The Difference in the Assessment of Knee Extension/Flexion Angles during Gait between Two Calibration Methods for Wearable Goniometer Sensors.

Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual's body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5-3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9-13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67-82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait.

Exploring the evolution of circular polarized light backscattered from turbid tissue-like disperse medium utilizing generalized Monte Carlo modeling approach with a combined use of Jones and Stokes-Mueller formalisms.

Significance: Phase retardation of circularly polarized light (CPL), backscattered by biological tissue, is used extensively for quantitative evaluation of cervical intraepithelial neoplasia, presence of senile Alzheimer's plaques, and characterization of biotissues with optical anisotropy. The Stokes polarimetry and Mueller matrix approaches demonstrate high potential in definitive non-invasive cancer diagnosis and tissue characterization. The ultimate understanding of CPL interaction with tissues is essential for advancing medical diagnostics, optical imaging, therapeutic applications, and the development of optical instruments and devices. Aim: We investigate propagation of CPL within turbid tissue-like scattering medium utilizing a combination of Jones and Stokes-Mueller formalisms in a Monte Carlo (MC) modeling approach. We explore the fundamentals of CPL memory effect and depolarization formation. Approach: The generalized MC computational approach developed for polarization tracking within turbid tissue-like scattering medium is based on the iterative solution of the Bethe-Salpeter equation. The approach handles helicity response of CPL scattered in turbid medium and provides explicit expressions for assessment of its polarization state. Results: Evolution of CPL backscattered by tissue-like medium at different conditions of observation in terms of source-detector configuration is assessed quantitatively. The depolarization of light is presented in terms of the coherence matrix and Stokes-Mueller formalism. The obtained results reveal the origins of the helicity flip of CPL depending on the source-detector configuration and the properties of the medium and are in a good agreement with the experiment. Conclusions: By integrating Jones and Stokes-Mueller formalisms, the combined MC approach allows for a more complete representation of polarization effects in complex optical systems. The developed model is suitable to imitate propagation of the light beams of different shape and profile, including Gaussian, Bessel, Hermite-Gaussian, and Laguerre-Gaussian beams, within tissue-like medium. Diverse configuration of the experimental conditions, coherent properties of light, and peculiarities of polarization can be also taken into account.

A Cost-Effective Method for Automatically Measuring Mechanical Parts Using Monocular Machine Vision.

Automatic measurements via image processing can accelerate measurements and provide comprehensive evaluations of mechanical parts. This paper presents a comprehensive approach to automating evaluations of planar dimensions in mechanical parts, providing significant advancements in terms of cost-effectiveness, accuracy, and repeatability. The methodology employed in this study utilizes a configuration comprising commonly available products in the industrial computer vision market, therefore enabling precise determinations of external contour specifications for mechanical components. Furthermore, it presents a functional prototype for making planar measurements by incorporating an improved subpixel edge-detection method, thus ensuring precise image-based measurements. The article highlights key concepts, describes the measurement procedures, and provides comparisons and traceability tests as a proof of concept for the system. The results show that this vision system did achieve suitable precision, with a mean error of 0.008 mm and a standard deviation of 0.0063 mm, when measuring gauge blocks of varying lengths at different heights. Moreover, when evaluating a circular sample, the system resulted in a maximum deviation of 0.013 mm, compared to an alternative calibrated measurement machine. In conclusion, the prototype validates the methods for planar dimension evaluations, highlighting the potential for enhancing manual measurements, while also maintaining accessibility. The presented system expands the possibilities of machine vision in manufacturing, especially in cases where the cost or agility of current systems is limited.

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques.

This article describes chemical and physical parameters, including their role in the storage, trade, and processing of potatoes, as well as their nutritional properties and health benefits resulting from their consumption. An analysis of the share of losses occurring during the production process is presented. The methods and applications used in recent years to estimate the physical and chemical parameters of potatoes during their storage and processing, which determine the quality of potatoes, are presented. The potential of the technologies used to classify the quality of potatoes, mechanical and ultrasonic, and image processing and analysis using vision systems, as well as their use in applications with artificial intelligence, are discussed.

[Design and Validation of Accuracy Assessment Tools and Algorithms for Optical Positioning System in Surgical Navigation].

