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Advanced Non-invasive Sensors: Methods and Applications

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Dr. Janusz Kulon

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Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd CF37 1DL, Cymru, UK
Interests: biomedical engineering and computing; design of medical instrumentation; non-invasive sensor applications

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

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Published Papers (2 papers)

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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

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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)

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Review

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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

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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)

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