Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions.

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

Sensing Technologies for Autism Spectrum Disorder Screening and Intervention.

This paper reviews the state-of-the-art in sensing technologies that are relevant for Autism Spectrum Disorder (ASD) screening and therapy. This disorder is characterized by difficulties in social communication, social interactions, and repetitive behaviors. It is diagnosed during the first three years of life. Early and intensive interventions have been shown to improve the developmental trajectory of the affected children. The earlier the diagnosis, the sooner the intervention therapy can begin, thus, making early diagnosis an important research goal. Technological innovations have tremendous potential to assist with early diagnosis and improve intervention programs. The need for careful and methodological evaluation of such emerging technologies becomes important in order to assist not only the therapists and clinicians in their selection of suitable tools, but to also guide the developers of the technologies in improving hardware and software. In this paper, we survey the literatures on sensing technologies for ASD and we categorize them into eye trackers, movement trackers, electrodermal activity monitors, tactile sensors, vocal prosody and speech detectors, and sleep quality assessment devices. We assess their effectiveness and study their limitations. We also examine the challenges faced by this growing field that need to be addressed before these technologies can perform up to their theoretical potential.

Photocatalytic degradation of organic dyes using reduced graphene oxide (rGO).

Photocatalysts have developed into a successful strategy for degrading synthetic and organic toxins, such as chemicals and dyes, in wastewater. In this study, graphene oxide was reduced at different temperatures and used for degrading indigo carmine and neutral red dyes. The wide surface areas, strong adsorption sites, and oxygen functionalities of reduced graphene oxide (rGO) at 250 °C (rGO-250) produced more photocatalytic degradation efficiency and adsorption percentage. The catalyst dosage, initial dye concentration, solution pH and recyclability were all used to optimize the photocatalytic activity of rGO-250. This research presents a capable nano-adsorbent photocatalyst for the efficient degradation of organic dyes. GO and rGOs were also investigated for carbon dioxide (CO2) absorption properties. Results showed that rGO-250 has better CO2 adsorption properties than other rGOs. Overall, it was observed that rGO-250 has better photocatalytic and CO2 adsorption capabilities compared to graphene oxide reduced at different temperatures.

A practical perspective for chromatic orthogonality for implementing in photolithography.

Theoretically, it is more challenging to anticipate the conversion and selectivity of a photochemical experiment compared to thermally generated reactivity. This is due to the interaction of light with a photoreactive substrate. Photochemical reactions do not yet receive the same level of broad analytical study. Here, we close this research gap by presenting a methodology for statistically forecasting the time-dependent progression of photoreactions using widely available LEDs. This study uses NiS/ZnO in perovskite (MAPbI3) solar cells as an additive (5 volume %). The effect of monolithic perovskite solar cells (mPSCs) on forecasting the wavelength of LEDs has been carefully investigated using various characterization methods, including X-ray diffraction (XRD) and Transmission electron microscopy (TEM). The photocatalytic activity was analyzed by measuring the voltage produced. Various factors like selectivity, stability and sensitivity were also examined. This work provides a new perspective to validate NiS/ZnO photocatalysts for predicting the wavelength of different light sources and to apply in photolithography.

Revealing the improved sensitivity of PEDOT:PSS/PVA thin films through secondary doping and their strain sensors application.

The field of strain sensing involves the ability to measure an electrical response that corresponds to a strain. The integration of synthetic and conducting polymers can create a flexible strain sensor with a wide range of applications, including soft robotics, sport performance monitoring, gaming and virtual reality, and healthcare and biomedical engineering. However, the use of insulating synthetic polymers can impede the semiconducting properties of sensors, which may reduce sensor sensitivity. Previous research has shown that the doping process can significantly enhance the electrical performance and ionic conduction of conducting polymers, thereby strengthening their potential for use in electronic devices. However the full effects of secondary doping on the crystallinity, stretchability, conductivity, and sensitivity of conducting polymer blends have not been studied. In this study, we investigated the effects of secondary doping on the properties of poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)/poly(vinyl alcohol) (PEDOT:PSS/PVA) polymer blend thin films and their potential use as strain sensors. The thin films were prepared using a facile drop-casting method. Morphology analysis using profilometry and atomic force microscopy confirmed the occurrence of phase segregation and revealed surface roughness values. This evidence provided a comprehensive understanding of the chemical interactions and physical properties of the thin films, and the effects of doping on these properties. The best films were selected and applied as sensitive strain sensors. EG-PEDOT:PSS/PVA thin films showing a significant increase of conductivity values from the addition of 1 vol% to 12 vol% addition, with conductivity values of 8.51 × 10-5 to 9.42 × 10-3 S cm-1. Our 12% EG-PEDOT:PSS/PVA sensors had the highest GF value of 2000 too. We compared our results with previous studies on polymeric sensors, and it was found that our sensors quantitatively had better GF values. Illustration that demonstrates the DMSO and EG dopant effects on PEDOT:PSS structure through bonding interaction, crystallinity, thermal stability, surface roughness, conductivity and stretchability was also provided. This study suggests a new aspect of doping interaction that can enhance the conductivity and sensitivity of PEDOT:PSS for device applications.