The precision of optical positioning system is one of the most important factors which affects the precision of navigation guided surgery. In this study, an efficient and low-cost tool and its algorithm were proposed to evaluate the accuracy of optical positioning system based on the ablation scenario of liver cancer, and two validation experiments were designed. Experimental results show that the tool and its algorithm can evaluate the accuracy of the current positioning system accurately and efficiently.

Lane Departure Assessment via Enhanced Single Lane-Marking.

Vision-based Lane departure warning system (LDWS) has been widely used in modern vehicles to improve drivability and safety. In this paper, a novel LDWS with precise positioning is proposed. Calibration strategy is first presented through a 3D camera imaging model with only three parallel and equally spaced lines, where the three angles of rotation for the transformation from the camera coordinate system to the world coordinate system are deduced. Then camera height is calculated compared to the previous works using a measured one with potential errors. A criterion for lane departure warning with only one of the two lane-markings is proposed to estimate both yaw angle and distance between the lane-markings and the vehicle. Experiments show that calibration strategy can be easily set up and achieve an average of 98.95% accuracy on the lane departure assessment.

Optical Waveguides and Integrated Optical Devices for Medical Diagnosis, Health Monitoring and Light Therapies.

Optical waveguides and integrated optical devices are promising solutions for many applications, such as medical diagnosis, health monitoring and light therapies. Despite the many existing reviews focusing on the materials that these devices are made from, a systematic review that relates these devices to the various materials, fabrication processes, sensing methods and medical applications is still seldom seen. This work is intended to link these multidisciplinary fields, and to provide a comprehensive review of the recent advances of these devices. Firstly, the optical and mechanical properties of optical waveguides based on glass, polymers and heterogeneous materials and fabricated via various processes are thoroughly discussed, together with their applications for medical purposes. Then, the fabrication processes and medical implementations of integrated passive and active optical devices with sensing modules are introduced, which can be used in many medical fields such as drug delivery and cardiovascular healthcare. Thirdly, wearable optical sensing devices based on light sensing methods such as colorimetry, fluorescence and luminescence are discussed. Additionally, the wearable optical devices for light therapies are introduced. The review concludes with a comprehensive summary of these optical devices, in terms of their forms, materials, light sources and applications.

Critical assessment of relevant methods in the field of biosensors with direct optical detection based on fibers and waveguides using plasmonic, resonance, and interference effects.

Direct optical detection has proven to be a highly interesting tool in biomolecular interaction analysis to be used in drug discovery, ligand/receptor interactions, environmental analysis, clinical diagnostics, screening of large data volumes in immunology, cancer therapy, or personalized medicine. In this review, the fundamental optical principles and applications are reviewed. Devices are based on concepts such as refractometry, evanescent field, waveguides modes, reflectometry, resonance and/or interference. They are realized in ring resonators; prism couplers; surface plasmon resonance; resonant mirror; Bragg grating; grating couplers; photonic crystals, Mach-Zehnder, Young, Hartman interferometers; backscattering; ellipsometry; or reflectance interferometry. The physical theories of various optical principles have already been reviewed in detail elsewhere and are therefore only cited. This review provides an overall survey on the application of these methods in direct optical biosensing. The "historical" development of the main principles is given to understand the various, and sometimes only slightly modified variations published as "new" methods or the use of a new acronym and commercialization by different companies. Improvement of optics is only one way to increase the quality of biosensors. Additional essential aspects are the surface modification of transducers, immobilization strategies, selection of recognition elements, the influence of non-specific interaction, selectivity, and sensitivity. Furthermore, papers use for reporting minimal amounts of detectable analyte terms such as value of mass, moles, grams, or mol/L which are difficult to compare. Both these essential aspects (i.e., biochemistry and the presentation of LOD values) can be discussed only in brief (but references are provided) in order to prevent the paper from becoming too long. The review will concentrate on a comparison of the optical methods, their application, and the resulting bioanalytical quality.

[A Fluorescence Diffusion Optical Tomography System Based on Lattice Boltzmann Forward Model].

