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Sensors, Systems, and AI for Healthcare II
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Sensors, Systems, and AI for Healthcare II

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

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 32055

Special Issue Editors

Prof. Dr. Amir Aminifar

E-Mail Website
Guest Editor
Uppsala University, Sweden
Interests: Wearables; artificial intelligence and machine learning; and security in health/medical applications
Dr. Nima TaheriNejad

E-Mail Website
Guest Editor
TU Wien (Austria), Gußhausstraße 27-29, 1040 Vienna, Austria
Interests: Wearable healthcare; computational self-awareness; affective computing; and embedded system design
Special Issues, Collections and Topics in MDPI journals
Dr. Paolo Perego

E-Mail Website
Guest Editor
Dipartimento di Design, Politecnico di Milano, Via Candiani 72, 20158 Milano, Italy
Interests: wearables; human–computer interaction; UX design rehabilitation; tele-medicine; mobile health
Special Issues, Collections and Topics in MDPI journals
Dr. Amir M. Rahmani

E-Mail Website
Guest Editor
Department of Computer Science, University of California, Irvine, CA 92697-3435, USA
Interests: e-Health; wearable Internet-of-Things; healthcare/nursing informatics; ubiquitous computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nanostructured materials represent a vibrant area of research and a techno-economic sector with full expansion in many application domains. In particular, in the field of optical biosensors, nanostructured materials have gained prominence in technological advancements due to their tunable physical characteristics in manipulating light-biological matter interaction resulting in enhanced performance with respect to their bulk counterparts. The final goal of this Special Issue is to provide novel and smart optical biosensing approaches based on nanostructured materials. The Special Issue will focus on two biological topics: cancer related biomarkers and virus detection. Cancer biomarkers are a wide range of biochemical entities, such as nucleic acids, proteins, sugars, small metabolites, and cytokinetic parameters, as well as entire tumour cells found in body fluids. They are routinely used in clinical environment for diagnosis, prognosis, and the prediction of treatment efficacy and recurrence. The importance of virus and viral proteins detection is dramatically demonstrated by our recent experiences. In particular, the health emergency related to SARS-CoV-2 put in evidence our inadequate response in terms of smart biosensing solutions for mass screening.

This Special Issue of Sensors welcomes both reviews and original research articles on the field of new nanostructured materials for optical biosensing. Topics include, but are not restricted to, plasmonic configurations, photonic crystals-based biosensors, metamaterials and metasurfaces for optical biosensing, label-free and/or fluorescence optical platforms, such as lab-on-a-chip and optical fiber sensing based on nanostructures. Smart bioreceptor immobilization procedures and their integration into nanostructured optical sensors are also of interest.

Prof. Dr. Amir Aminifar
Dr. Nima TaheriNejad
Dr. Amir Rahmani
Dr. Paolo Perego
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.

Keywords

  • wearable sensors
  • mobile healthcare devices
  • machine-learning for health
  • artificial intelligence for health
  • security and privacy in health applications and technologies

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Published Papers (6 papers)

