By removing this redundancy, a high cluster of near zero samples is gained. The efficiency of the proposed algorithm is assessed using the standard MIT-BIH database. The results indicate that by aligning ECG frames, the proposed technique can achieve superior reconstruction quality compared to state-of-the-art techniques for all compression ratios. This study proves that by aligning ECG frames with a 0.05% unaligned frame rate(R-peak detection error), more compression could be gained for PRD > 5% when 5-bit non-uniform quantizer is used. Furthermore, analysis done on power consumption of the proposed technique, indicates that a very good recovery performance can be gained by only consuming 4.9μW more energy per frame compared to traditional CS.

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Section: Related Workmentioning

confidence: 99%

Redundancy cancellation of compressed measurements by QRS complex alignment

2022

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“…The proposed design also offloads pre-processing from sensor nodes to the server node, resulting in even significant reductions in hardware costs prior to dictionary learning. Because it is data-driven, the paradigm that has been proposed has the potential to be applied to a broad range of physiological indicators" [50]. In conclusion, based on the literature review, we can infer that much research has been undertaken and/or is currently being conducted in an effort to develop WBSNs that are flexible; reliable; secure; real-time; and energy-efficient, with the goal of using them in healthcare applications.…”

Section: Literature Surveymentioning

confidence: 99%

A Review Approach to Modeling, Analysis, and Design Framework for Wireless Sensor Network Health Monitoring Systems

2022

pnr

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According to the results of the survey on Modeling, Analysis, and Design Framework for Wireless Sensor Network Health Monitoring Systems, the current study is written. Wireless Sensor Networks (WSNs) are fast emerging as a key enabling technology for a diverse variety of applications across a wide range of industries and disciplines. Recent breakthroughs in wireless sensor networks (WSN) have made it feasible to deploy extensive health monitoring in both the home care and hospital sectors, thanks to recent advances in wireless sensor networks. As a result of technological developments in sensors, power-efficient integrated circuits, and wireless communication, it is now feasible to design physiological sensor nodes that are compact and lightweight, low in cost, and capable of giving real-time information. Depending on their design, these nodes are capable of detecting, processing, and sending one or more vital signals, among other things. As a side benefit, they may be used to monitor one's health via wireless personal area networks (WPANs) or wireless body sensor networks, among other applications (WBSNs). Many studies have been undertaken and/or are currently being conducted in an effort to develop WBSNs that are flexible, reliable, secure, real-time, and power-efficient, with the goal of using them in healthcare applications. WBSNs are a kind of wireless backhaul network (WBSN).

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“…And Morell et al (2016) applied the principal component analysis method to the WSNs data collection process to construct the sparse representation matrix, and at the same time gave a method for updating the sparse representation matrix. Xu et al (2016) proposed a method of using dictionary learning to construct sparse representation bases, which can effectively enhance the sparseness of the converted signal and improve the accuracy of sensor data reconstruction. The dictionary learning form can be expressed as follows: …”

Section: Proposed Algorithmmentioning

confidence: 99%

A novel double sparse structure dictionary learning–based compressive data-gathering algorithm in wireless sensor networks

Chen

Guo

Zhou

et al. 2021

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Purpose As the limited energy of wireless sensor networks (WSNs), energy-efficient data-gathering algorithms are required. This paper proposes a compressive data-gathering algorithm based on double sparse structure dictionary learning (DSSDL). The purpose of this paper is to reduce the energy consumption of WSNs. Design/methodology/approach The historical data is used to construct a sparse representation base. In the dictionary-learning stage, the sparse representation matrix is decomposed into the product of double sparse matrices. Then, in the update stage of the dictionary, the sparse representation matrix is orthogonalized and unitized. The finally obtained double sparse structure dictionary is applied to the compressive data gathering in WSNs. Findings The dictionary obtained by the proposed algorithm has better sparse representation ability. The experimental results show that, the sparse representation error can be reduced by at least 3.6% compared with other dictionaries. In addition, the better sparse representation ability makes the WSNs achieve less measurement times under the same accuracy of data gathering, which means more energy saving. According to the results of simulation, the proposed algorithm can reduce the energy consumption by at least 2.7% compared with other compressive data-gathering methods under the same data-gathering accuracy. Originality/value In this paper, the double sparse structure dictionary is introduced into the compressive data-gathering algorithm in WSNs. The experimental results indicate that the proposed algorithm has good performance on energy consumption and sparse representation.

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An energy-efficient compressive sensing framework incorporating online dictionary learning for long-term wireless health monitoring

Cited by 18 publications

References 17 publications

Redundancy cancellation of compressed measurements by QRS complex alignment

Redundancy cancellation of compressed measurements by QRS complex alignment

A Review Approach to Modeling, Analysis, and Design Framework for Wireless Sensor Network Health Monitoring Systems

A novel double sparse structure dictionary learning–based compressive data-gathering algorithm in wireless sensor networks

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