Fluorescence Diffuse Optical Tomography (FDOT) is significant for biomedical applications, such as medical diagnostics, drug research. The fluorescence probe distribution in biological tissues can be quantitatively and non-invasively obtained via FDOT, achieving targets positioning and detection. In order to reduce the cost of FDOT, this study designs a FDOT system based on Lattice Boltzmann forward model. The system is used to realize two functions of light propagation simulation and FDOT reconstruction, and is composed of a parameter module, an algorithm module, a result display module and a data interaction module. In order to verify the effectiveness of the platform, this study carries out the light propagation simulation experiment and the FDOT reconstruction experiment, respectively comparing the Monte Carlo (MC) light propagation simulation results and the real position of the light source to be reconstructed. Experiments show that the proposed FDOT system has good reliability and has a high promotion value.

Low-cost hyper-spectral imaging system using a linear variable bandpass filter for agritech applications.

Hyperspectral imaging for agricultural applications provides a solution for non-destructive, large-area crop monitoring. However, current products are bulky and expensive due to complicated optics and electronics. A linear variable filter was developed for implementation into a prototype hyperspectral imaging camera that demonstrates good spectral performance between 450 and 900 nm. Equipped with a feature extraction and classification algorithm, the proposed system can be used to determine potato plant health with ∼88% accuracy. This algorithm was also capable of species identification and is demonstrated as being capable of differentiating between rocket, lettuce, and spinach. Results are promising for an entry-level, low-cost hyperspectral imaging solution for agriculture applications.

A Cost-Effective and Portable Optical Sensor System to Estimate Leaf Nitrogen and Water Contents in Crops.

Non-invasive determination of leaf nitrogen (N) and water contents is essential for ensuring the healthy growth of the plants. However, most of the existing methods to measure them are expensive. In this paper, a low-cost, portable multispectral sensor system is proposed to determine N and water contents in the leaves, non-invasively. Four different species of plants-canola, corn, soybean, and wheat-are used as test plants to investigate the utility of the proposed device. The sensor system comprises two multispectral sensors, visible (VIS) and near-infrared (NIR), detecting reflectance at 12 wavelengths (six from each sensor). Two separate experiments were performed in a controlled greenhouse environment, including N and water experiments. Spectral data were collected from 307 leaves (121 for N and 186 for water experiment), and the rational quadratic Gaussian process regression (GPR) algorithm was applied to correlate the reflectance data with actual N and water content. By performing five-fold cross-validation, the N estimation showed a coefficient of determination () of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola showed an of 18.02%, corn showed an of 68.41%, soybean showed an of 46.38%, and wheat showed an of 64.58%. The result reveals that the proposed low-cost sensor with an appropriate regression model can be used to determine N content. However, further investigation is needed to improve the water estimation results using the proposed device.

ODX: A Fitness Tracker-Based Device for Continuous Bacterial Growth Monitoring.

Continuous monitoring of bacterial growth in aqueous media is a crucial process in academic research as well as in the biotechnology industry. Bacterial growth is usually monitored by measuring the optical density of bacteria in liquid media, using benchtop spectrophotometers. Due to the large form factor of the existing spectrophotometers, they cannot be used for live monitoring of the bacteria inside bacterial incubation chambers. Additionally, the use of benchtop spectrometers for continuous monitoring requires multiple samplings and is labor intensive. To overcome these challenges, we have developed an optical density measuring device (ODX) by modifying a generic fitness tracker. The resulting ODX device is an ultraportable and low-cost device that can be used inside bacterial incubators for real-time monitoring even while shaking is in progress. We evaluated the performance of ODX with different bacterial types and growth conditions and compared it with a commercial benchtop spectrophotometer. In all cases, ODX showed comparable performance to that of the standard benchtop spectrophotometer. Finally, we demonstrate a simple and useful smartphone application whereby the user is notified when the bacterial concentration reaches the targeted value. Due to its potential for automation and mass production, we believe that the ODX has a wide range of applications in biotechnology research and industry.

Validation of speed-resolved laser Doppler perfusion in a multimodal optical system using a blood-flow phantom.

