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Intelligent Sensors for Healthcare and Patient Monitoring
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Intelligent Sensors for Healthcare and Patient Monitoring

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 5982

Special Issue Editor

Dr. Mehdi Boukallel

E-Mail Website
Guest Editor
CEA LIST, Ambient Intelligence and Interactive Systems Department, Sensory and Ambient Interfaces Laboratory, 91191 Palaiseau, France
Interests: intelligent sensors; medical computing; medical disorders; microsensors; patient monitoring; patient rehabilitation; patient treatment; force sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the development of intelligent sensors for healthcare and patient monitoring has gained significant attention. These sensors are capable of collecting and analyzing data from patients. They can be used in a variety of healthcare settings, including hospitals, clinics, and even at home. They are used to monitor vital signs such as heart rate, blood pressure, and oxygen saturation levels while providing healthcare professionals with continuous and accurate data on a patient’s health status. This real-time monitoring can improve patient outcomes by enabling early detection and intervention in critical situations.

Intelligent sensors can also be used to monitor patients with chronic conditions such as diabetes, asthma, and COPD. By continuously tracking patients’ symptoms and medication adherence, healthcare professionals can better manage these conditions by providing personalized care plans. Finally, intelligent sensors can help reduce healthcare costs by reducing hospital readmissions. By providing remote patient monitoring, healthcare professionals can detect and address potential issues before they become serious, ultimately improving patient outcomes and reducing the need for hospitalization.

However, there are some challenges associated with the use of intelligent sensors in healthcare. Privacy concerns must be addressed to ensure that patient data are protected and used ethically. In addition, healthcare professionals must be trained to interpret and act on the data collected by these sensors

Dr. Mehdi Boukallel
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.

Keywords

  • intelligent sensors for vital signs monitoring and chronic conditions (stroke, diabetes, asthma, and COPD)
  • intelligent sensors for gait analysis or motion analysis
  • wearable sensors
  • flexible, soft, and e-textile sensors
  • AI for healthcare applications
  • efficient AI models trained on small data set
  • privacy and normalization
  • sensor fusion
  • remote patient monitoring
  • wireless sensors

Published Papers (7 papers)

