Enhanced low-light image fusion through multi-stage processing with Bayesian analysis and quadratic contrast function.

This manuscript introduces an innovative multi-stage image fusion framework that adeptly integrates infrared (IR) and visible (VIS) spectrum images to surmount the difficulties posed by low-light settings. The approach commences with an initial preprocessing stage, utilizing an Efficient Guided Image Filter for the infrared (IR) images to amplify edge boundaries and a function for the visible (VIS) images to boost local contrast and brightness. Utilizing a two-scale decomposition technique that incorporates Lipschitz constraints-based smoothing, the images are effectively divided into distinct base and detail layers, thereby guaranteeing the preservation of essential structural information. The process of fusion is carried out in two distinct stages: firstly, a method grounded in Bayesian theory is employed to effectively combine the base layers, so effectively addressing any inherent uncertainty. Secondly, a Surface from Shade (SfS) method is utilized to ensure the preservation of the scene's geometry by enforcing integrability on the detail layers. Ultimately a Choose Max principle is employed to determine the most prominent textural characteristics, resulting in the amalgamation of the base and detail layers to generate an image that exhibits a substantial enhancement in both clarity and detail. The efficacy of our strategy is substantiated by rigorous testing, showcasing notable progressions in edge preservation, detail enhancement, and noise reduction. Consequently, our method presents significant advantages for real-world applications in image analysis.

Digital Twins for Healthcare Using Wearables.

Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors.

Development of Non-Invasive Biosensors for Neonatal Jaundice Detection: A Review.

One of the most common problems many babies encounter is neonatal jaundice. The symptoms are yellowing of the skin or eyes because of bilirubin (from above 2.0 to 2.5 mg/dL in the blood). If left untreated, it can lead to serious neurological complications. Traditionally, jaundice detection has relied on invasive blood tests, but developing non-invasive biosensors has provided an alternative approach. This systematic review aims to assess the advancement of these biosensors. This review discusses the many known invasive and non-invasive diagnostic modalities for detecting neonatal jaundice and their limitations. It also notes that the recent research and development on non-invasive biosensors for neonatal jaundice diagnosis is still in its early stages, with the majority of investigations being in vitro or at the pre-clinical level. Non-invasive biosensors could revolutionize neonatal jaundice detection; however, a number of issues still need to be solved before this can happen. These consist of in-depth validation studies, affordable and user-friendly gadgets, and regulatory authority approval. To create biosensors that meet regulatory requirements, additional research is required to make them more precise and affordable.

Online Video-Mediated Compassion Training Program for Mental Health and Well-Being of University Students.

College students experiencing psychological distress have significantly greater negative emotions than students who practice compassionate thinking. We have developed Eight Steps to Great Compassion (ESGC), an innovative brief and no-cost online video training program about how to increase compassion among busy and young adult university students. To examine the effectiveness and benefits of the ESGC, a single-group pre-test-post-test quantitative design with undergraduate university students (N = 92; Mage = 20.39) evaluated its effects. The results from the post-test showed that the ESGC had a significant positive impact on increased feelings of compassion towards oneself, compassion for others, and the sense of personal well-being from the pre-test. The analysis of the PERMA-Profiler subscales also reflected a statistically significant increase in overall well-being and health and a decrease in negative emotions and loneliness. From the Post-Survey Lesson Feedback, 88% of the participants reported significant positive changes in themselves and the way that they live due to the program. These findings appear to show important implications for improving healthy minds and reducing negative emotions among university students.

Smart Textile Impact Sensor for e-Helmet to Measure Head Injury.

