Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Advanced Non-Invasive Sensors: Methods and Applications

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Benefits of Publishing in a Special Issue
Related Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (5 January 2025) | Viewed by 18636

Share This Special Issue

Special Issue Editor

Prof. Dr. Janusz Kulon

E-Mail Website
Guest Editor

Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd CF37 1DL, UK
Interests: biomedical engineering and computing; design of medical instrumentation; non-invasive sensor applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of sensor technology is witnessing transformative change with the advent of non-invasive sensing methods. This non-invasive approach, preserving the integrity of the observed system or process, ensures a wide range of applications, from personal health monitoring to industrial automation, agriculture, and environmental sensing. Advancements in methodologies, ranging from design and fabrication to the processing algorithms, play a pivotal role in enhancing the efficiency and effectiveness of non-invasive sensors.

How can we advance non-invasive sensor technology to deliver higher performance and broader applicability? How can we leverage these advancements to innovate applications across various domains? We eagerly await innovative research papers that address these questions and illuminate the path forward for non-invasive sensing.

This Special Issue, entitled “Advanced Non-invasive Sensor: Methods and Applications”, welcomes contributions that delve into every facet of non-invasive sensor technology.

Relevant topics include, but are not limited to, the following:

Advanced design and fabrication of non-invasive sensors;
Innovative sensor applications in health monitoring, agriculture, industry, and environment;
Integration of AI and non-invasive sensors;
Non-invasive sensor networks;
Data analysis and processing for non-invasive sensor signals;
Wearable non-invasive devices;
Sensor-captured imaging and non-invasive techniques.

Let us join hands to shed light on the exciting frontier of non-invasive sensor technology, its potential and practical applications across diverse domains. We look forward to your valuable contributions.

Dr. Janusz Kulon
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.

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Advanced Non-Invasive Sensors: Methods and Applications—2nd Edition in Sensors

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

17 pages, 1415 KiB

Open AccessArticle

Distance and Angle Insensitive Radar-Based Multi-Human Posture Recognition Using Deep Learning

by Sohaib Abdullah, Shahzad Ahmed, Chanwoo Choi and Sung Ho Cho

Sensors 2024, 24(22), 7250; https://doi.org/10.3390/s24227250 - 13 Nov 2024

Cited by 2 | Viewed by 1331

Abstract

Human posture recognition has a wide range of applicability in the detective and preventive healthcare industry. Recognizing posture through frequency-modulated continuous wave (FMCW) radar poses a significant challenge as the human subject is static. Unlike existing radar-based studies, this study proposes a novel framework to extract the postures of two humans in close proximity using FMCW radar point cloud. With radar extracted range, velocity, and angle information, point clouds in the Cartesian domain are retrieved. Afterwards, unsupervised clustering is implemented to segregate the two humans, and finally a deep learning model named DenseNet is applied to classify the postures of both human subjects. Using four base postures (namely, standing, sitting on chair, sitting on floor, and lying down), ten posture combinations for two human scenarios are classified with an average accuracy of 96%. Additionally, using the centroid information of human clusters, an approach to detect and classify overlapping human participants is also introduced. Experiments with five posture combinations of two overlapping humans yielded an accuracy of above 96%. The proposed framework has the potential to offer a privacy-preserving preventive healthcare sensing platform for an elderly couple living alone. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications)

► Show Figures

Figure 1

12 pages, 2226 KiB

Open AccessArticle

The Neurological and Hemodynamics Safety of an Airway Clearance Technique in Patients with Acute Brain Injury: An Analysis of Intracranial Pressure Pulse Morphology Using a Non-Invasive Sensor

by Daniela de Almeida Souza, Gisele Francini Devetak, Marina Wolff Branco, Reinaldo Luz Melo, Jean Lucas Tonial, Ana Marcia Delattre and Silvia Regina Valderramas

