Recent Progress in Intelligent Wearable Sensors for Health Monitoring and Wound Healing Based on Biofluids

Cheng, Siyang; Gu, Zhen; Zhou, Liping; Hao, Mingda; An, Heng; Song, Kun; Wu, Xiaochao; Zhang, Kexin; Zhao, Zeya; Dong, Yaozhong

doi:10.3389/fbioe.2021.765987

Cited by 27 publications

(19 citation statements)

References 188 publications

(266 reference statements)

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“…However, the correlation between tears and blood concentration needs further investigation. [84] However, despite all advantages mentioned previously, some remarkable challenges in utilizing microfluidic devices have remained unsolved. For instance, unstable experimental conditions in microchannels fabricated by polydimethylsiloxane can be originated from various factors, including micro bubbles formation in microchannels, fabrication discrepancies, chemical impurities, and clogging, or even factors such as substrate deformation, surface fouling, pressure, and temperature fluctuations, as long-term operation effects.…”

Section: Positioning Systemsmentioning

confidence: 99%

“…Also, a reference for treating and diagnosing diseases that lead to secondary blood composition changes can be established. [ 84 ] However, the biofluids that are secreted naturally, but more readily available (such as urine, sweat, interstitial fluid, tear, salvia, and so on), are also getting attention. There may be more specific and sensitive indicators for specific diseases in other body fluids rather than blood.…”

Section: The Advantages Of Microfluidics For Biotechnologymentioning

confidence: 99%

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Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Harofte

Soltani

Siavashy

et al. 2022

Small

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utilization of biological systems to develop new products at one end of the spectrum to the application of technology for solving the problems in biology at the other end. [1] According to the new experimental technologies like DNA nucleotide sequencing and RNA sequencing genomics, [2] Mass Spectrometry [3] as well as X-ray crystallography and Nuclear Magnetic Resonance spectroscopy [4] in proteomics, large datasets are generated and sorted in the field of biotechnology that make new opportunities for researchers along with companies that present services and products in this area. Nowadays, performing stateof-the-art research in biotechnology is roughly impossible without exploiting database and artificial intelligence (AI) technologies. [5] The integration of AI and biotechno logy is now prevalent. AI elucidates computational systems which extract signals and learn from the input. [1] Historically, AI has a close relationship with biology. Precisely, this relation is between statistics and genetics. Compared to text or image datasets, biological data is more multidimensional, complicated, and noisy. Hence, AI models are more pertinent than simple statistical models for biological datasets. [6] Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.

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Section: Positioning Systemsmentioning

confidence: 99%

Section: The Advantages Of Microfluidics For Biotechnologymentioning

confidence: 99%

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Harofte

Soltani

Siavashy

et al. 2022

Small

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“…Flexible SERS sensors have been used to diagnose and screen for various diseases such as cancer, diabetes and glaucoma by detecting disease-related biomarkers. Liu et al (Cheng, et al, 2021b) introduced flexible plasma metasurface with SERS activity as a basic sensing component for wearable sensors (Figure 2A). Since sweat samples containing different drugs exhibit completely Frontiers in Chemistry frontiersin.org different SERS spectra, the sensor can fingerprint the targets extracted from sweat, monitor changes in trace amounts of drugs in the body through sweat, and obtain an individual drug metabolism profile.…”

Section: Applications In Point-of-care Testing Non-invasive Medical D...mentioning

confidence: 99%

Surface-enhanced Raman scattering as a potential strategy for wearable flexible sensing and point-of-care testing non-invasive medical diagnosis

Liu

Guo

et al. 2022

Front. Chem.

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As a powerful and effective analytical tool, surface-enhanced Raman scattering (SERS) has attracted considerable research interest in the fields of wearable flexible sensing and non-invasive point-of-care testing (POCT) medical diagnosis. In this mini-review, we briefly summarize the design strategy, the development progress of wearable SERS sensors and its applications in this field. We present SERS substrate analysis of material design requirements for wearable sensors and highlight the benefits of novel plasmonic particle-in-cavity (PIC)-based nanostructures for flexible SERS sensors, as well as the unique interfacial adhesion effect and excellent mechanical properties of natural silk fibroin (SF) derived from natural cocoons, indicating promising futures for applications in the field of flexible electronic, optical, and electrical sensors. Additionally, SERS wearable sensors have shown great potential in the fields of different disease markers as well as in the diagnosis testing for COVID-19. Finally, the current challenges in this field are pointed out, as well as the promising prospects of combining SERS wearable sensors with other portable health monitoring systems for POCT medical diagnosis in the future.

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“…The digital healthcare revolution promises to switch from a hospital-centered model to a personal-centered model ( 1 ), offering the possibility to remotely and continuously monitor patients' health state, thus reducing the use of bulky instruments and complicated procedures ( 2 ). One of the key elements of this revolution is represented by wearable devices, which are small electronic systems that can be worn during daily life ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

Quality Assessment and Morphological Analysis of Photoplethysmography in Daily Life

Moscato

Palmerini

Palumbo

et al. 2022

Front. Digit. Health

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The photoplethysmographic (PPG) signal has been applied in various research fields, with promising results for its future clinical application. However, there are several sources of variability that, if not adequately controlled, can hamper its application in pervasive monitoring contexts. This study assessed and characterized the impact of several sources of variability, such as physical activity, age, sex, and health state on PPG signal quality and PPG waveform parameters (Rise Time, Pulse Amplitude, Pulse Time, Reflection Index, Delta T, and DiastolicAmplitude). We analyzed 31 24 h recordings by as many participants (19 healthy subjects and 12 oncological patients) with a wristband wearable device, selecting a set of PPG pulses labeled with three different quality levels. We implemented a Multinomial Logistic Regression (MLR) model to evaluate the impact of the aforementioned factors on PPG signal quality. We then extracted six parameters only on higher-quality PPG pulses and evaluated the influence of physical activity, age, sex, and health state on these parameters with Generalized Linear Mixed Effects Models (GLMM). We found that physical activity has a detrimental effect on PPG signal quality quality (94% of pulses with good quality when the subject is at rest vs. 9% during intense activity), and that health state affects the percentage of available PPG pulses of the best quality (at rest, 44% for healthy subjects vs. 13% for oncological patients). Most of the extracted parameters are influenced by physical activity and health state, while age significantly impacts two parameters related to arterial stiffness. These results can help expand the awareness that accurate, reliable information extracted from PPG signals can be reached by tackling and modeling different sources of inaccuracy.

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Recent Progress in Intelligent Wearable Sensors for Health Monitoring and Wound Healing Based on Biofluids

Cited by 27 publications

References 188 publications

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Surface-enhanced Raman scattering as a potential strategy for wearable flexible sensing and point-of-care testing non-invasive medical diagnosis

Quality Assessment and Morphological Analysis of Photoplethysmography in Daily Life

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