Key result
A 65-nm CMOS impulse radar system successfully extracted human respiratory features and identified respiratory diseases using machine learning algorithms.
Why the study?
Does a 65-nm CMOS low-power impulse radar system accurately extract human respiratory features and diagnose respiratory diseases?
Does a 65-nm CMOS low-power impulse radar system accurately extract human respiratory features and diagnose respiratory diseases?
A novel 65-nm CMOS impulse radar system effectively extracts respiratory features and diagnoses respiratory diseases using machine learning algorithms.
Answers are grounded in this paper and cite their sources.
May enable contactless respiratory monitoring; hypothesis-generating and requires larger validation trials.
This paper presents a radar system for extracting human respiratory features. The proposed radar chip comprises three major components: a digital-to-time converter (DTC), a transmitter, and a receiver. The all-digital standard cell-based DTC achieves a timing resolution of 10 ps on a 100-ns time scale, supporting a range-gated sensing process. The transmitter is composed of a digital pulse generator. The receiver comprises a direct-sampling passive frontend for achieving high linearity, an integrator for enhancing the signal-to-noise ratio, and a successive approximation register analog-to-digital converter for signal quantization. A fully integrated CMOS impulse radar chip was fabricated using 65-nm CMOS technology, and the total power consumption is 21 mW. In the backend, a real-time digital signal-processing platform captures human respiratory waveforms via the radar chip and processes the waveforms by applying a human respiratory feature extraction algorithm. Furthermore, a clinical trial was conducted for establishing a new diagnosis workflow for identifying respiratory diseases by the proposed wireless sensor system. The proposed system was validated by applying an adaptive network-based fuzzy inference system and support vector machine algorithm to the clinical trial results. These algorithms confirmed the effectiveness of the proposed system in diagnosing respiratory diseases.
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Tseng et al. (2016) studied Respiratory diseases. 65-nm CMOS Low-Power Impulse Radar System was evaluated on Identification of respiratory diseases. A 65-nm CMOS impulse radar system successfully extracted human respiratory features and identified respiratory diseases using machine learning algorithms.
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