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Research

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14 pages, 312 KiB  
Article
Automated Generation of Clinical Reports Using Sensing Technologies with Deep Learning Techniques
by Celia Cabello-Collado, Javier Rodriguez-Juan, David Ortiz-Perez, Jose Garcia-Rodriguez, David Tomás and Maria Flores Vizcaya-Moreno
Sensors 2024, 24(9), 2751; https://doi.org/10.3390/s24092751 - 25 Apr 2024
Viewed by 217
Abstract
This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, [...] Read more.
This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient–doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes. Full article
(This article belongs to the Special Issue Intelligent Sensors for Healthcare and Patient Monitoring)
24 pages, 538 KiB  
Article
Call to Action: Investigating Interaction Delay in Smartphone Notifications
by Michael Stach, Lena Mulansky, Manfred Reichert, Rüdiger Pryss and Felix Beierle
Sensors 2024, 24(8), 2612; https://doi.org/10.3390/s24082612 - 19 Apr 2024
Viewed by 262
Abstract
Notifications are an essential part of the user experience on smart mobile devices. While some apps have to notify users immediately after an event occurs, others can schedule notifications strategically to notify them only on opportune moments. This tailoring allows apps to shorten [...] Read more.
Notifications are an essential part of the user experience on smart mobile devices. While some apps have to notify users immediately after an event occurs, others can schedule notifications strategically to notify them only on opportune moments. This tailoring allows apps to shorten the users’ interaction delay. In this paper, we present the results of a comprehensive study that identified the factors that influence users’ interaction delay to their smartphone notifications. We analyzed almost 10 million notifications collected in-the-wild from 922 users and computed their response times with regard to their demographics, their Big Five personality trait scores and the device’s charging state. Depending on the app category, the following tendencies can be identified over the course of the day: Most notifications were logged in late morning and late afternoon. This number decreases in the evening, between 8 p.m. and 11 p.m., and at the same time exhibits the lowest average interaction delays at daytime. We also found that the user’s sex and age is significantly associated with the response time. Based on the results of our study, we encourage developers to incorporate more information on the user and the executing device in their notification strategy to notify users more effectively. Full article
(This article belongs to the Special Issue Intelligent Sensors for Healthcare and Patient Monitoring)
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19 pages, 2291 KiB  
Article
Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease
by Tianze Yu, Kye Won Park, Martin J. McKeown and Z. Jane Wang
Sensors 2023, 23(22), 9149; https://doi.org/10.3390/s23229149 - 13 Nov 2023
Viewed by 1038
Abstract
The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson’s Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified [...] Read more.
The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson’s Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified Parkinson’s Disease Rating Scale (UPDRS) and its variations serve as the primary clinical tools for evaluating motor symptoms in PD, but are time-intensive and prone to inter-rater variability. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping, a UPDRS task to assess bradykinesia. However, these methods often use abstract features that are not closely related to clinical experience. In this paper, we introduce a customized machine learning approach for the automated scoring of UPDRS bradykinesia using single-view RGB videos of finger tapping, based on the extraction of detailed features that rigorously conform to the established UPDRS guidelines. We applied the method to 75 videos from 50 PD patients collected in both a laboratory and a realistic clinic environment. The classification performance agreed well with expert assessors, and the features selected by the Decision Tree aligned with clinical knowledge. Our proposed framework was designed to remain relevant amid ongoing patient recruitment and technological progress. The proposed approach incorporates features that closely resonate with clinical reasoning and shows promise for clinical implementation in the foreseeable future. Full article
(This article belongs to the Special Issue Intelligent Sensors for Healthcare and Patient Monitoring)
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13 pages, 637 KiB  
Article
User Perspectives of Geriatric German Patients on Smart Sensor Technology in Healthcare
by Marcin Orzechowski, Tobias Skuban-Eiseler, Anna Ajlani, Ulrich Lindemann, Jochen Klenk and Florian Steger
Sensors 2023, 23(22), 9124; https://doi.org/10.3390/s23229124 - 11 Nov 2023
Viewed by 1103
Abstract
With consideration of the progressing aging of our societies, the introduction of smart sensor technology can contribute to the improvement of healthcare for older patients and to reductions of the costs of care. From the clinical and medico-ethical points of view, the advantages [...] Read more.
With consideration of the progressing aging of our societies, the introduction of smart sensor technology can contribute to the improvement of healthcare for older patients and to reductions of the costs of care. From the clinical and medico-ethical points of view, the advantages of smart sensor technology are copious. However, any ethical evaluation of an introduction of a new technology in medical practice requires an inclusion of patients’ perspectives and their assessments. We have conducted qualitative, semi-structured, exploratory interviews with 11 older patients in order to gain their subjective opinions on the use of smart sensor devices for rehabilitation purposes. The interviews were analyzed using methods of qualitative content and thematic analyses. In our analysis, we have focused on ethical aspects of adoption of this technology in clinical practice. Most of the interviewees expressed their trust in this technology, foremost because of its accuracy. Several respondents stated apprehension that the use of smart sensors will lead to a change in the patient–healthcare professional relationship. Regarding costs of introduction of smart sensors into healthcare, interviewees were divided between health insurance bearing the costs and individual participation in corresponding costs. Most interviewees had no concerns about the protection of their privacy or personal information. Considering these results, improvement of users’ technology literacy regarding possible threats connected with putting smart sensors into clinical practice is a precondition to any individual application of smart sensors. This should occur in the form of extended and well-designed patient information adapted to individual levels of understanding. Moreover, application of smart sensors needs to be accompanied with careful anamnesis of patient’s needs, life goals, capabilities, and concerns. Full article
