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Mobile Healthcare Based on IoT
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Mobile Healthcare Based on IoT

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 11274

Special Issue Editor

Prof. Dr. Hangsik Shin

E-Mail Website
Guest Editor
Chonnam National Univesity, Yeosu, Korea
Interests: biomedical measurement; biosignal processing; mobile healthcare; Internet of medical things; digital healthcare; wearable healthcare

Special Issue Information

Dear Colleagues,

Mobile healthcare has revolutionized the traditional healthcare paradigm that has been centered around hospitals, allowing us to experience healthcare anywhere and anytime. Mobile healthcare focuses on the treatment of diseases after they occur, improving human health and quality of life through early diagnosis, prevention, and efficient post-management of diseases. Internet-of Things (IoT) technology combined with healthcare enables individuals to measure and diagnose their condition anytime, anywhere. In addition, it is possible to perform improved healthcare based on the general information related to the health of the individual, such as behavioral monitoring, environmental monitoring, and lifestyle monitoring, which was not possible in the existing medical system. Mobile healthcare continues to demand advanced technology that can be applied in real life or breakthrough technology that can solve the problems of conventional methods, and this is the main topic of this Special Issue.

Prof. Dr. Hangsik Shin
Guest Editor

Manuscript Submission Information

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Keywords

  • Ambulatory monitoring for healthcare
  • Implantable device for mobile health
  • Intelligent connectivity in mobile healthcare
  • Internet-of-Things for healthcare application
  • Mobile healthcare
  • Pervasive healthcare
  • Remote health monitoring
  • Telehealth
  • Wearable healthcare

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Published Papers (3 papers)

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Research

18 pages, 4601 KiB  
Article
An On-Device Learning System for Estimating Liquid Consumption from Consumer-Grade Water Bottles and Its Evaluation
by Avirup Roy, Hrishikesh Dutta, Henry Griffith and Subir Biswas
Sensors 2022, 22(7), 2514; https://doi.org/10.3390/s22072514 - 25 Mar 2022
Cited by 5 | Viewed by 3376
Abstract
A lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estimation with the result of two tasks: the detection of [...] Read more.
A lightweight on-device liquid consumption estimation system involving an energy-aware machine learning algorithm is developed in this work. This system consists of two separate on-device neural network models that carry out liquid consumption estimation with the result of two tasks: the detection of sip from gestures with which the bottle is handled by its user and the detection of first sips after a bottle refill. This predictive volume estimation framework incorporates a self-correction mechanism that can minimize the error after each bottle fill-up cycle, which makes the system robust to errors from the sip classification module. In this paper, a detailed characterization of sip detection is performed to understand the accuracy-complexity tradeoffs by developing and implementing a variety of different ML models with varying complexities. The maximum energy consumed by the entire framework is around 119 mJ during a maximum computation time of 300 μs. The energy consumption and computation times of the proposed framework is suitable for implementation in low-power embedded hardware that can be incorporated in consumer grade water bottles. Full article
(This article belongs to the Special Issue Mobile Healthcare Based on IoT)
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11 pages, 2140 KiB  
Article
Recurrence Plot and Machine Learning for Signal Quality Assessment of Photoplethysmogram in Mobile Environment
by Donggeun Roh and Hangsik Shin
Sensors 2021, 21(6), 2188; https://doi.org/10.3390/s21062188 - 20 Mar 2021
Cited by 29 | Viewed by 4081
Abstract
The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring complicated pre-processing. Photoplethysmograms were recorded [...] Read more.
The purpose of this study was to develop a machine learning model that could accurately evaluate the quality of a photoplethysmogram based on the shape of the photoplethysmogram and the phase relevance in a pulsatile waveform without requiring complicated pre-processing. Photoplethysmograms were recorded for 76 participants (5 min for each participant). All recorded photoplethysmograms were segmented for each beat to obtain a total of 49,561 pulsatile segments. These pulsatile segments were manually labeled as ‘good’ and ‘poor’ classes and converted to a two-dimensional phase space trajectory image using a recurrence plot. The classification model was implemented using a convolutional neural network with a two-layer structure. As a result, the proposed model correctly classified 48,827 segments out of 49,561 segments and misclassified 734 segments, showing a balanced accuracy of 0.975. Sensitivity, specificity, and positive predictive values of the developed model for the test dataset with a ‘poor’ class classification were 0.964, 0.987, and 0.848, respectively. The area under the curve was 0.994. The convolutional neural network model with recurrence plot as input proposed in this study can be used for signal quality assessment as a generalized model with high accuracy through data expansion. It has an advantage in that it does not require complicated pre-processing or a feature detection process. Full article
(This article belongs to the Special Issue Mobile Healthcare Based on IoT)
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15 pages, 6468 KiB  
Article
Adaptive Fine Distortion Correction Method for Stereo Images of Skin Acquired with a Mobile Phone
by Cho-I Moon and Onseok Lee
Sensors 2020, 20(16), 4492; https://doi.org/10.3390/s20164492 - 11 Aug 2020
Cited by 3 | Viewed by 2980
Abstract
With the development of the mobile phone, we can acquire high-resolution images of the skin to observe its detailed features using a mobile camera. We acquire stereo images using a mobile camera to enable a three-dimensional (3D) analysis of the skin surface. However, [...] Read more.
With the development of the mobile phone, we can acquire high-resolution images of the skin to observe its detailed features using a mobile camera. We acquire stereo images using a mobile camera to enable a three-dimensional (3D) analysis of the skin surface. However, geometric changes in the observed skin structure caused by the lens distortion of the mobile phone result in a low accuracy of the 3D information extracted through stereo matching. Therefore, our study proposes a Distortion Correction Matrix (DCM) to correct the fine distortion of close-up mobile images, pixel by pixel. We verified the correction performance by analyzing the results of correspondence point matching in the stereo image corrected using the DCM. We also confirmed the correction results of the image taken at the five different working distances and derived a linear regression model for the relationship between the angle of the image and the distortion ratio. The proposed DCM considers the distortion degree, which appears to be different in the left and right regions of the image. Finally, we performed a fine distortion correction, which is difficult to check with the naked eye. The results of this study can enable the accurate and precise 3D analysis of the skin surface using corrected mobile images. Full article
(This article belongs to the Special Issue Mobile Healthcare Based on IoT)
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