Concussions, a prevalent public health concern in the United States, often result from mild traumatic brain injuries (mTBI), notably in sports such as American football. There is limited exploration of smart-textile-based sensors for measuring the head impacts associated with concussions in sports and recreational activities. In this paper, we describe the development and construction of a smart textile impact sensor (STIS) and validate STIS functionality under high magnitude impacts. This STIS can be inserted into helmet cushioning to determine head impact force. The designed 2 × 2 STIS matrix is composed of a number of material layered structures, with a sensing surface made of semiconducting polymer composite (SPC). The SPC dimension was modified in the design iteration to increase sensor range, responsiveness, and linearity. This was to be applicable in high impact situations. A microcontroller board with a biasing circuit was used to interface the STIS and read the sensor's response. A pendulum test setup was constructed to evaluate various STISs with impact forces. A camera and Tracker software were used to monitor the pendulum swing. The impact forces were calculated by measuring the pendulum bob's velocity and acceleration. The performance of the various STISs was measured in terms of voltage due to impact force, with forces varying from 180 to 722 N. Through data analysis, the threshold impact forces in the linear range were determined. Through an analysis of linear regression, the sensors' sensitivity was assessed. Also, a simplified model was developed to measure the force distribution in the 2 × 2 STIS areas from the measured voltages. The results showed that improving the SPC thickness could obtain improved sensor behavior. However, for impacts that exceeded the threshold, the suggested sensor did not respond by reflecting the actual impact forces, but it gave helpful information about the impact distribution on the sensor regardless of the accurate expected linear response. Results showed that the proposed STIS performs satisfactorily within a range and has the potential to be used in the development of an e-helmet with a large STIS matrix that could cover the whole head within the e-helmet. This work also encourages future research, especially on the structure of the sensor that could withstand impacts which in turn could improve the overall range and performance and would accurately measure the impact in concussion-causing impact ranges.

Development of an anatomical breast phantom from polyvinyl chloride plastisol with lesions of various shape, elasticity and echogenicity for teaching ultrasound examination.

PURPOSE: The WHO reported an increasing trend in the number of new cases of breast cancer, making it the most prevalent cancer in the world. This fact necessitates the availability of highly qualified ultrasonographers, which can be achieved by the widespread implementation of training phantoms. The goal of the present work is to develop and test an inexpensive, accessible, and reproducible technology for creating an anatomical breast phantom for practicing ultrasound diagnostic skills in grayscale and elastography imaging, as well as ultrasound-guided biopsy sampling. METHODS: We used FDM 3D printer and PLA plastic for printing an anatomical breast mold. We made a phantom using a mixture of polyvinyl chloride plastisol, graphite powder, and metallic glitter to simulate soft tissues and lesions. Various degrees of elasticity were imparted using plastisols of stiffness ranging from 3 to 17 on the Shore scale. The lesions were shaped by hand. The materials and methods used are easily accessible and reproducible. RESULTS: Using the proposed technology, we have developed and tested a basic, differential, and elastographic versions of the breast phantom. The three versions of the phantom are anatomical and intended for use in medical education: the basic version is for practicing primary hand-eye coordination skills; the differential one is for practicing the differential diagnosis skills; the elastographic version helps developing the skills needed for assessing the stiffness of tissues. CONCLUSION: The proposed technology allows the creation of breast phantoms for practicing hand-eye coordination and develop the critical skills for navigation and assessment of the shape, margins, and size of the lesion, as well as performing an ultrasound-guided biopsy. It is cost-effective, reproducible, and easily implementable, and could be instrumental in generating ultrasonographers with crucial skills for accurate diagnosis of breast cancer, especially in low-resource settings.

Yoga Pose Estimation Using Angle-Based Feature Extraction.

OBJECTIVE: This research addresses the challenges of maintaining proper yoga postures, an issue that has been exacerbated by the COVID-19 pandemic and the subsequent shift to virtual platforms for yoga instruction. This research aims to develop a mechanism for detecting correct yoga poses and providing real-time feedback through the application of computer vision and machine learning (ML) techniques. METHODS AND PROCEDURES: This study utilized computer vision-based pose estimation methods to extract features and calculate yoga pose angles. A variety of models, including extremely randomized trees, logistic regression, random forest, gradient boosting, extreme gradient boosting, and deep neural networks, were trained and tested to classify yoga poses. Our study employed the Yoga-82 dataset, consisting of many yoga pose images downloaded from the web. RESULTS: The results of this study show that the extremely randomized trees model outperformed the other models, achieving the highest prediction accuracy of 91% on the test dataset and 92% in a fivefold cross-validation experiment. Other models like random forest, gradient boosting, extreme gradient boosting, and deep neural networks achieved accuracies of 90%, 89%, 90%, and 85%, respectively, while logistic regression underperformed, having the lowest accuracy. CONCLUSION: This research concludes that the extremely randomized trees model presents superior predictive power for yoga pose recognition. This suggests a valuable avenue for future exploration in this domain. Moreover, the approach has significant potential for implementation on low-powered smartphones with minimal latency, thereby enabling real-time feedback for users practicing yoga at home.

Broader Estimates of Gastrocnemius Activity Generated a More Representative Cocontraction Index: A Study in Pediatric Population.