(This article belongs to the Special Issue Intelligent Sensors for Healthcare and Patient Monitoring)
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14 pages, 487 KiB  
Article
Automated Sleep Stage Classification in Home Environments: An Evaluation of Seven Deep Neural Network Architectures
by Jaime Yeckle and Vidya Manian
Sensors 2023, 23(21), 8942; https://doi.org/10.3390/s23218942 - 03 Nov 2023
Viewed by 677
Abstract
Sleep is an essential human physiological need that has garnered increasing scientific attention due to the burgeoning prevalence of sleep-related disorders and their impact on public health. Among contemporary challenges, the demand for authentic sleep monitoring outside the confines of specialized laboratories, ideally [...] Read more.
Sleep is an essential human physiological need that has garnered increasing scientific attention due to the burgeoning prevalence of sleep-related disorders and their impact on public health. Among contemporary challenges, the demand for authentic sleep monitoring outside the confines of specialized laboratories, ideally within the home environment, has arisen. Addressing this, we explore the development of pragmatic approaches that facilitate implementation within domestic settings. Such approaches necessitate the deployment of streamlined, computationally efficient automated classifiers. In pursuit of a sleep stage classifier tailored for home use, this study rigorously assessed seven conventional neural network architectures prominent in deep learning (LeNet, ResNet, VGG, MLP, LSTM-CNN, LSTM, BLSTM). Leveraging sleep recordings from a cohort of 20 subjects, we elucidate that LeNet, VGG, and ResNet exhibit superior performance compared to recent advancements reported in the literature. Furthermore, a comprehensive architectural analysis was conducted, illuminating the strengths and limitations of each in the context of home-based sleep monitoring. Our findings distinctly identify LeNet as the most-amenable architecture for this purpose, with LSTM and BLSTM demonstrating relatively lesser compatibility. Ultimately, this research substantiates the feasibility of automating sleep stage classification employing lightweight neural networks, thereby accommodating scenarios with constrained computational resources. This advancement aims at revolutionizing the field of sleep monitoring, making it more accessible and reliable for individuals in their homes. Full article
(This article belongs to the Special Issue Intelligent Sensors for Healthcare and Patient Monitoring)
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16 pages, 2401 KiB  
Article
Data Mining and Fusion Framework for In-Home Monitoring Applications
by Idongesit Ekerete, Matias Garcia-Constantino, Christopher Nugent, Paul McCullagh and James McLaughlin
Sensors 2023, 23(21), 8661; https://doi.org/10.3390/s23218661 - 24 Oct 2023
Viewed by 1528
Abstract
Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to [...] Read more.
Sensor Data Fusion (SDT) algorithms and models have been widely used in diverse applications. One of the main challenges of SDT includes how to deal with heterogeneous and complex datasets with different formats. The present work utilised both homogenous and heterogeneous datasets to propose a novel SDT framework. It compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. A total of 574 privacy-friendly (binary) images and 1722 datasets gleaned from thermal and Radar sensing solutions, respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets, respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, and CN2 Induction. Further evaluation of the Sensor Data Fusion framework based on cross-validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% for Precision, and 96.4% for Recall. The novelty of the proposed framework includes cost and timesaving advantages for data labelling and preparation, and feature extraction. Full article
(This article belongs to the Special Issue Intelligent Sensors for Healthcare and Patient Monitoring)
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22 pages, 545 KiB  
Systematic Review
Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis
by Robin Kraft, Manfred Reichert and Rüdiger Pryss
Sensors 2024, 24(2), 472; https://doi.org/10.3390/s24020472 - 12 Jan 2024
Viewed by 765
Abstract
As mobile devices have become a central part of our daily lives, they are also becoming increasingly important in research. In the medical context, for example, smartphones are used to collect ecologically valid and longitudinal data using Ecological Momentary Assessment (EMA), which is [...] Read more.
As mobile devices have become a central part of our daily lives, they are also becoming increasingly important in research. In the medical context, for example, smartphones are used to collect ecologically valid and longitudinal data using Ecological Momentary Assessment (EMA), which is mostly implemented through questionnaires delivered via smart notifications. This type of data collection is intended to capture a patient’s condition on a moment-to-moment and longer-term basis. To collect more objective and contextual data and to understand patients even better, researchers can not only use patients’ input via EMA, but also use sensors as part of the Mobile Crowdsensing (MCS) approach. In this paper, we examine how researchers have embraced the topic of MCS in the context of EMA through a systematic literature review. This PRISMA-guided review is based on the databases PubMed, Web of Science, and EBSCOhost. It is shown through the results that both EMA research in general and the use of sensors in EMA research are steadily increasing. In addition, most of the studies reviewed used mobile apps to deliver EMA to participants, used a fixed-time prompting strategy, and used signal-contingent or interval-contingent self-assessment as sampling/assessment strategies. The most commonly used sensors in EMA studies are the accelerometer and GPS. In most studies, these sensors are used for simple data collection, but sensor data are also commonly used to verify study participant responses and, less commonly, to trigger EMA prompts. Security and privacy aspects are addressed in only a subset of mHealth EMA publications. Moreover, we found that EMA adherence was negatively correlated with the total number of prompts and was higher in studies using a microinteraction-based EMA (μEMA) approach as well as in studies utilizing sensors. Overall, we envision that the potential of the technological capabilities of smartphones and sensors could be better exploited in future, more automated approaches. Full article
(This article belongs to the Special Issue Intelligent Sensors for Healthcare and Patient Monitoring)
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