The PeriFlux 6000 EPOS system combines diffuse reflectance spectroscopy (DRS) and laser Doppler flowmetry (LDF) for the assessment of oxygen saturation (expressed in percentage), red blood cell (RBC) tissue fraction (expressed as volume fraction, %RBC), and perfusion (%RBC × mm / s) in the microcirculation. It also allows the possibility of separating the perfusion into three speed regions (0 to 1, 1 to 10, and >10 mm / s). We evaluate the speed-resolved perfusion components, i.e., the relative amount of perfusion within each speed region, using a blood-flow phantom. Human blood was pumped through microtubes with an inner diameter of 0.15 mm. Measured DRS and LDF spectra were compared to Monte Carlo-simulated spectra in an optimization routine, giving the best-fit parameters describing the measured spectra. The root-mean-square error for each of the three speed components (0 to 1, 1 to 10, and >10 mm / s, respectively) when describing the blood-flow speed in the microtubes was 2.9%, 8.1%, and 7.7%. The presented results show that the system can accurately discriminate blood perfusion originating from different blood-flow speeds, which may enable improved measurement of healthy and dysfunctional microcirculatory flow.

Real-Time Sensing with Patterned Plasmonic Substrates and a Compact Imager Chip.

Optical sensing is an important research field due to its proven ability to be extremely sensitive, nondestructive, and applicable to sensing a wide range of chemical, thermal, electric, or magnetic phenomena. Beyond traditional optical sensors that often rely on bulky setups, plasmonic nanostructures can offer many advantages based on their sensitivity, compact form, cost-effectiveness, multiplexing compatibility, and compatibility with many standard semiconductor nanofabrication techniques. In particular, plasmon-enhanced optical transmission through arrays of nanostructured holes has led to the development of a new generation of optical sensors. In this chapter we present a simple fabrication technique to use plasmonic nanostructures as compact sensors. We position the nanohole array, an LED illumination source, and a spacer layer directly on top of a standard complementary metal-oxide-semiconductor (CMOS) imager chip. This setup is a viable sensor platform in both liquid and gas environments. These devices could operate as low-cost sensors for environmental monitoring, security, food safety, or monitoring small-molecule binding to extract affinity information and binding constants.

Data-driven model optimization for optically pumped magnetometer sensor arrays.

Optically pumped magnetometers (OPMs) have reached sensitivity levels that make them viable portable alternatives to traditional superconducting technology for magnetoencephalography (MEG). OPMs do not require cryogenic cooling and can therefore be placed directly on the scalp surface. Unlike cryogenic systems, based on a well-characterised fixed arrays essentially linear in applied flux, OPM devices, based on different physical principles, present new modelling challenges. Here, we outline an empirical Bayesian framework that can be used to compare between and optimise sensor arrays. We perturb the sensor geometry (via simulation) and with analytic model comparison methods estimate the true sensor geometry. The width of these perturbation curves allows us to compare different MEG systems. We test this technique using simulated and real data from SQUID and OPM recordings using head-casts and scanner-casts. Finally, we show that given knowledge of underlying brain anatomy, it is possible to estimate the true sensor geometry from the OPM data themselves using a model comparison framework. This implies that the requirement for accurate knowledge of the sensor positions and orientations a priori may be relaxed. As this procedure uses the cortical manifold as spatial support there is no co-registration procedure or reliance on scalp landmarks.

A shape-memory and spiral light-emitting device for precise multisite stimulation of nerve bundles.

We previously demonstrated that for long-term spastic limb paralysis, transferring the seventh cervical nerve (C7) from the nonparalyzed side to the paralyzed side results in increase of 17.7 in Fugl-Meyer score. One strategy for further improvement in voluntary arm movement is selective activation of five target muscles innervated by C7 during recovery process. In this study, we develop an implantable multisite optogenetic stimulation device (MOSD) based on shape-memory polymer. Two-site stimulation of sciatic nerve bundles by MOSD induces precise extension or flexion movements of the ankle joint, while eight-site stimulation of C7 nerve bundles induce selective limb movement. Long-term implant of MOSD to mice with severed and anastomosed C7 nerve is proven to be both safe and effective. Our work opens up the possibility for multisite nerve bundle stimulation to induce highly-selective activations of limb muscles, which could inspire further applications in neurosurgery and neuroscience research.