The electromyography (EMG) cocontraction index (CCI) given by the antagonistic/agonistic Root Mean Square (RMS) amplitude ratio of the same muscle is a qualified biomarker used for spastic cocontraction quantification and management in cerebral palsy children. However, this normative EMG ratio is likely subject to a potential source of errors with biased estimates when measuring the gastrocnemius plantar flexors activity. Due to the uneven distribution of electrical activity within the muscle volume, cocontraction levels can be misestimated, if EMGs are obtained from the sole traditional bipolar sensor location recommended by SENIAM. This preliminary study, on 10 healthy children (mean age 10 yr), investigated whether surface EMG detected proximally and distally via two pairs of bipolar electrodes, within the medial gastrocnemius (MG), provides a significant difference in CCI estimates during non-dynamic (isometric dorsiflexion) and dynamic (swing phases of gait) conditions. Gait cycles were extracted from Inertial Measurement Unit sensors. Medial gastrocnemius activity was greater distally than proximally during plantar flexion when it acts as an agonist (~24±18%) and it was greater proximally during dorsiflexion (~23±9%) when it is acting as an antagonist. As a direct consequence, CCI estimates from the conventional sensor location were significantly different (~36%) from the CCIs computed by considering broader MG regions. This difference arose in all subjects during isometric efforts and in two of 10 healthy children during the swing phase of gait who presented cocontraction patterns ( [Formula: see text]). EMG bipolar sampling encompassing proximal and distal gastrocnemius muscle regions may reduce bias in CCI computation and provide a more representative and accurate cocontraction index that is especially important for comparisons to the diseased state.

Innovative aberration correction in ultrasound diagnostics with direct phase estimation for enhanced image quality.

The paper addresses a crucial challenge in medical radiology and introduces a novel general approach, which utilises applied mathematics and information technology techniques, for aberration correction in ultrasound diagnostics. Ultrasound imaging of inhomogeneous media inherently suffers from variations in ultrasonic speed between tissue. The characteristics of aberrations are unique to each patient due to tissue morphology. This study proposes a new phase aberration correction method based on the Fourier transform and leveraging of the synthetic aperture mode. The proposed method enables correction after the emission and reception of ultrasonic wave, allowing for the estimation of aberration profiles for different parts of the sonogram. To demonstrate the method's performance, this study included the conducting of experiments using a commercially available quality control phantom, an ex-vivo temporal human bone, and specially designed distortion layers. At a frequency of 2 MHz, the experiments demonstrated an increase of two-and-three-quarters in echo signal intensity and a decrease of nearly two-fold in the width of the angular distribution compared to the pre-correction state. However, it is important to note that the implementation of the method has a limitation, as it requires an aperture synthesis mode and access to raw RF data, which restricts use in common scanners. To ensure the reproducibility of the results, this paper provides public access to an in-house C + + code for aberration correction following the proposed method, as well as the dataset used in this study.

Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing.

Neurofeedback, utilizing an electroencephalogram (EEG) and/or a functional near-infrared spectroscopy (fNIRS) device, is a real-time measurement of brain activity directed toward controlling and optimizing brain function. This treatment has often been attributed to improvements in disorders such as ADHD, anxiety, depression, and epilepsy, among others. While there is evidence suggesting the efficacy of neurofeedback devices, the research is still inconclusive. The applicability of the measurements and parameters of consumer neurofeedback wearable devices has improved, but the literature on measurement techniques lacks rigorously controlled trials. This paper presents a survey and literary review of consumer neurofeedback devices and the direction toward clinical applications and diagnoses. Relevant devices are highlighted and compared for treatment parameters, structural composition, available software, and clinical appeal. Finally, a conclusion on future applications of these systems is discussed through the comparison of their advantages and drawbacks.

K-Means Clustering Machine Learning Approach Reveals Groups of Homogeneous Individuals With Unique Brain Activation, Task, and Performance Dynamics Using fNIRS.

