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AI-based Sensing for Health Monitoring and Medical Diagnosis
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AI-based Sensing for Health Monitoring and Medical Diagnosis

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 3605

Special Issue Editor

Prof. Dr. George D. Magoulas

E-Mail Website
Guest Editor
Birkbeck Knowledge Lab, School of Computing and Mathematical Sciences, Birkbeck College, University of London, London WC1E 7HX, UK
Interests: computational models of learning and cognition; artificial neural networks and deep learning; evolutionary computing; learning technologies; bio-inspired machine learning; software engineering for AI and machine learning systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI-based sensing for health monitoring and medical diagnosis has the potential to reform healthcare provision, improving patients’ care while reducing costs. AI-based sensors are being developed to monitor, measure, analyse and interpret vital signs such as heart rate, blood pressure, respiration rate and oxygen levels without direct contact with the patient. Sensing technology enables patients’ tracking over time, and AI methods can inform medical decision making by healthcare professionals by recognising early trends or abnormalities that can potentially indicate a health issue before it becomes serious. AI-based sensors also allow for the remote monitoring of patients with chronic conditions such as diabetes or hypertension, and can be used for the early detection of infectious diseases such as influenza or coronavirus. In this context, AI and sophisticated data analysis methods enable identifying patterns from large amounts of data collected by various sources, such as wearables or medical records, and can help both researchers and clinicians discover new insights into disease prevention and treatment, and make preventive care decisions. Innovations in this area can ultimately enhance the decision making capabilities of healthcare professionals, and lead to personalised treatment plans based on individual data points, offering patients feedback about their treatment and health status over long periods of time.

Prof. Dr. George Magoulas
Guest Editor

Manuscript Submission Information

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Keywords

  • AI and machine learning for health monitoring
  • healthcare analytics
  • AI-enabled medical sensors
  • AI-assisted clinical decisions
  • AI-enabled monitoring of digital biomarkers
  • AI in medical sensors for the diagnosis and management of disease
  • medical decision making

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Published Papers (2 papers)

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Research

16 pages, 11754 KiB  
Article
Assessment System for Child Head Injury from Falls Based on Neural Network Learning
by Ziqian Yang, Baiyu Tsui and Zhihui Wu
Sensors 2023, 23(18), 7896; https://doi.org/10.3390/s23187896 - 15 Sep 2023
Cited by 2 | Viewed by 1273
Abstract
Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system [...] Read more.
Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from falls. Two phases are involved in processing the framework: In phase I, the data of joints is obtained by processing surveillance video with Open Pose. The long short-term memory (LSTM) network and 3D transform model are then used to integrate key spots’ frame space and time information. In phase II, the head acceleration is derived and inserted into the HIC value calculation, and a classification model is developed to assess the injury. We collected 200 RGB-captured daily films of 13- to 30-month-old toddlers playing near furniture edges, guardrails, and upside-down falls. Five hundred video clips extracted from these are divided in an 8:2 ratio into a training and validation set. We prepared an additional collection of 300 video clips (test set) of toddlers’ daily falling at home from their parents to evaluate the framework’s performance. The experimental findings revealed a classification accuracy of 96.67%. The feasibility of a real-time AI technique for assessing head injuries in falls through monitoring was proven. Full article
(This article belongs to the Special Issue AI-based Sensing for Health Monitoring and Medical Diagnosis)
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25 pages, 5678 KiB  
Article
MAE-Based Self-Supervised Pretraining Algorithm for Heart Rate Estimation of Radar Signals
by Yashan Xiang, Jian Guo, Ming Chen, Zheyu Wang and Chong Han
Sensors 2023, 23(18), 7869; https://doi.org/10.3390/s23187869 - 13 Sep 2023
Cited by 4 | Viewed by 1749
Abstract
Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods [...] Read more.
Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the “masked phase autoencoders with a vision transformer network” (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage—it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time. Full article
(This article belongs to the Special Issue AI-based Sensing for Health Monitoring and Medical Diagnosis)
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