MyoRobot 2.0: An advanced biomechatronics platform for automated, environmentally controlled skeletal muscle single fiber biomechanics assessment employing inbuilt real-time optical imaging.

We present an enhanced version of our previously engineered MyoRobot system for reliable, versatile and automated investigations of skeletal muscle or linear polymer material (bio)mechanics. That previous version already replaced strenuous manual protocols to characterize muscle biomechanics properties and offered automated data analysis. Here, the system was further improved for precise control over experimental temperature and muscle single fiber sarcomere length. Moreover, it also now features the calculation of fiber cross-sectional area via on-the-fly optical diameter measurements using custom-engineered microscope optics. With this optical systems integration, the MyoRobot 2.0 allows to tailor a wealth of recordings for relevant physiological parameters to be sequentially executed in living single myofibers. Research questions include assessing temperature-dependent performance of active or passive biomechanics, or automated control over length-tension or length-velocity relations. The automatically obtained passive stress-strain relationships and elasticity modules are important parameters in (bio)material science. From the plethora of possible applications, we validated the improved MyoRobot 2.0 by assessing temperature-dependent myofibrillar Ca2+ sensitivity, passive axial compliance and Young's modulus. We report a Ca2+ desensitization and a narrowed dynamic range at higher temperatures in murine M. extensor digitorum longus single fibers. In addition, an increased axial mechanical compliance in single muscle fibers with Young's moduli between 40 - 60 kPa was found, compatible with reported physiological ranges. These applications demonstrate the robustness of our MyoRobot 2.0 for facilitated single muscle fiber biomechanics assessment.

Theoretical and experimental modeling of interstitial laser hyperthermia with surface cooling device using Nd3+-doped nanoparticles.

To improve methods of laser hyperthermia for the treatment of bulk malignant neoplasms, an urgent task is the development of techniques and devices that automatically control heating at a given tissue depth and ensure its uniformity. The article proposes the concept of a system for performing hyperthermia with real-time spectroscopic temperature control and surface cooling, which allows to record spectra of diffusely scattered radiation and fluorescent signal from various depths of biological tissues by the means of the variation of the angle and distance between the fiber source of laser radiation and the receiving fiber. Theoretical and experimental modeling of the spatial distribution of diffusely scattered radiation and temperature inside the tissue with a fiber optic device providing surface cooling of the irradiated tissue, and recording spectral information from a given depth in real time, is presented. Simulation of radiation propagation in biological tissues, depending on the distance between the source and the receiver and the angle of their tilt, was carried out using the Monte Carlo method. Modeling of the temperature distribution inside the tissues was carried out by means of a numerical solution of the heat conduction equation. Experimental modeling was carried out on phantoms of biological tissues simulating their scattering properties as well as accumulation of the investigated nanoparticles doped with Nd3+ ions. It was shown that inorganic nanoparticles doped with rare-earth Nd3+ ions can be used as temperature labels for feedback to the therapeutic laser. According to the results of the theoretical simulation, optimal configurations of the relative arrangement of the fibers were chosen, as well as the optimum surface cooling temperatures for the given power densities. The heating of the phantom of the neoplasm containing the investigated nanoparticles doped with Nd3+ ions by laser radiation with an 805-nm wavelength and power density of 1 W/cm2 up to 42 °C at a depth of 1 cm while maintaining the surface temperature within the limits of the norm was demonstrated.

Advances in Optical Sensing and Bioanalysis Enabled by 3D Printing.

The recent explosion of 3D printing applications in scientific literature has expanded the speed and effectiveness of analytical technological development. 3D printing allows for manufacture that is simply designed in software and printed in-house with nearly no constraints on geometry, and analytical methodologies can thus be prototyped and optimized with little difficulty. The versatility of methods and materials available allows the analytical chemist or biologist to fine-tune both the structural and functional portions of their apparatus. This flexibility has more recently been extended to optical-based bioanalysis, with higher resolution techniques and new printing materials opening the door for a wider variety of optical components, plasmonic surfaces, optical interfaces, and biomimetic systems that can be made in the laboratory. There have been discussions and reviews of various aspects of 3D printing technologies in analytical chemistry; this Review highlights recent literature and trends in their applications to optical sensing and bioanalysis.