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22 pages, 8762 KiB  
Article
Enabling Security Services in Socially Assistive Robot Scenarios for Healthcare Applications
by Alexandru Vulpe, Răzvan Crăciunescu, Ana-Maria Drăgulinescu, Sofoklis Kyriazakos, Ali Paikan and Pouyan Ziafati
Sensors 2021, 21(20), 6912; https://doi.org/10.3390/s21206912 - 18 Oct 2021
Cited by 14 | Viewed by 3907
Abstract
Today’s IoT deployments are highly complex, heterogeneous and constantly changing. This poses severe security challenges such as limited end-to-end security support, lack of cross-platform cross-vertical security interoperability as well as the lack of security services that can be readily applied by security practitioners [...] Read more.
Today’s IoT deployments are highly complex, heterogeneous and constantly changing. This poses severe security challenges such as limited end-to-end security support, lack of cross-platform cross-vertical security interoperability as well as the lack of security services that can be readily applied by security practitioners and third party developers. Overall, these require scalable, decentralized and intelligent IoT security mechanisms and services which are addressed by the SecureIoT project. This paper presents the definition, implementation and validation of a SecureIoT-enabled socially assisted robots (SAR) usage scenario. The aim of the SAR scenario is to integrate and validate the SecureIoT services in the scope of personalized healthcare and ambient assistive living (AAL) scenarios, involving the integration of two AAL platforms, namely QTrobot (QT) and CloudCare2U (CC2U). This includes risk assessment of communications security, predictive analysis of security risks, implementing access control policies to enhance the security of solution, and auditing of the solution against security, safety and privacy guidelines and regulations. Future perspectives include the extension of this security paradigm by securing the integration of healthcare platforms with IoT solutions, such as Healthentia with QTRobot, by means of a system product assurance process for cyber-security in healthcare applications, through the PANACEA toolkit. Full article
(This article belongs to the Special Issue Sensors, Systems, and AI for Healthcare II)
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25 pages, 10016 KiB  
Article
Recognition of Fine-Grained Walking Patterns Using a Smartwatch with Deep Attentive Neural Networks
by Hyejoo Kim, Hyeon-Joo Kim, Jinyoon Park, Jeh-Kwang Ryu and Seung-Chan Kim
Sensors 2021, 21(19), 6393; https://doi.org/10.3390/s21196393 - 24 Sep 2021
Cited by 13 | Viewed by 3854
Abstract
Generally, people do various things while walking. For example, people frequently walk while looking at their smartphones. Sometimes we walk differently than usual; for example, when walking on ice or snow, we tend to waddle. Understanding walking patterns could provide users with contextual [...] Read more.
Generally, people do various things while walking. For example, people frequently walk while looking at their smartphones. Sometimes we walk differently than usual; for example, when walking on ice or snow, we tend to waddle. Understanding walking patterns could provide users with contextual information tailored to the current situation. To formulate this as a machine-learning problem, we defined 18 different everyday walking styles. Noting that walking strategies significantly affect the spatiotemporal features of hand motions, e.g., the speed and intensity of the swinging arm, we propose a smartwatch-based wearable system that can recognize these predefined walking styles. We developed a wearable system, suitable for use with a commercial smartwatch, that can capture hand motions in the form of multivariate timeseries (MTS) signals. Then, we employed a set of machine learning algorithms, including feature-based and recent deep learning algorithms, to learn the MTS data in a supervised fashion. Experimental results demonstrated that, with recent deep learning algorithms, the proposed approach successfully recognized a variety of walking patterns, using the smartwatch measurements. We analyzed the results with recent attention-based recurrent neural networks to understand the relative contributions of the MTS signals in the classification process. Full article
(This article belongs to the Special Issue Sensors, Systems, and AI for Healthcare II)
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29 pages, 957 KiB  
Article
Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions
by Laura Martínez-Delgado, Mario Munoz-Organero and Paula Queipo-Alvarez
Sensors 2021, 21(16), 5273; https://doi.org/10.3390/s21165273 - 4 Aug 2021
Cited by 7 | Viewed by 3198
Abstract
Diabetes is a chronic disease caused by the inability of the pancreas to produce insulin or problems in the body to use it efficiently. It is one of the fastest growing health challenges affecting more than 400 million people worldwide, according to the [...] Read more.