Sensors 2024, 24(21), 7066; https://doi.org/10.3390/s24217066 - 2 Nov 2024

Viewed by 1423

Abstract

Patients with acute brain injury (ACI) often require mechanical ventilation (MV) and are subject to pulmonary complications, thus justifying the use of Airway Clearance Techniques (ACTs), but their effects on intracranial pressure (ICP) are unknown. This study investigates the neurological and hemodynamics safety of an ACT called ventilator hyperinflation (VHI) in patients with ACI. This was a randomized clinical equivalence trial, which included patients aged ≥ 18 years with a clinical diagnosis of hemorrhagic stroke, with symptom onset within 48 h. The participants were randomly allocated to the Experimental Group (EG, n = 15), which underwent VHI followed by tracheal aspiration (TA), and the Control Group (CG, n = 15), which underwent TA only. Neurological safety was verified by analyzing the morphology of the ICP wave through the non-invasive B4C sensor, which detects bone deformation of the skull, resulting in a P2/P1 ratio and TTP, and hemodynamics through a multi-parameter monitor. Evaluations were recorded during five instances: T1 (baseline/pre-VHI), T2 (post-VHI and before TA), T3 (post-TA), T4 and T5 (monitoring 10 and 20 min after T3). The comparison between groups showed that there was no effect of the technique on the neurological variables with a mean P2/P1 ratio [F (4,112) = 1.871; p = 0.120; np2 = 0.063] and TTP [F (4,112) = 2.252; p = 0.068; np2 = 0.074], and for hemodynamics, heart rate [F (4,112) = 1.920; p = 0.112; np2 = 0.064] and mean arterial pressure [F(2.73, 76.57) = 0.799; p = 0.488; np2 = 0.028]. Our results showed that VHI did not pose a neurological or hemodynamics risk in neurocritical patients after ACI. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications)

► Show Figures

Figure 1

15 pages, 535 KiB

Open AccessArticle

Thought-Controlled Computer Applications: A Brain–Computer Interface System for Severe Disability Support

by Kais Belwafi and Fakhreddine Ghaffari

Sensors 2024, 24(20), 6759; https://doi.org/10.3390/s24206759 - 21 Oct 2024

Cited by 3 | Viewed by 2037

Abstract

This study introduces an integrated computational environment that leverages Brain–Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human–Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system’s Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications)

► Show Figures

Figure 1

10 pages, 892 KiB

Open AccessArticle

Disturbances in Electrodermal Activity Recordings Due to Different Noises in the Environment

by Dindar S. Bari, Haval Y. Y. Aldosky, Christian Tronstad and Ørjan G. Martinsen

Sensors 2024, 24(16), 5434; https://doi.org/10.3390/s24165434 - 22 Aug 2024

Cited by 1 | Viewed by 1111

Abstract

Electrodermal activity (EDA) is a widely used psychophysiological measurement in laboratory-based studies. In recent times, these measurements have seen a transfer from the laboratory to wearable devices due to the simplicity of EDA measurement as well as modern electronics. However, proper conditions for EDA measurement are recommended once wearable devices are used, and the ambient conditions may influence such measurements. It is not completely known how different types of ambient noise impact EDA measurement and how this translates to wearable EDA measurement. Therefore, this study explored the effects of various noise disturbances on the generation of EDA responses using a system for the simultaneous recording of all measures of EDA, i.e., skin conductance responses (SCRs), skin susceptance responses (SSRs), and skin potential responses (SPRs), at the same skin site. The SCRs, SSRs, and SPRs due to five types of noise stimuli at different sound pressure levels (70, 75, 80, 85, and 90 dB) were measured from 40 participants. The obtained results showed that EDA responses were generated at all levels and that the EDA response magnitudes were significantly (p < 0.001) influenced by the increasing noise levels. Different types of environmental noise may elicit EDA responses and influence wearable recordings outside the laboratory, where such noises are more likely than in standardized laboratory tests. Depending on the application, it is recommended to prevent these types of unwanted variation, presenting a challenge for the quality of wearable EDA measurement in real-world conditions. Future developments to shorten the quality gap between standardized laboratory-based and wearable EDA measurements may include adding microphone sensors and algorithms to detect, classify, and process the noise-related EDA. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications)

► Show Figures

Figure 1

14 pages, 6120 KiB

Open AccessArticle

Reliable Stenosis Detection Based on Thrill Waveform Analysis Using Non-Contact Arteriovenous Fistula Imaging

by Rumi Iwai, Takunori Shimazaki, Jaakko Hyry, Yoshifumi Kawakubo, Masashi Fukuhara, Hiroki Aono, Shingo Ata, Takeshi Yokoyama and Daisuke Anzai

Sensors 2024, 24(15), 5068; https://doi.org/10.3390/s24155068 - 5 Aug 2024

Viewed by 1823

Abstract

Hemodialysis therapy is an extracorporeal circulation treatment that serves as a substitute for renal function. In Japan, patients receive this efficient four-hour treatment, three times per week, allowing them to maintain a social life nearly equivalent to that of healthy individuals. Before the treatment, two punctures are performed to establish extracorporeal circulation, and a high blood flow rate is essential to ensure efficient therapy. Specialized blood vessels created through arteriovenous fistula (AVF) surgery are utilized to achieve high blood flow rates. Although the AVF allows safe and efficient dialysis treatment, AVF stenosis leads to a serious problem in dialysis. To early detect this abnormal blood flow, auscultation and palpation methods are widely used in hospitals. However, these methods can only provide qualitative judgment of the AVF condition, so the results cannot be shared among other doctors and staff. Additionally, since the conventional methods require contact with the skin, some issues require consideration regarding infection and low reproducibility. In our previous study, we proposed an alternative method for auscultation using non-contact optical imaging technology. This study aims to construct a reliable AVF stenosis detection method using Thrill waveform analysis based on the developed non-contact device to solve the problem with the contact palpation method. This paper demonstrates the performance validation of the non-contact imaging in the normal AVF group (206 total data, 75 patients, mean age: 69.1 years) and in the treatable stenosis group (107 total data, 17 patients, mean age: 70.1 years). The experimental results of the Mann–Whitney U test showed a significant difference (

