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Sensors Fusion in Digital Healthcare Applications

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

Deadline for manuscript submissions: 10 August 2024 | Viewed by 9816

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Dr. Boon Giin Lee

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School of Computer Science, Faculty of Science and Engineering, The University of Nottingham Ningbo China, Ningbo 315100, China
Interests: human–computer interaction; sensors application; computer vision
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Special Issue Information

Dear Colleagues,

A multi-sensor fusion field is evolving, and its applications in healthcare are substantial. The principle of sensor fusion is utilized for integrating multi-source and multi-modality health data to improve health monitoring and diagnostics. Additionally, sensors are getting smaller and cheaper, allowing them to be integrated into intelligent and autonomous healthcare systems. Data fusion methods used in the Internet-of-Things enable "smart" healthcare systems, including physiological, behavioral, and social aspects monitoring, for improving quality of life, health, and well-being. Additionally, sensor fusion is becoming more and more relevant in artificial intelligence (AI) research.

This Special Issue invites submissions of original research and novel work on sensors fusion for digital healthcare applications, covering a wide range of areas such as:

Multi-modality Sensors in Healthcare;
Health Rehabilitation;
Virtual Reality, Augmented Reality, Mixed Reality in Healthcare;
Education and Learning for Digital Health;
Health and Safety Risk Assessment;
Wireless Sensors Network for Healthcare;
Smart Health Diagnostics;
Environmental Effects on Public Health;
Robotics in Healthcare;
Internet-of-Things in Healthcare;
Smart Wearable Healthcare;
Integrated Healthcare Communication Platform;
Sensor Fusion in Biomedical Imaging;
Remote Sensing in Healthcare;
Healthcare in Transportation Systems.

Dr. Boon Giin Lee
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 (5 papers)

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Research

26 pages, 3507 KiB

Open AccessArticle

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

by Christina Garcia and Sozo Inoue

Sensors 2024, 24(2), 319; https://doi.org/10.3390/s24020319 - 05 Jan 2024

Viewed by 692

Abstract

In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different beacons by relabeling to the locations with less samples, resolving data imbalance. Standard deviation and Kullback–Leibler divergence between minority and majority classes are used to measure signal pattern to find matching beacons to relabel. By matching beacons between classes, two variations of relabeling are implemented, specifically full and partial matching. The performance is evaluated using the real-world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. A Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the beacon data with our proposed relabeling method for data augmentation, we achieve a higher minority class F1-score compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected beacon data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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22 pages, 6332 KiB

Open AccessArticle

Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study

by Qicheng Chen and Boon Giin Lee

Sensors 2023, 23(13), 6099; https://doi.org/10.3390/s23136099 - 02 Jul 2023

Cited by 2 | Viewed by 2474

Abstract

Due to the phenomenon of “involution” in China, the current generation of college and university students are experiencing escalating levels of stress, both academically and within their families. Extensive research has shown a strong correlation between heightened stress levels and overall well-being decline. Therefore, monitoring students’ stress levels is crucial for improving their well-being in educational institutions and at home. Previous studies have primarily focused on recognizing emotions and detecting stress using physiological signals like ECG and EEG. However, these studies often relied on video clips to induce various emotional states, which may not be suitable for university students who already face additional stress to excel academically. In this study, a series of experiments were conducted to evaluate students’ stress levels by engaging them in playing Sudoku games under different distracting conditions. The collected physiological signals, including PPG, ECG, and EEG, were analyzed using enhanced models such as LRCN and self-supervised CNN to assess stress levels. The outcomes were compared with participants’ self-reported stress levels after the experiments. The findings demonstrate that the enhanced models presented in this study exhibit a high level of proficiency in assessing stress levels. Notably, when subjects were presented with Sudoku-solving tasks accompanied by noisy or discordant audio, the models achieved an impressive accuracy rate of 95.13% and an F1-score of 93.72%. Additionally, when subjects engaged in Sudoku-solving activities with another individual monitoring the process, the models achieved a commendable accuracy rate of 97.76% and an F1-score of 96.67%. Finally, under comforting conditions, the models achieved an exceptional accuracy rate of 98.78% with an F1-score of 95.39%. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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13 pages, 499 KiB

