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Andy Bardill

Researcher at Middlesex University

Publications -  26
Citations -  385

Andy Bardill is an academic researcher from Middlesex University. The author has contributed to research in topics: Product design & Electrical impedance tomography. The author has an hindex of 7, co-authored 25 publications receiving 266 citations.

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Journal ArticleDOI

The application of mHealth to mental health: opportunities and challenges

TL;DR: How traditional boundaries between research and clinical practice are becoming increasingly blurred and how, in turn, this is leading to exciting new developments in the assessment and management of common mental disorders is shown.
Journal ArticleDOI

A High Frame Rate Wearable EIT System Using Active Electrode ASICs for Lung Respiration and Heart Rate Monitoring

TL;DR: A high specification, wearable, electrical impedance tomography (EIT) system with 32 active electrodes is presented and its successful operation in capturing EIT lung respiration and heart rate biosignals from a volunteer is demonstrated.
Journal ArticleDOI

A 122 fps, 1 MHz Bandwidth Multi-Frequency Wearable EIT Belt Featuring Novel Active Electrode Architecture for Neonatal Thorax Vital Sign Monitoring

TL;DR: A highly integrated, wearable electrical impedance tomography (EIT) belt for neonatal thorax vital multiple sign monitoring is presented, which features a new active electrode architecture that allows programmable flexible electrode current drive and voltage sense patterns under simple digital control.
Proceedings ArticleDOI

SenseMap: Supporting browser-based online sensemaking through analytic provenance

TL;DR: A simplified sensemaking model based on Pirolli and Card's model is derived to better represent the behaviors of users: users iteratively collect information sources relevant to the task, curate them in a way that makes sense, and finally communicate their findings to others.
Journal ArticleDOI

Torso shape detection to improve lung monitoring.

TL;DR: This study presents and compares two methodologies that can detect the patient-specific torso shape by means of wearable devices based on previously reported bend sensor technology, and a novel approach based on the use of accelerometers.