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AI-Enabled Low Power Implantable and Wearable Medical Devices

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 8402

Special Issue Editors


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Guest Editor
Department of Computer Science and Engineering, School of Engineering, American University of Ras al Khaimah, Ras al Khaimah P.O. Box 10021, United Arab Emirates
Interests: neural engineering; low power sensors/IoT devices; hardware accelerators; FPGAs/ASIC; applied artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Dept of Med Phys & Biomedical Engineering, University College London, London, UK
Interests: Healthcare technology; medical imaging; image-guided surgery; surgical navigation and robotics; augmented reality; bioelectronics; imaging of epileptic activity and machine learning for stroke diagnosis

Special Issue Information

This Special Issue seeks submissions detailing cutting edge research from academia, industry, and practitioners with an emphasis on original, novel, innovative, and impact-oriented research providing insights into “AI-Enabled Low-Power Implantable and Wearable Medical Devices”. This Special Issue seeks all types of manuscripts, including reviews, research articles, and communications). An area of particular interest to this Special Issue is research that considers intelligent machine learning-based front-end sensing that could be embedded into low-power implantable and wearable medical devices as well as AI-enabled medical devices that will promote user autonomy and independent living.

The submissions will undergo rigorous peer review, where each submitted paper will be reviewed by independent experts. We also recommend the submission of supplementary material with each paper, including test data and multimedia, as it significantly increases the visibility, downloads, and citations of articles.

Selection and Evaluation Criteria

  • Relevance to the topics of this Special Issue
  • Research novelty (e.g., new techniques) and potential impact
  • Content quality and readability

To summarise, this Special Issue aims to bring together innovative developments and synergies in the below-mentioned topics but are not limited to the following:

  • Embedded machine learning in wearable/implantable devices;
  • Flexible electronic wearable devices;
  • Textile sensors;
  • Low-power electronic solutions for signals acquisition and processing from wearable sensors;
  • Conductive/e-textiles for body area sensing;
  • Non-conventional patient monitoring;
  • Wearable devices for environmental monitoring;
  • SoC for health monitoring applications;
  • Algorithms for front-end sensing;
  • 3D printing technology applied to wearable sensor development;
  • 5G based IoT paradigms for wearable/implantable devices;
  • 5G for remote patient/environment sensing;
  • Remote charging for low power implantable devices;
  • Software development for wearable sensors and body sensor networks.

We invite the scientific community to support Sensors (IF 3.576) in this timely initiative by submitting new and groundbreaking papers that will establish the roadmap for future research.

Dr. Arfan Ghani 
Dr. Chan H. See
Dr. Thomas Dowrick
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

19 pages, 3526 KiB  
Article
Towards Accurate, Cost-Effective, Ultra-Low-Power and Non-Invasive Respiration Monitoring: A Reusable Wireless Wearable Sensor for an Off-the-Shelf KN95 Mask
by Yu Xu, Qi Li, Zhenzhou Tang, Jun Liu and Bingjin Xiang
Sensors 2021, 21(20), 6698; https://doi.org/10.3390/s21206698 - 9 Oct 2021
Cited by 5 | Viewed by 2232
Abstract
Respiratory rate is a critical vital sign that indicates health condition, sleep quality, and exercise intensity. This paper presents a non-invasive, ultra-low-power, and cost-effective wireless wearable sensor, which is installed on an off-the-shelf KN95 mask to facilitate respiration monitoring. The sensing principle is [...] Read more.
Respiratory rate is a critical vital sign that indicates health condition, sleep quality, and exercise intensity. This paper presents a non-invasive, ultra-low-power, and cost-effective wireless wearable sensor, which is installed on an off-the-shelf KN95 mask to facilitate respiration monitoring. The sensing principle is based on the periodic airflow temperature variations caused by exhaled hot air and inhaled cool air in respiratory cycles. By measuring the periodic temperature variations at the exhalation valve of mask, the respiratory parameters can be accurately and reliably detected, regardless of body movements and breathing pathways through nose or mouth. Specifically, we propose a voltage divider with controllable resistors and corresponding selection criteria to improve the sensitivity of temperature measurement, a peak detection algorithm with spline interpolation to increase sampling period without reducing the detection accuracy, and effective low-power optimization measures to prolong the battery life. The experimental results have demonstrated the effectiveness of the proposed sensor, showing a small mean absolute error (MAE) of 0.449 bpm and a very low power consumption of 131.4 μW. As a high accuracy, low cost, low power, and reusable miniature wearing device for convenient respiration monitoring in daily life, the proposed sensor holds promise in real-world feasibility. Full article
(This article belongs to the Special Issue AI-Enabled Low Power Implantable and Wearable Medical Devices)
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17 pages, 3552 KiB  
Article
A Low Power Sigma-Delta Modulator with Hybrid Architecture
by Shengbiao An, Shuang Xia, Yue Ma, Arfan Ghani, Chan Hwang See, Raed A. Abd-Alhameed, Chuanfeng Niu and Ruixia Yang
Sensors 2020, 20(18), 5309; https://doi.org/10.3390/s20185309 - 16 Sep 2020
Cited by 4 | Viewed by 5137
Abstract
Analogue-to-digital converters (ADC) using oversampling technology and the Σ-∆ modulation mechanism are widely applied in digital audio systems. This paper presents an audio modulator with high accuracy and low power consumption by using a discrete second-order feedforward structure. A 5-bit successive approximation register [...] Read more.
Analogue-to-digital converters (ADC) using oversampling technology and the Σ-∆ modulation mechanism are widely applied in digital audio systems. This paper presents an audio modulator with high accuracy and low power consumption by using a discrete second-order feedforward structure. A 5-bit successive approximation register (SAR) quantizer is integrated into the chip, which reduces the number of comparators and the power consumption of the quantizer compared with flash ADC-type quantizers. An analogue passive adder is used to sum the input signals and it is embedded in a SAR ADC composed of a capacitor array and a dynamic comparator which has no static power consumption. To validate the design concept, the designed modulator is developed in a 180 nm CMOS process. The peak signal to noise distortion ratio (SNDR) is calculated as 106 dB and the total power consumption of the chip is recorded as 3.654 mW at the chip supply voltage of 1.8 V. The input sine wave of 0 to 25 kHz is sampled at a sampling frequency of 3.2 Ms/s. Moreover, the results achieve a 16-bit effective number of bits (ENOB) when the amplitude of the input signal is varied between 0.15 and 1.65 V. By comparing with other modulators which were realized by a 180 nm CMOS process, the proposed architecture outperforms with lower power consumption. Full article
(This article belongs to the Special Issue AI-Enabled Low Power Implantable and Wearable Medical Devices)
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