Open AccessArticle

Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques

by José A. González-Nóvoa, Laura Busto, Silvia Campanioni, José Fariña, Juan J. Rodríguez-Andina, Dolores Vila and César Veiga

Sensors 2023, 23(3), 1162; https://doi.org/10.3390/s23031162 - 19 Jan 2023

Cited by 2 | Viewed by 1661

Abstract

Due to the high occupational pressure suffered by intensive care units (ICUs), a correct estimation of the patients’ length of stay (LoS) in the ICU is of great interest to predict possible situations of collapse, to help healthcare personnel to select appropriate treatment options and to predict patients’ conditions. There has been a high amount of data collected by biomedical sensors during the continuous monitoring process of patients in the ICU, so the use of artificial intelligence techniques in automatic LoS estimation would improve patients’ care and facilitate the work of healthcare personnel. In this work, a novel methodology to estimate the LoS using data of the first 24 h in the ICU is presented. To achieve this, XGBoost, one of the most popular and efficient state-of-the-art algorithms, is used as an estimator model, and its performance is optimized both from computational and precision viewpoints using Bayesian techniques. For this optimization, a novel two-step approach is presented. The methodology was carefully designed to execute codes on a high-performance computing system based on graphics processing units, which considerably reduces the execution time. The algorithm scalability is analyzed. With the proposed methodology, the best set of XGBoost hyperparameters are identified, estimating LoS with a MAE of 2.529 days, improving the results reported in the current state of the art and probing the validity and utility of the proposed approach. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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9 pages, 598 KiB

Open AccessArticle

The Trade-Off between Airborne Pandemic Control and Energy Consumption Using Air Ventilation Solutions

by Ariel Alexi, Ariel Rosenfeld and Teddy Lazebnik

Sensors 2022, 22(22), 8594; https://doi.org/10.3390/s22228594 - 08 Nov 2022

Cited by 2 | Viewed by 1276

Abstract

Airborne diseases cause high mortality and adverse socioeconomic consequences. Due to urbanization, more people spend more time indoors. According to recent research, air ventilation reduces long-range airborne transmission in indoor settings. However, air ventilation solutions often incur significant energy costs and ecological footprints. The trade-offs between energy consumption and pandemic control indoors have not yet been thoroughly analyzed. In this work, we use advanced sensors to monitor the energy consumption and pandemic control capabilities of an air-conditioning system, a pedestal fan, and an open window in hospital rooms, classrooms, and conference rooms. A simulation of an indoor airborne pandemic spread of Coronavirus (COVID-19) is used to analyze the Pareto front. For the three examined room types, the Pareto front consists of all three air ventilation solutions, with some ventilation configurations demonstrating significant inefficiencies. Specifically, air-conditioning is found to be efficient only at a very high energy cost and fans seem to pose a reasonable alternative. To conclude, a more informed ventilation policy can bring about a more desirable compromise between energy consumption and pandemic spread control. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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22 pages, 2952 KiB

Open AccessArticle

A Sensor and Machine Learning-Based Sensory Management Recommendation System for Children with Autism Spectrum Disorders

by Lingling Deng and Prapa Rattadilok

Sensors 2022, 22(15), 5803; https://doi.org/10.3390/s22155803 - 03 Aug 2022

Cited by 6 | Viewed by 2470

Abstract

Sensory processing issues are one of the most common issues observed in autism spectrum disorders (ASD). Technologies that could address the issue serve a more and more important role in interventions for ASD individuals nowadays. In this study, a sensory management recommendation system was developed and tested to help ASD children deal with atypical sensory responses in class. The system employed sensor fusion and machine learning techniques to identify distractions, anxious situations, and the potential causes of these in the surroundings. Another novelty of the system included a sensory management strategy making a module based on fuzzy logic, which generated alerts to inform teachers and caregivers about children’s states and risky environmental factors. Sensory management strategies were recommended to help improve children’s attention or calm children down. The evaluation results suggested that the use of the system had a positive impact on children’s performance and its design was user-friendly. The sensory management recommendation system could work as an intelligent companion for ASD children that helps with their in-class performance by recommending management strategies in relation to the real-time information about the children’s environment. Full article

(This article belongs to the Special Issue Sensors Fusion in Digital Healthcare Applications)

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