Wearable functional near-infrared spectroscopy (fNIRS) for measuring brain function, in terms of hemodynamic responses, is pervading our everyday life and holds the potential to reliably classify cognitive load in a naturalistic environment. However, human's brain hemodynamic response, behavior, and cognitive and task performance vary, even within and across homogeneous individuals (with same training and skill sets), which limits the reliability of any predictive model for human. In the context of high-stakes tasks, such as in military and first-responder operations, the real-time monitoring of cognitive functions and relating it to the ongoing task, performance outcomes, and behavioral dynamics of the personnel and teams is invaluable. In this work, a portable wearable fNIRS system (WearLight) developed by the author was upgraded, and an experimental protocol was designed to image the prefrontal cortex (PFC) area of the brain of 25 healthy homogeneous participants in a naturalistic environment while participants performed n-back working memory (WM) tasks with four difficulty levels. The raw fNIRS signals were processed using a signal processing pipeline to derive the brain's hemodynamic responses. An unsupervised k-means machine learning (ML) clustering approach, utilizing the task-induced hemodynamic responses as input variables, suggested three unique participant groups. Task performance in terms of % correct, % missing, reaction time, inverse efficiency score (IES), and a proposed IES was extensively evaluated for each participant and the three groups. Results showed that, on average, brain hemodynamic response increased, whereas task performance degraded, with increasing WM load. However, the regression and correlation analysis of WM task, performance, and the brain's hemodynamic responses (TPH) revealed interesting hidden characteristics and the variation in the TPH relationship between groups. The proposed IES also served as a better scoring method that had distinct score ranges for different load levels as opposed to the overlapping scores of the traditional IES method. Results showed that the k-means clustering has the potential to find groups of individuals in an unsupervised manner using the brain's hemodynamic responses and to study the underlying relationship between the TPH in groups. Using the method presented in this paper, real-time monitoring of cognitive and task performance of soldiers, and preferentially forming small units to accomplish tasks based on the insights and goals may be helpful. The results showed that WearLight can image PFC, and this study also suggests future directions for the multi-modal body sensor network (BSN) combining advanced ML algorithms for real-time state classification, cognitive and physical performance prediction, and the mitigation of performance degradation in the high-stakes environment.

Multi-Modal Ensemble Deep Learning in Head and Neck Cancer HPV Sub-Typing.

Oropharyngeal Squamous Cell Carcinoma (OPSCC) is one of the common forms of heterogeneity in head and neck cancer. Infection with human papillomavirus (HPV) has been identified as a major risk factor for OPSCC. Therefore, differentiating the HPV-positive and negative cases in OPSCC patients is an essential diagnostic factor influencing future treatment decisions. In this study, we investigated the accuracy of a deep learning-based method for image interpretation and automatically detected the HPV status of OPSCC in routinely acquired Computed Tomography (CT) and Positron Emission Tomography (PET) images. We introduce a 3D CNN-based multi-modal feature fusion architecture for HPV status prediction in primary tumor lesions. The architecture is composed of an ensemble of CNN networks and merges image features in a softmax classification layer. The pipeline separately learns the intensity, contrast variation, shape, texture heterogeneity, and metabolic assessment from CT and PET tumor volume regions and fuses those multi-modal features for final HPV status classification. The precision, recall, and AUC scores of the proposed method are computed, and the results are compared with other existing models. The experimental results demonstrate that the multi-modal ensemble model with soft voting outperformed single-modality PET/CT, with an AUC of 0.76 and F1 score of 0.746 on publicly available TCGA and MAASTRO datasets. In the MAASTRO dataset, our model achieved an AUC score of 0.74 over primary tumor volumes of interest (VOIs). In the future, more extensive cohort validation may suffice for better diagnostic accuracy and provide preliminary assessment before the biopsy.

Relationship of compassion for self and others to sense of well-being of college students.

Objective: The mental health issues among college students have increased significantly in recent years. The primary purpose of this study was to explore and describe the relationship between self-compassion, compassion for others, and a sense of well-being among undergraduate college students. Participants: This study surveyed N = 651 college students aged 18-24 years at an urban university in the Northeast. Methods: Students completed an online survey through Survey Monkey that was comprised of questions about their selfcompassion, compassion for others, and overall sense of well-being. Results: The results indicate that self-compassion, compassion for others, and sense of well-being are positively related. Exploratory tests for sex differences showed that females reported having significantly higher compassion for others while males reported having substantially higher self-compassion. Conclusion: The authors discuss the implications of the results and suggest a need for more compassion education programs at institutions of higher education. Suggestions are made for future experimental research that measures the impact of self-compassion and compassion for others, especially after the COVID-19 pandemic that impacted many college students' education, economy, relationships, and job prospects.

An E-Textile Respiration Sensing System for NICU Monitoring: Design and Validation.

