Wireless Body Sensor Network With Adaptive Low-Power Design for Biometrics and Healthcare Applications

Chen, Shih-Lun; Lee, Ho-Yin; Chen, Chiung-An; Huang, Hong-Yi; Luo, Ching‐Hsing

doi:10.1109/jsyst.2009.2032440

Cited by 134 publications

(32 citation statements)

References 32 publications

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“…Method III outperformed existing well-known lossy and lossless compression methods by scoring the highest BCR at 4.5 while scoring the lowest PRD at 0.53. Note that Methods III and III have already outperformed the state-of-the-art lossless compression methods: delta predictor/Rice-Golomb coding 10 , adaptive predictor/Huffman coding 11 , simple predictor/Huffman coding 12 , and slope predictor/fixed-length packaging methods 13 in terms of increasing BCR and decreasing PRD simultaneously. The higher the BCR and the lower PRD values in an algorithm, the better the compression performance However, the detection of QRS has to be considered when choosing the optimal compression algorithm.…”

Section: Resultsmentioning

confidence: 97%

“…The two-stage adaptive predictor and the Huffman coding schemes 11 achieved a BCR of 2.43, but Huffman coding is highly complex, generates variable-length coded data, and would need further packaging to interface with a standard input/output 8 . Simple predictor and Huffman coding 12 achieved a BCR of 1.92. The slope predictor and a fixed-length packaging scheme 12 achieved a BCR of 2.25.…”

Section: Discussionmentioning

confidence: 99%

“…Simple predictor and Huffman coding 12 achieved a BCR of 1.92. The slope predictor and a fixed-length packaging scheme 12 achieved a BCR of 2.25. Method I (linear predictor and fixed-length packaging) achieved a BCR of 2.28.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.

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Section: Resultsmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

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Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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“…As this reason, many telemedicine and electronic biomedicine researches, especial on the development of biochips for wireless body area sensor network applications, are presented [1][2]. The body area sensor network provides wireless connectivity among various physiological sensors and medical devices carried by a patient for the continuous and ambulatory health care.…”

Section: Introductionmentioning

confidence: 99%

Development of a high-gain high-isolation low-power receiver for wireless body area sensor network application system

Huang

Luo

et al. 2015

2015 Ieee Sensors

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This study presents a high power conversion gain (CG), excellent isolation and low power consumption receiver for wireless body sensor network application system utilizing TSMC 0.18 μm CMOS technology with chip size only 1.12 mm × 0.98 mm. The high gain and low power receiver which comprises variable gain low noise amplifier (VGLNA) and down-conversion mixer, the VGLNA with current-reused and RC feedback techniques to improve the power consumption and stable the bias voltage, and the self-biased technique is applied to the VGA stage for reduction of the voltage supply. Otherwise, the downconversion mixer is based on the Gilbert cell architecture which employs output load with RC in parallel structure, currentbleeding and source-degeneration to enhance the power conversion gain (CG) and linearity. The implemented receiver exhibits high CG of 17.6 dB, low power consumption of 20 mW and excellent isolation of -68 dB is demonstrated in this study. Keywords-high power conversion gain; isolation; low power; receiver; wireless body sensor network application systemI.

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“…This new trend, and the use of wireless sensors within hospitals and home environments, can reduce healthcare costs and improve our daily lifestyle. Many wireless systems have been proposed to accomplish this feat, with diverse solutions [9,10].…”

Section: Introductionmentioning

confidence: 99%

Physiological Features from an On-Body Radio Propagation Channel

Munoz

Foster

Hao

2012

2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks

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A pair of low-power wireless sensor nodes, operating in the 2.45 GHz ISM band, are used to record the radio propagation of a particular on-body channel, the waistchest channel. The recorded time-sampled radio link data is analysed in the frequency domain. It is shown that the wave propagation along the human body's surface embeds not only the radio channel characteristics, but also physiological features.

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Wireless Body Sensor Network With Adaptive Low-Power Design for Biometrics and Healthcare Applications

Cited by 134 publications

References 32 publications

Efficient ECG Compression and QRS Detection for E-Health Applications

Efficient ECG Compression and QRS Detection for E-Health Applications

Development of a high-gain high-isolation low-power receiver for wireless body area sensor network application system

Physiological Features from an On-Body Radio Propagation Channel

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