Diabetes is a chronic disease caused by the inability of the pancreas to produce insulin or problems in the body to use it efficiently. It is one of the fastest growing health challenges affecting more than 400 million people worldwide, according to the World Health Organization. Intensive research is being carried out on artificial intelligence methods to help people with diabetes to optimize the way in which they use insulin, carbohydrate intakes, or physical activity. By predicting upcoming levels of blood glucose concentrations, preventive actions can be taken. Previous research studies using machine learning methods for blood glucose level predictions have mainly focused on the machine learning model used. Little attention has been given to the pre-processing of insulin and carbohydrate signals in order to mimic the human absorption processes. In this manuscript, a recurrent neural network (RNN) based model for predicting upcoming blood glucose levels in people with type 1 diabetes is combined with several carbohydrate and insulin absorption curves in order to optimize the prediction results. The proposed method is applied to data from real patients suffering type 1 diabetes mellitus (T1DM). The achieved results are encouraging, obtaining accuracy levels around 0.510 mmol/L (9.2 mg/dl) in the best scenario. Full article
(This article belongs to the Special Issue Sensors, Systems, and AI for Healthcare II)
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13 pages, 3388 KiB  
Article
A Raspberry Pi-Based Traumatic Brain Injury Detection System for Single-Channel Electroencephalogram
by Navjodh Singh Dhillon, Agustinus Sutandi, Manoj Vishwanath, Miranda M. Lim, Hung Cao and Dong Si
Sensors 2021, 21(8), 2779; https://doi.org/10.3390/s21082779 - 15 Apr 2021
Cited by 16 | Viewed by 5703
Abstract
Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with [...] Read more.
Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16–64 s epochs for TBI vs. control conditions. This work can enable the development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems. Full article
(This article belongs to the Special Issue Sensors, Systems, and AI for Healthcare II)
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21 pages, 985 KiB  
Article
Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation
by Fatemeh Sarhaddi, Iman Azimi, Sina Labbaf, Hannakaisa Niela-Vilén, Nikil Dutt, Anna Axelin, Pasi Liljeberg and Amir M. Rahmani
Sensors 2021, 21(7), 2281; https://doi.org/10.3390/s21072281 - 24 Mar 2021
Cited by 53 | Viewed by 13718
Abstract
Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother’s and her unborn baby’s health and well-being. Different studies [...] Read more.
Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother’s and her unborn baby’s health and well-being. Different studies have thus far proposed maternal health monitoring systems. However, they are designed for a specific health problem or are limited to questionnaires and short-term data collection methods. Moreover, the requirements and challenges have not been evaluated in long-term studies. Maternal health necessitates a comprehensive framework enabling continuous monitoring of pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system to provide ubiquitous maternal health monitoring during pregnancy and postpartum. The system consists of various data collectors to track the mother’s condition, including stress, sleep, and physical activity. We carried out the full system implementation and conducted a real human subject study on pregnant women in Southwestern Finland. We then evaluated the system’s feasibility, energy efficiency, and data reliability. Our results show that the implemented system is feasible in terms of system usage during nine months. We also indicate the smartwatch, used in our study, has acceptable energy efficiency in long-term monitoring and is able to collect reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system. Full article
(This article belongs to the Special Issue Sensors, Systems, and AI for Healthcare II)
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5 pages, 174 KiB  
Comment
Comment on Martínez-Delgado et al. Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions. Sensors 2021, 21, 5273
by Josiah Z. R. Misplon, Varun Saini, Brianna P. Sloves, Sarah H. Meerts and David R. Musicant
Sensors 2024, 24(13), 4361; https://doi.org/10.3390/s24134361 - 5 Jul 2024
Viewed by 831
Abstract
The paper “Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions” (Sensors 2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diabetes mellitus (T1DM). By [...] Read more.
The paper “Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions” (Sensors 2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diabetes mellitus (T1DM). By building exponential models from raw carbohydrate and insulin data to simulate the absorption in the body, the authors reported a reduction in their model’s root-mean-square error (RMSE) from 15.5 mg/dL (raw) to 9.2 mg/dL (exponential) when predicting blood glucose levels one hour into the future. In this comment, we demonstrate that the experimental techniques used in that paper are flawed, which invalidates its results and conclusions. Specifically, after reviewing the authors’ code, we found that the model validation scheme was malformed, namely, the training and test data from the same time intervals were mixed. This means that the reported RMSE numbers in the referenced paper did not accurately measure the predictive capabilities of the approaches that were presented. We repaired the measurement technique by appropriately isolating the training and test data, and we discovered that their models actually performed dramatically worse than was reported in the paper. In fact, the models presented in the that paper do not appear to perform any better than a naive model that predicts future glucose levels to be the same as the current ones. Full article
(This article belongs to the Special Issue Sensors, Systems, and AI for Healthcare II)
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