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Wearable Medical Sensors and Artificial Intelligence
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Wearable Medical Sensors and Artificial Intelligence

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 5683

Special Issue Editor

Dr. Leonardo D'Acquisto

E-Mail Website
Guest Editor
Department of Enigineering, Università degli Studi di Palermo, Piazza Marina, 61, 90133 Palermo PA, Italy
Interests: mechanical engineering; measurements

Special Issue Information

Dear Colleagues,

Wearable technologies offer a convenient means of monitoring many physiological features, presenting a multitude of medical solutions. Not only are these devices easy for the consumer to use, but they offer real-time data for physicians to analyze as well. From the Apple Watch’s EKG capabilities to new continuous glucose monitoring systems, wearable medical technologies have a wide range of potential applications in healthcare. For example, an artificial intelligence (AI)-powered wearable device that measures multiple vital signs has recently received FDA clearance for patients to use at home.

This Special Issue is concerned with the application of medical wearables to health monitoring, including but not limited to the following topics:

  • Intelligent medical data sensing and processing for distributed monitoring;
  • EEG/ECG sensors and brain interfacing;
  • VR/AR sensors, mixed reality, and data visualization;
  • Advances of medical sensor data fusion;
  • Internet of medical things;
  • Robot-assisted wearable sensors for healthcare;
  • Highly sensitive and specific sensor interface circuits and signal processing;
  • Artificial intelligence algorithms/circuits and for sensor reasoning, classification and decision.

Dr. Leonardo D'Acquisto
Guest Editor

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

18 pages, 9968 KiB  
Article
Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators
by Dulan Perera, Yu-Kai Wang, Chin-Teng Lin, Hung Nguyen and Rifai Chai
Sensors 2022, 22(16), 6230; https://doi.org/10.3390/s22166230 - 19 Aug 2022
Cited by 12 | Viewed by 2069
Abstract
This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment [...] Read more.
This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger–Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions. Full article
(This article belongs to the Special Issue Wearable Medical Sensors and Artificial Intelligence)
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28 pages, 13568 KiB  
Article
AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements
by Asad Muhammad Butt, Hassan Alsaffar, Muhannad Alshareef and Khurram Karim Qureshi
Sensors 2022, 22(8), 3085; https://doi.org/10.3390/s22083085 - 18 Apr 2022
Cited by 4 | Viewed by 3016
Abstract
Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in [...] Read more.
Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87–93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10–20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065–0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models. Full article
(This article belongs to the Special Issue Wearable Medical Sensors and Artificial Intelligence)
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