p = 0.0002

) between the normal and abnormal groups, which indicated the effectiveness of the proposed method as a new possible alternative to palpation. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications)

► Show Figures

Figure 1

16 pages, 3728 KiB

Open AccessArticle

Were Frailty Identification Criteria Created Equal? A Comparative Case Study on Continuous Non-Invasively Collected Neurocardiovascular Signals during an Active Standing Test in the Irish Longitudinal Study on Ageing (TILDA)

by Feng Xue, Silvin Knight, Emma Connolly, Aisling O’Halloran, Morgana Afonso Shirsath, Louise Newman, Eoin Duggan, Rose Anne Kenny and Roman Romero-Ortuno

Sensors 2024, 24(2), 442; https://doi.org/10.3390/s24020442 - 11 Jan 2024

Cited by 1 | Viewed by 1851

Abstract

Background: In this observational study, we compared continuous physiological signals during an active standing test in adults aged 50 years and over, characterised as frail by three different criteria, using data from The Irish Longitudinal Study on Ageing (TILDA). Methods: This study utilised data from TILDA, an ongoing landmark prospective cohort study of community-dwelling adults aged 50 years or older in Ireland. The initial sampling strategy in TILDA was based on random geodirectory sampling. Four independent groups were identified: those characterised as frail only by one of the frailty tools used (the physical Frailty Phenotype (FP), the 32-item Frailty Index (FI), or the Clinical Frailty Scale (CFS) classification tree), and a fourth group where participants were not characterised as frail by any of these tools. Continuous non-invasive physiological signals were collected during an active standing test, including systolic (sBP) and diastolic (dBP) blood pressure, as well as heart rate (HR), using digital artery photoplethysmography. Additionally, the frontal lobe cerebral oxygenation (Oxy), deoxygenation (Deoxy), and tissue saturation index (TSI) were also non-invasively measured using near-infrared spectroscopy (NIRS). The signals were visualised across frailty groups and statistically compared using one-dimensional statistical parametric mapping (SPM). Results: A total of 1124 participants (mean age of 63.5 years; 50.2% women) were included: 23 were characterised as frail only by the FP, 97 by the FI, 38 by the CFS, and 966 by none of these criteria. The SPM analyses revealed that only the group characterised as frail by the FI had significantly different signals (p < 0.001) compared to the non-frail group. Specifically, they exhibited an attenuated gain in HR between 10 and 15 s post-stand and larger deficits in sBP and dBP between 15 and 20 s post-stand. Conclusions: The FI proved to be more adept at capturing distinct physiological responses to standing, likely due to its direct inclusion of cardiovascular morbidities in its definition. Significant differences were observed in the dynamics of cardiovascular signals among the frail populations identified by different frailty criteria, suggesting that caution should be taken when employing frailty identification tools on physiological signals, particularly the neurocardiovascular signals in an active standing test. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications)

► Show Figures

Figure 1

Review

Jump to: Research

23 pages, 8208 KiB

Open AccessReview

Smart Sensing Chairs for Sitting Posture Detection, Classification, and Monitoring: A Comprehensive Review

by David Faith Odesola, Janusz Kulon, Shiny Verghese, Adam Partlow and Colin Gibson

Sensors 2024, 24(9), 2940; https://doi.org/10.3390/s24092940 - 5 May 2024

Cited by 8 | Viewed by 8152

Abstract

Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. The prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for the real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart sensing chairs, with a specific focus on the strategies used for posture detection and classification and the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, Google Scholar, Scopus, and PubMed databases yielded 39 pertinent studies that utilized non-invasive methods for posture monitoring. The analysis revealed that Force Sensing Resistors (FSRs) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy due to the constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments. Full article

(This article belongs to the Special Issue Advanced Non-Invasive Sensors: Methods and Applications)

► Show Figures

Journal Menu

Journal Browser

Advanced Non-Invasive Sensors: Methods and Applications

Share This Special Issue

Special Issue Editor

Special Issue Information

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (7 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI