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access icon free Design of a charge amplifier for a low-power respiration-monitoring system

© The Institution of Engineering and Technology
Received 27/07/2018, Accepted 10/01/2019, Revised 02/12/2018, Published 17/01/2019

Home-based health care applications are rapidly gaining popularity, enabling a renewed focus on the design of low-power and low-noise front-end circuitry. In this context, the evaluation of low-frequency biomedical signals, such as the respiration pattern, benefits from the design of a front-end amplifier with reduced power consumption and low noise. Continuous efforts on improving the performances of respiration-monitoring devices have resulted in the reduction of noise and motion artefacts by increasing complexity (e.g. complex algorithms or high precision filtering) at the expense of increased power consumption. This study is focused on the design of a fully integrated charge amplifier for respiration monitoring based on a pyroelectric sensor. Simulation and test results show a power consumption of 1.8 µW, an active die area of 0.085 mm2, a bandwidth in the range from 10 mHz to 13 kHz, and a remarkable noise efficiency factor of ∼2.79, which fits well with the development of an energy efficient wearable device.

Inspec keywords: power consumption; pneumodynamics; pyroelectric detectors; low-power electronics; biomedical electronics; medical signal processing; health care; energy conservation; patient monitoring; amplifiers

Other keywords: low-power respiration-monitoring system; respiration-monitoring devices; respiration monitoring; low-noise front-end circuitry; low-frequency biomedical signals; power consumption; respiration pattern; reduced power consumption; home-based health care applications; frequency 10 mHz to 13 kHz; energy efficient wearable device; pyroelectric sensor; fully integrated charge amplifier; power 1.8 muW; front-end amplifier

Subjects: Biomedical measurement and imaging; Sensing devices and transducers; Electrical/electronic equipment (energy utilisation); Amplifiers; Biology and medical computing; Haemodynamics, pneumodynamics; Patient diagnostic methods and instrumentation; Digital signal processing

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