The world is witnessing a rising number of preterm infants who are at significant risk of medical conditions. These infants require continuous care in Neonatal Intensive Care Units (NICU). Medical parameters are continuously monitored in premature infants in the NICU using a set of wired, sticky electrodes attached to the body. Medical adhesives used on the electrodes can be harmful to the baby, causing skin injuries, discomfort, and irritation. In addition, respiration rate (RR) monitoring in the NICU faces challenges of accuracy and clinical quality because RR is extracted from electrocardiogram (ECG). This research paper presents a design and validation of a smart textile pressure sensor system that addresses the existing challenges of medical monitoring in NICU. We designed two e-textile, piezoresistive pressure sensors made of Velostat for noninvasive RR monitoring; one was hand-stitched on a mattress topper material, and the other was embroidered on a denim fabric using an industrial embroidery machine. We developed a data acquisition system for validation experiments conducted on a high-fidelity, programmable NICU baby mannequin. We designed a signal processing pipeline to convert raw time-series signals into parameters including RR, rise and fall time, and comparison metrics. The results of the experiments showed that the relative accuracies of hand-stitched sensors were 98.68 (top sensor) and 98.07 (bottom sensor), while the accuracies of embroidered sensors were 99.37 (left sensor) and 99.39 (right sensor) for the 60 BrPM test case. The presented prototype system shows promising results and demands more research on textile design, human factors, and human experimentation.

A modified surface EMG biomarker for gait assessment in spastic cerebral palsy.

OBJECTIVE: Muscle clinical metrics are crucial for spastic cocontraction management in children with Cerebral Palsy (CP). We investigated whether the ankle plantar flexors cocontraction index (CCI) normalized with respect to the bipedal heel rise (BHR) approach provides more robust spastic cocontraction estimates during gait than those obtained through the widely accepted standard maximal isometric plantar flexion (IPF). METHODS: Ten control and 10 CP children with equinus gait pattern performed the BHR and IPF testing and walked barefoot 10-m distance. We compared agonist medial gastrocnemius EMG during both testing and CCIs obtained as the ratios of antagonist EMG during swing phase of gait and either BHR or IPF agonist EMG. RESULTS: Agonist EMG values from the BHR were: (i) internally reliable (Cronbach's α = 0.993), (ii) ~50 ± 0.4% larger than IPF, (iii) and positively correlated. Derived CCIs were significantly smaller (p < 0.05) in both populations. CONCLUSION: The bipedal heel rise approach may be accurate enough to reveal greater agonist activity of plantar flexors than the maximal isometric plantar flexion and seems to be more appropriate to obtain cocontraction estimates during swing of gait. SIGNIFICANCE: This modified biomarker may represent a step forward towards improved accuracy of spastic gait management in pediatric.

The Validation of a Portable Functional NIRS System for Assessing Mental Workload.

Portable functional near-infrared spectroscopy (fNIRS) systems have the potential to image the brain in naturalistic settings. Experimental studies are essential to validate such fNIRS systems. Working memory (WM) is a short-term active memory that is associated with the temporary storage and manipulation of information. The prefrontal cortex (PFC) brain area is involved in the processing of WM. We assessed the PFC brain during n-back WM tasks in a group of 25 college students using our laboratory-developed portable fNIRS system, WearLight. We designed an experimental protocol with 32 n-back WM task blocks with four different pseudo-randomized task difficulty levels. The hemodynamic response of the brain was computed from the experimental data and the evaluated brain responses due to these tasks. We observed the incremental mean hemodynamic activation induced by the increasing WM load. The left-PFC area was more activated in the WM task compared to the right-PFC. The task performance was seen to be related to the hemodynamic responses. The experimental results proved the functioning of the WearLight system in cognitive load imaging. Since the portable fNIRS system was wearable and operated wirelessly, it was possible to measure the cognitive load in the naturalistic environment, which could also lead to the development of a user-friendly brain-computer interface system.

Melanoma in the Blink of an Eye: Pathologists' Rapid Detection, Classification, and Localization of Skin Abnormalities.

Expert radiologists can quickly extract a basic "gist" understanding of a medical image following less than a second exposure, leading to above-chance diagnostic classification of images. Most of this work has focused on radiology tasks (such as screening mammography), and it is currently unclear whether this pattern of results and the nature of visual expertise underlying this ability are applicable to pathology, another medical imaging domain demanding visual diagnostic interpretation. To further characterize the detection, localization, and diagnosis of medical images, this study examined eye movements and diagnostic decision-making when pathologists were briefly exposed to digital whole slide images of melanocytic skin biopsies. Twelve resident (N = 5), fellow (N = 5), and attending pathologists (N = 2) with experience interpreting dermatopathology briefly viewed 48 cases presented for 500 ms each, and we tracked their eye movements towards histological abnormalities, their ability to classify images as containing or not containing invasive melanoma, and their ability to localize critical image regions. Results demonstrated rapid shifts of the eyes towards critical abnormalities during image viewing, high diagnostic sensitivity and specificity, and a surprisingly accurate ability to localize critical diagnostic image regions. Furthermore, when pathologists fixated critical regions with their eyes, they were subsequently much more likely to successfully localize that region on an outline of the image. Results are discussed relative to models of medical image interpretation and innovative methods for monitoring and assessing expertise development during medical education and training.

