Low-Power System for Detection of Symptomatic Patterns in Audio Biological Signals

Markandeya, Himanshu S.; Roy, Kaushik

doi:10.1109/tvlsi.2016.2521869

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…However, all the above studies focused on the detection of a single symptom; the robustness of such methods is not addressed if applied to multiple concurrent symptoms, a feature of ‘real-world’ clinical practice. To the best of the authors’ knowledge, the only study that explored the overall detection of multiple concurrent symptoms including cough and wheeze was performed by Himanshu S. Markandeya [ 14 ]. This system employed a two-stage classification structure and achieved 90% accuracy from 74 recordings.…”

Section: Introductionmentioning

confidence: 99%

Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices

Bing

Shi

Chotirmall

et al. 2022

Sensors

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Smart wearable sensors are essential for continuous health-monitoring applications and detection accuracy of symptoms and energy efficiency of processing algorithms are key challenges for such devices. While several machine-learning-based algorithms for the detection of abnormal breath sounds are reported in literature, they are either too computationally expensive to implement into a wearable device or inaccurate in multi-class detection. In this paper, a kernel-like minimum distance classifier (K-MDC) for acoustic signal processing in wearable devices was proposed. The proposed algorithm was tested with data acquired from open-source databases, participants, and hospitals. It was observed that the proposed K-MDC classifier achieves accurate detection in up to 91.23% of cases, and it reaches various detection accuracies with a fewer number of features compared with other classifiers. The proposed algorithm’s low computational complexity and classification effectiveness translate to great potential for implementation in health-monitoring wearable devices.

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Section: Introductionmentioning

confidence: 99%

Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices

Bing

Shi

Chotirmall

et al. 2022

Sensors

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show abstract

“…Hence, the power density of the wireless frequency in this channel [10] increased and we can drain it to overcome the limitation due to the low input power density of the RFEH in improving the system's energy reliability. In complementing the RFEH, the Sensors 2022, 22, 4415 2 of 18 power consumption of various portable devices operates within the hundreds of µW range for the detection of symptomatic patterns in audio biological signals [11], ECG [12,13], and other health monitoring [14]. Table 1 shows the typical range of the power consumption of various electronic devices.…”

Section: Introductionmentioning

confidence: 99%

A Fully-Integrated Ambient RF Energy Harvesting System with 423-μW Output Power

Churchill

Ramiah

Chong³

et al. 2022

Sensors

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This paper proposes a 2.4-GHz fully-integrated single-frequency multi-channel RF energy harvesting (RFEH) system with increased harvested power density. The RFEH can produce an output power of ~423-μW in harvesting ambient RF energy. The front-end consists of an on-chip impedance matching network with a stacked rectifier concurrently matched to a 50 Ω input source. The circuit mitigates the “dead-zone” by enhancing the pumping efficiency, achieved through the increase of Vgs drivability of the proposed internal gate boosting 6-stage low-input voltage charge pump and the 5-stage shared-auxiliary-biasing ring-voltage-controlled-oscillator (VCO) integrated to improve the start-up. The RFEH system, simulated in 180-nm complementary metal–oxide–semiconductor (CMOS), occupies an active area of 1.02 mm2. Post-layout simulations show a peak power conversion efficiency(PCE) of 21.15%, driving a 3.3-kΩ load at an input power of 0 dBm and sensitivity of −14.1 dBm corresponding to an output voltage, Vout,RFEH of 1.25 V.

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Common-mode interference and power conversion efficiency of differential rectifiers in RF energy harvesters

Liu

Hsu

Fahs

et al. 2020

Analog Integr Circ Sig Process

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Low-Power System for Detection of Symptomatic Patterns in Audio Biological Signals

Cited by 5 publications

References 17 publications

Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices

Distance-Based Detection of Cough, Wheeze, and Breath Sounds on Wearable Devices

A Fully-Integrated Ambient RF Energy Harvesting System with 423-μW Output Power

Common-mode interference and power conversion efficiency of differential rectifiers in RF energy harvesters

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