Evaluation of Unobtrusive Microwave Sensors in Healthcare 4.0—Toward the Creation of Digital-Twin Model

Khan, Sagheer; Saied, Imran; Ratnarajah, Tharmalingam; Arslan, Tughrul

doi:10.3390/s22218519

Cited by 7 publications

(6 citation statements)

References 58 publications

(61 reference statements)

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“…Existing studies have used smartphones, watches, and other wearable devices to collect parameters like daily food intake, exercise, sleep time, and mood changes for DT development. 21 , 57 , 58 Intrabody microrobots offer another pathway for data collection. Negnab et al 59 have developed controllers for magnetic levitation microrobots.…”

Section: Discussionmentioning

confidence: 99%

Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review

Sheng,

Wang,

Qiao

et al. 2023

DIGITAL HEALTH

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Objective Digital twins (DTs) have received widespread attention recently, providing new ideas and possibilities for future healthcare. This review aims to provide a quantitative review to analyze specific study contents, research focus, and trends of DT in healthcare. Simultaneously, this review intends to expand the connotation of “healthcare” into two directions, namely “Disease treatment” and “Health enhancement” to analyze the content within the “DT + healthcare” field thoroughly. Methods A data mining method named Structure Topic Modeling (STM) was used as the analytical tool due to its topic analysis ability and versatility. Google Scholar, Web of Science, and China National Knowledge Infrastructure supplied the material papers in this review. Results A total of 94 high-quality papers published between 2018 and 2022 were gathered and categorized into eight topics, collectively covering the transformative impact across a broader spectrum in healthcare. Three main findings have emerged: (1) papers published in healthcare predominantly concentrate on technology development (artificial intelligence, Internet of Things, etc.) and application scenarios(personalized, precise, and real-time health service); (2) the popularity of research topics is influenced by various factors, including policies, COVID-19, and emerging technologies; and (3) the preference for topics is diverse, with a general inclination toward the attribute of “Health enhancement.” Conclusions This review underscores the significance of real-time capability and accuracy in shaping the future of DT, where algorithms and data transmission methods assume central importance in achieving these goals. Moreover, technological advancements, such as omics and Metaverse, have opened up new possibilities for DT in healthcare. These findings contribute to the existing literature by offering quantitative insights and valuable guidance to keep researchers ahead of the curve.

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Section: Discussionmentioning

confidence: 99%

Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review

Sheng,

Wang,

Qiao

et al. 2023

DIGITAL HEALTH

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“…[140]- [142], [193], [144], [113], [121], [122], [128], [129], [131], [147], [151], [154], [155], [160], [176], [101], [119], [124] Measurements as inputs 28 [97], [110], [171], [121], [133], [134], [156], [157], [162], [194], [195], [99], [106]- [108], [136], [153], [163], [167], [169], [174], [184], [185], [190], [196] [102], [116], [168], [169], [180] Probabilistic model updating 7 [98], [105], [111],…”

Section: Referencesmentioning

confidence: 99%

“…These products exhibit great diversity. They include medical robots for surgery [102], [107], microrobots for drug delivery [118], birdcage radio frequency coil of MRI scanners [98], organ transplantation devices [180], unobtrusive microwave sensors for data collection applications [168], wearable exoskeletons for medical monitoring [196], to name a few.…”

Section: A: Product Twinningmentioning

confidence: 99%

A Systematic Literature Review of Digital Twin Research for Healthcare Systems: Research Trends, Gaps, and Realization Challenges

