Recent Advances and Design Strategies Towards Wearable Near-Infrared Spectroscopy

Liu, Shuoyan; Xue, Bing; Yan, Wenyuan; Rwei, Alina Y.; Wu, Changsheng

doi:10.1109/ojnano.2022.3226603

Cited by 4 publications

(2 citation statements)

References 96 publications

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“…In the realm of wearable technology, the future of wristworn antennas appears promising with continuous advancements in miniaturization techniques [129], [130] and integration with additive manufacturing technologies [131]- [133], such as 3D printing [134], screen printing [135], [136], and inkjet printing [137], [138]. Antenna miniaturization facilitates sleeker and more aesthetically pleasing designs, thereby enhancing user comfort and wearability [139]. The authors of [129], [130] discuss several antenna miniaturization techniques, such as implementing space-filling curves, fractal geometries, meander lines, engineered ground planes, reactive loading, high-dielectric constant substrates, 3D printing for complex geometries, slow-wave structures, and metamaterials.…”

Section: Future Perspectivesmentioning

confidence: 99%

Sub-GHz Wrist-Worn Antennas for Wireless Sensing Applications: A Review

Kumar,

Moloudian,

Simorangkir

et al. 2024

IEEE Open J. Antennas Propag.

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With recent advances in wearable wrist-worn wireless sensing applications, the demand for smartwatches and wristbands is rapidly increasing due to their widespread adoption in applications such as smart health monitoring, security, and fitness tracking. Currently, these devices primarily operate in the 2.45 GHz band, leveraging the availability of Bluetooth and Wi-Fi wireless technologies. However, the use of 433 MHz, 868 MHz, 915 MHz, 923 MHz) for wearable systems has also gained interest due to the emergence of wireless technologies like long-range wide area network (LoRaWAN), narrowband-IoT (NB-IoT) and Sigfox, which offer the potential for long-range wireless communications and sensing applications. In recent times, there has been a notable surge in the commercial production of a variety of Sub-GHz wrist-worn wireless sensing devices for health monitoring and tracking applications. Nevertheless, communications at Sub-GHz frequencies present significant challenges in antenna design, primarily due to the practical size constraints of wrist-worn devices and the necessity for using electrically small antennas. This paper meticulously reviews wrist-worn Sub-GHz antennas reported in the literature, analyzing key antenna parameters such as antenna topology, size, impedance bandwidth, peak realized gain, radiation efficiency, and specific absorption rate (SAR). Additionally, it underlines antenna design challenges, limitations, current trends, and presents potential future perspectives. To the best of the author's knowledge, there is currently no existing literature comprehensively reviewing Sub-GHz wristworn antennas. Therefore, this paper represents the inaugural effort to provide a comprehensive review in this specific domain.

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Section: Future Perspectivesmentioning

confidence: 99%

Sub-GHz Wrist-Worn Antennas for Wireless Sensing Applications: A Review

Kumar,

Moloudian,

Simorangkir

et al. 2024

IEEE Open J. Antennas Propag.

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“…However, during cognitive tasks, pupil dilation not only occurs, but direct current (DC) HbO2 levels detected with fNIRS also increase, closely linking them to cognitive and neuronal activity (Jobsis, 1977;Ferrari & Quaresima, 2012); for recent reviews (see Zhou et al, 2023;Liu et al, 2023;Hoshi & Tamura, 1993;Menon et al, 2002;Hugdahl et al, 2004;Tanida et al, 2004;Tanida et al, 2007;Yang et al, 2009;Richter et al, 2009;Pfurtscheller et al, 2010;Power et al, 2010;Verner et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Reduction of low-frequency oscillations in cerebral circulation correlates with pupil dilation during cognition: an fNIRS study

Moreno-Castillo,

Manjarrez

2023

Preprint

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Numerous studies have utilized functional near-infrared spectroscopy (fNIRS) to investigate brain hemodynamics during diverse cognitive tasks. However, although these studies have consistently reported increased oxygenation levels in the prefrontal cortex, they have not explored their effects on the amplitude of brain low-frequency oscillations (LFO). Additionally, other reports have shown that pupil dilation occurs during cognitive tasks, which may indicate a correlation between LFO amplitude and pupil dilation. However, no study has demonstrated such a correlation. Our research has two aims: firstly, to analyze the impact of cognitive tasks on the amplitude of LFO using oxyhemoglobin (HbO2) fNIRS signals, and secondly, to assess the relationship between the amplitude of these LFO and pupil diameters during such cognitive tasks. We found that during arithmetic tasks, the brain LFOs recorded on the prefrontal cortex were temporarily reduced while pupil diameter increased. These findings offer new insights into the physiological functions of reactivity of LFO in cerebral circulation. Additionally, combining fNIRS signals to track LFO on the prefrontal cortex with pupil measurement shows the feasibility of developing efficient hybrid brain-computer interfaces of LFO-pupil detection capable of predicting the initiation and ending of cognitive processes.Significant statementInspired by the Berger effect, which demonstrates alpha wave reactivity with eye-opening, our study investigated reactivity in other physiological oscillations related to brain hemodynamics during cognition. Our findings revealed a novel phenomenon: a temporary reduction in the amplitude of low-frequency oscillations (LFOs) of oxyhemoglobin (HbO2) in the brain’s prefrontal cortex, concurrent with increased pupil diameter during arithmetic tasks. This discovery expands our knowledge of LFO in cerebral circulation and creates new avenues for studying fNIRS-LFO reactivity during cognitive tasks in healthy individuals and patients.

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Advances in Machine Learning for Wearable Sensors

Xiao,

Yin,

et al. 2024

ACS Nano

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Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and unsupervised learning algorithms have emerged as powerful tools, allowing for the detection of complex patterns and relationships in large, high-dimensional data sets. In this Review, we aim to delineate the latest advancements in machine learning for wearable sensors, focusing on key developments in algorithmic techniques, applications, and the challenges intrinsic to this evolving landscape. Additionally, we highlight the potential of machine-learning approaches to enhance the accuracy, reliability, and interpretability of wearable sensor data and discuss the opportunities and limitations of this emerging field. Ultimately, our work aims to provide a roadmap for future research endeavors in this exciting and rapidly evolving area.

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Recent Advances and Design Strategies Towards Wearable Near-Infrared Spectroscopy

Cited by 4 publications

References 96 publications

Sub-GHz Wrist-Worn Antennas for Wireless Sensing Applications: A Review

Sub-GHz Wrist-Worn Antennas for Wireless Sensing Applications: A Review

Reduction of low-frequency oscillations in cerebral circulation correlates with pupil dilation during cognition: an fNIRS study

Advances in Machine Learning for Wearable Sensors

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