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The Fitzpatrick Skin Phototype Classification (FSPC) scale is widely used to categorize skin types but has limitations such as the underrepresentation of darker skin phototypes, low classification resolution, and subjectivity. These limitations may contribute to dermatological care disparities in patients with darker skin phototypes, including the misdiagnosis of wound healing progression and escalated dermatological disease severity. This study introduces (1) an optical sensor measuring reflected light across 410–940 nm, (2) an unsupervised K-means algorithm for skin phototype classification using broadband optical data, and (3) methods to optimize classification across the Near-ultraviolet-A, Visible, and Near-infrared spectra. The differentiation capability of the algorithm was compared to human assessment based on FSPC in a diverse participant population (n = 30) spanning an even distribution of the full FSPC scale. The FSPC assessment distinguished between light and dark skin phototypes (e.g., FSPC I vs. VI) at 560, 585, and 645 nm but struggled with more similar phototypes (e.g., I vs. II). The K-means algorithm demonstrated stronger differentiation across a broader range of wavelengths, resulting in better classification resolution and supporting its use as a quantifiable and reproducible method for skin type classification. We also demonstrate the optimization of this method for specific bandwidths of interest and their associated clinical implications. [ABSTRACT FROM AUTHOR]

An optoelectronic sensor was developed to measure the in-plane displacement between two parallel surfaces. This sensor used a photodetector, which was placed on one of the parallel surfaces, to measure the intensity of the red (R), green (G), blue (B), and white/clear (C) light spectra of a broad-spectrum light that was reflected off a color grid on the opposing surface. The in-plane displacement between these two surfaces caused a change in the reflected RGB and C light intensity, allowing the prediction of the displacement direction and magnitude by using a polynomial regression prediction algorithm to convert the RGB and C light intensity to in-plane displacement. Results from benchtop experiments showed that the sensor can achieve accurate displacement predictions with a coefficient of determination R2 > 0.97, a root mean squared error (RMSE) < 0.3 mm, and a mean absolute error (MAE) < 0.36 mm. By measuring the in-plane displacement between two surfaces, this sensor can be applied to measure the shear of a flexible layer, such as a shoe's insole or the lining of a limb prosthesis. This sensor would allow slippage detection in wearable devices such as orthotics, prostheses, and footwear to quantify the overfitting or underfitting of these devices. [ABSTRACT FROM AUTHOR]

There is an increasing need to accurately measure compressive force for biomedical and industrial applications. However, this need has not been fully addressed, as many sensors are bulky, have high power requirements, and/or are susceptible to electromagnetic interference. This paper presents an optoelectronics-based force sensor that can overcome the limitations of many sensors in the market. The sensor uses a light emitting diode (LED) to transmit visible broad-spectrum light into a photoresistor through an optically clear spacer on top of an elastomeric medium. In the absence of an external force, the light path is mostly blocked by the opaque elastomeric medium. Under a compressive force, the clear spacer compresses the elastomer, moving itself into the light path, and thus increasing the overall light transmission. The amount of light received by the photoresistor is used to quantify compressive force based on elastomer displacement/compression and a priori knowledge of elastomer stiffness. This sensing scheme was tested under eight different configurations: two different sized sensors with four types of elastomers per size (20A neoprene, 30A neoprene, 50A neoprene, and 75A styrene–butadiene rubber (SBR)). All configurations measured force with R2 > 0.97, RMSE < 1.9 N, and sensitivity values ranging from 17 to 485 N/V. This sensing scheme provides a low-cost, low-power method for accurate force sensing with a wide force range. [ABSTRACT FROM AUTHOR]

Magnetoelastic sensors, which undergo mechanical resonance when interrogated with magnetic fields, can be functionalized to measure various physical quantities and chemical/biological analytes by tracking their resonance behaviors. The unique wireless and functionalizable nature of these sensors makes them good candidates for biological sensing applications, from the detection of specific bacteria to tracking force loading inside the human body. In this study, we evaluate the viability of magnetoelastic sensors based on a commercially available magnetoelastic material (Metglas 2826 MB) for wirelessly monitoring the attachment and growth of human mesenchymal stromal cells (hMSCs) in 2D in vitro cell culture. The results indicate that the changes in sensor resonance are linearly correlated with cell quantity. Experiments using a custom-built monitoring system also demonstrated the ability of this technology to collect temporal profiles of cell growth, which could elucidate key stages of cell proliferation based on acute features in the profile. Additionally, there was no observed change in the morphology of cells after they were subjected to magnetic and mechanical stimuli from the monitoring system, indicating that this method for tracking cell growth may have minimal impact on cell quality and potency. [ABSTRACT FROM AUTHOR]

Magnetoelastic sensors, typically made of magnetostrictive and magnetically-soft materials, can be fabricated from commercially available materials into a variety of shapes and sizes for their intended applications. Since these sensors are wirelessly interrogated via magnetic fields, they are good candidates for use in both research and industry, where detection of environmental parameters in closed and controlled systems is necessary. Common applications for these sensors include the investigation of physical, chemical, and biological parameters based on changes in mass loading at the sensor surface which affect the sensor's behavior at resonance. To improve the performance of these sensors, optimization of sensor geometry, size, and detection conditions are critical to increasing their mass sensitivity and detectible range. This work focuses on investigating how the geometry of the sensor influences its resonance spectrum, including the sensor's shape, size, and aspect ratio. In addition to these factors, heterogeneity in resonance magnitude was mapped for the sensor surface and the effect of the magnetic bias field strength on the resonance spectrum was investigated. Analysis of the results indicates that the shape of the sensor has a strong influence on the emergent resonant modes. Reducing the size of the sensor decreased the sensor's magnitude of resonance. The aspect ratio of the sensor, along with the bias field strength, was also observed to affect the magnitude of the signal; over or under biasing and aspect ratio extremes were observed to decrease the magnitude of resonance, indicating that these parameters can be optimized for a given shape and size of magnetoelastic sensor. [ABSTRACT FROM AUTHOR]

In high concentrations, ionic species can be toxic in the body, catalyzing unwanted bioreactions, inhibiting enzymes, generating free radicals, in addition to having been associated with diseases like Alzheimer's and cancer. Although ionic species are ubiquitous in the environment in trace amounts, high concentrations of these metals are often found within industrial and agricultural waste runoff. Therefore, it remains a global interest to develop technologies capable of quickly and accurately detecting trace levels of ionic species, particularly in aqueous environments that naturally contain other competing/inhibiting ions. Herein, we provide an overview of the technologies that have been developed, including the general theory, design, and benefits/challenges associated with ion-selective electrode technologies (carrier-doped membranes, carbon-based varieties, enzyme inhibition electrodes). Notable variations of these electrodes will be highlighted, and a brief overview of associated electrochemical techniques will be given. [ABSTRACT FROM AUTHOR]

Wireless technologies are incorporated in implantable devices since at least the 1950s. With remote data collection and control of implantable devices, these wireless technologies help researchers and clinicians to better understand diseases and to improve medical treatments. Today, wireless technologies are still more commonly used for research, with limited applications in a number of clinical implantable devices. Recent development and standardization of wireless technologies present a good opportunity for their wider use in other types of implantable devices, which will significantly improve the outcomes of many diseases or injuries. This review briefly describes some common wireless technologies and modern advancements, as well as their strengths and suitability for use in implantable medical devices. The applications of these wireless technologies in treatments of orthopedic and cardiovascular injuries and disorders are described. This review then concludes with a discussion on the technical challenges and potential solutions of implementing wireless technologies in implantable devices. [ABSTRACT FROM AUTHOR]