Xames,

Topcu

2024

IEEE Access

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Using the PRISMA approach, we present the first systematic literature review of digital twin (DT) research in healthcare systems (HSs). This endeavor stems from the pressing need for a thorough analysis of this emerging yet fragmented research area, with the goal of consolidating knowledge to catalyze its growth. Our findings are structured around three research questions aimed at identifying: (i) current research trends, (ii) gaps, and (iii) realization challenges. Current trends indicate global interest and interdisciplinary collaborations to address complex HS challenges. However, existing research predominantly focuses on conceptualization; research on integration, verification, and implementation is nascent. Additionally, we document that a substantial body of papers mislabel their work, often disregarding modeling and twinning methods that are necessary elements of a DT. Furthermore, we provide a non-exhaustive classification of the literature based on two axes: the object (i.e., product or process) and the context (i.e., patient's body, medical procedures, healthcare facilities, and public health). While this is a testament to the diversity of the field, it implies a specific pattern that could be reimagined. We also identify two gaps: (i) considering the human-in-the-loop nature of HSs with a focus on provider decision-making and (ii) implementation research. Lastly, we discuss two challenges for broad-scale implementation of DTs in HSs: improving virtual-to-physical connectivity and data-related issues. In conclusion, this study suggests that DT research could potentially help alleviate the acute shortcomings of HSs that are often manifested in the inability to concurrently improve the quality of care, provider wellbeing, and cost efficiency.

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“…An additional drawback is that the cameras are a privacy concern. Under the threat of cybercrimes, it is difficult to have a promising solution for indoor sensing [44]. Wearable devices can be utilized as a solution for unobtrusive sensing [45].…”

Section: Sensing a Wi-fi Sensingmentioning

confidence: 99%

“…al. [44] discussed the importance of the sensor's position in data collection for the creation of DT models. If the sensor utilized provides inaccurate measurements of the patient's respiration rate, the DT model will not replicate the respiration behavior in real-time.…”

Section: B Sensing In Dtmentioning

confidence: 99%

A Novel Digital Twin (DT) Model Based on WiFi CSI, Signal Processing and Machine Learning for Patient Respiration Monitoring and Decision-Support

Khan,

Alzaabi,

Iqbal

et al. 2023

IEEE Access

Self Cite

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Digital Twin (DT) in Healthcare 4.0 (H4.0) presents a digital model of the patient with all its biological properties and characteristics. One of the application areas is patient respiration monitoring for enhanced patient care and decision support to healthcare professionals. Obtrusive methods of patient monitoring create hindrances in the patient's daily routine. This research presents a novel DT model (ResDT) based on Wi-Fi Carrier State Information (CSI), improved signal processing, and Machine Learning (ML) algorithms for monitoring and classification (binary and multi-class) of patient respiration. A Wi-Fi sensor ESP32 with Wi-Fi CSI was utilized for the collection of respiration data. This provides an added advantage of unobtrusive monitoring of patient vital signs. The Patient's Breaths Per Minute (BPM) is estimated from raw sensor data through the integration of multiple signal processing methodologies for denoising (smoothing and filtering) and dimensionality reduction (PCA, SVM, EMD, EMD-PCA). Multiple filters and dimensionality reduction methodologies are compared for accurate BPM estimation. The elliptical filter provides a relatively better estimation of the BPM with 87.5% accurate estimation as compared to other bandpass filters such as Butterworth (BF), Chebyshev type 1 Filter (CH1), Chebyshev type 2 Filter (CH2), and wavelet Decomposition (62.5%, 75%, 68.75%, and 75% respectively). Principal Component Analysis (PCA) was performed to provide better dimensionality reduction with 87.5% accurate BPM values compared to EMD, SVD, and EMD-PCA (57%, 44%, and 44% respectively). Additionally, the fine tree algorithm, from the implemented 21 ML supervised classification algorithms with K-fold crossvalidation, was observed to be the optimal choice for multi-class and binary-class classification problems in the presented ResDT model with 96.9% and 95.8% accuracy respectively.

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Evaluation of Unobtrusive Microwave Sensors in Healthcare 4.0—Toward the Creation of Digital-Twin Model

Cited by 7 publications

References 58 publications

Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review

Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review

A Systematic Literature Review of Digital Twin Research for Healthcare Systems: Research Trends, Gaps, and Realization Challenges

A Novel Digital Twin (DT) Model Based on WiFi CSI, Signal Processing and Machine Learning for Patient Respiration Monitoring and Decision-Support

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