WearLight: Toward a Wearable, Configurable Functional NIR Spectroscopy System for Noninvasive Neuroimaging.

Functional near-infrared spectroscopy (fNIRS) has emerged as an effective brain monitoring technique to measure the hemodynamic response of the cortical surface. Its wide popularity and adoption in recent time attribute to its portability, ease of use, and flexibility in multimodal studies involving electroencephalography. While fNIRS is still emerging on various fronts including hardware, software, algorithm, and applications, it still requires overcoming several scientific challenges associated with brain monitoring in naturalistic environments where the human participants are allowed to move and required to perform various tasks stimulating brain behaviors. In response to these challenges and demands, we have developed a wearable fNIRS system, WearLight that was built upon an Internet-of-Things embedded architecture for onboard intelligence, configurability, and data transmission. In addition, we have pursued detailed research and comparative analysis on the design of the optodes encapsulating an near-infrared light source and a detector into 3-D printed material. We performed rigorous experimental studies on human participants to test reliability, signal-to-noise ratio, and configurability. Most importantly, we observed that WearLight has a capacity to measure hemodynamic responses in various setups including arterial occlusion on the forearm and frontal lobe brain activity during breathing exercises in a naturalistic environment. Our promising experimental results provide an evidence of preliminary clinical validation of WearLight. This encourages us to move toward intensive studies involving brain monitoring.

Region-of-interest diffuse optical tomography system.

Diffuse optical tomography (DOT) using near-infrared light is a promising tool for non-invasive imaging of deep tissue. This technique is capable of quantitative reconstruction of absorption (µa) and scattering coefficient (µs) inhomogeneities in the tissue. The rationale for reconstructing the optical property map is that the absorption coefficient variation provides diagnostic information about metabolic and disease states of the tissue. The aim of DOT is to reconstruct the internal tissue cross section with good spatial resolution and contrast from noisy measurements non-invasively. We develop a region-of-interest scanning system based on DOT principles. Modulated light is injected into the phantom/tissue through one of the four light emitting diode sources. The light traversing through the tissue gets partially absorbed and scattered multiple times. The intensity and phase of the exiting light are measured using a set of photodetectors. The light transport through a tissue is diffusive in nature and is modeled using radiative transfer equation. However, a simplified model based on diffusion equation (DE) can be used if the system satisfies following conditions: (a) the optical parameter of the inhomogeneity is close to the optical property of the background, and (b) µs of the medium is much greater than µa (µs > > µa). The light transport through a highly scattering tissue satisfies both of these conditions. A discrete version of DE based on finite element method is used for solving the inverse problem. The depth of probing light inside the tissue depends on the wavelength of light, absorption, and scattering coefficients of the medium and the separation between the source and detector locations. Extensive simulation studies have been carried out and the results are validated using two sets of experimental measurements. The utility of the system can be further improved by using multiple wavelength light sources. In such a scheme, the spectroscopic variation of absorption coefficient in the tissue can be used to arrive at the oxygenation changes in the tissue.

Designing and testing a wearable, wireless fNIRS patch.

Optical brain monitoring using near infrared (NIR) light has got a lot of attention in order to study the complexity of the brain due to several advantages as oppose to other methods such as EEG, fMRI and PET. There are a few commercially available functional NIR spectroscopy (fNIRS) brain monitoring systems, but they are still non-wearable and pose difficulties in scanning the brain while the participants are in motion. In this work, we present our endeavors to design and test a low-cost, wireless fNIRS patch using NIR light sources at wavelengths of 770 and 830nm, photodetectors and a microcontroller to trigger the light sources, read photodetector's output and transfer data wirelessly (via Bluetooth) to a smart-phone. The patch is essentially a 3-D printed wearable system, recording and displaying the brain hemodynamic responses on smartphone, also eliminates the need for complicated wiring of the electrodes. We have performed rigorous lab experiments on the presented system for its functionality. In a proof of concept experiment, the patch detected the NIR absorption on the arm. Another experiment revealed that the patch's battery could last up to several hours with continuous fNIRS recording with and without wireless data transfer.