Cardiovascular autonomic neuropathy is associated with increased glycemic variability driven by hyperglycemia rather than hypoglycemia in patients with diabetes.

AIM: Cardiac autonomic neuropathy (CAN) has been suggested to be associated with hypoglycemia and impaired hypoglycemia unawareness. We have assessed the relationship between CAN and extensive measures of glucose variability (GV) in patients with type 1 and type 2 diabetes. METHODS: Participants with diabetes underwent continuous glucose monitoring (CGM) to obtain measures of GV and the extent of hyperglycemia and hypoglycemia and cardiovascular autonomic reflex testing. RESULTS: Of the 40 participants (20 T1DM and 20 T2DM) (aged 40.70 ± 13.73 years, diabetes duration 14.43 ± 7.35 years, HbA1c 8.85 ± 1.70%), 23 (57.5%) had CAN. Despite a lower coefficient of variation (CV) (31.26 ± 11.87 vs. 40.33 ± 11.03, P = 0.018), they had a higher CONGA (8.42 ± 2.58 vs. 6.68 ± 1.88, P = 0.024) with a lower median LBGI (1.60 (range: 0.20-3.50) vs. 4.90 (range: 3.20-7.40), P = 0.010) and percentage median time spent in hypoglycemia (4 (range:4-13) vs. 1 (range:0-5), P = 0.008), compared to those without CAN. The percentage GRADEEuglycemia (3.30 ± 2.78 vs. 5.69 ± 3.09, P = 0.017) and GRADEHypoglycemia (0.3 (range: 0 - 3.80) vs. 1.8 (range: 0.9-6.5), P = 0.036) were significantly lower, while the percentage median GRADEHyperglycemia (95.45 (range:93-98) vs. 91.6 (82.8-95.1), P = 0.013) was significantly higher in participants with CAN compared to those without CAN. CONCLUSION: CAN was associated with increased glycemic variability with less time in euglycemia attributed to a greater time in hyperglycemia but not hypoglycemia.

Dataset for influence of visual and haptic feedback on the detection of threshold forces in a surgical grasping task.

The data is related to minimal force thresholds perception in robotic surgical grasping applications. The experimental setup included an indenter-based haptic device acting on the fingertip of a participant and a visual system that displays grasping tasks by a surgical grasper. The experiments included the display of two presentations at different force levels (i.e., grasping and indentation) in three different modes, namely, visual-alone, haptic-alone, and bimodal (i.e., combined). For each mode, the participants were asked to identify which of the two presentations was higher. Each experiment was repeated till the termination conditions were met. Sixty participants took part in these experiments. The experiments were randomized and the threshold forces were calculated based on an algorthim. The datasets contain the individual responses of each participant, the threshold forces calculations, and the number of iterations.

Extended Reality "X-Reality" for Prosthesis Training of Upper-Limb Amputees: A Review on Current and Future Clinical Potential.

The rejection rates of upper-limb prosthetic devices in adults are high, currently averaging 26% and 23% for body-powered and electric devices, respectively. While many factors influence acceptance, prosthesis training methods relying on novel virtual reality systems have been cited as a critical factor capable of increasing the likelihood of long-term, full-time use. Despite that, these implementations have not yet garnered widespread traction in the clinical setting, and their use remains immaterial. This review aims to explore the reasons behind this situation by identifying trends in existing research that seek to advance Extended Reality "X-Reality" systems for the sake of upper-limb prosthesis rehabilitation and, secondly, analyzing barriers and presenting potential pathways to deployment for successful adoption in the future. The search yielded 42 research papers that were divided into two categories. The first category included articles that focused on the technical aspect of virtual prosthesis training. Articles in the second category utilize user evaluation procedures to ensure applicability in a clinical environment. The review showed that 75% of articles that conducted whole system testing experimented with non-immersive virtual systems. Furthermore, there is a shortage of experiments performed with amputee subjects. From the large-scale studies analyzed, 71% of those recruited solely non-disabled participants. This paper shows that X-Reality technologies for prosthesis rehabilitation of upper-limb amputees carry significant benefits. Nevertheless, much still must be done so that the technology reaches widespread clinical use.

Guidelines for Robot-to-Human Handshake From the Movement Nuances in Human-to-Human Handshake.

The handshake is the most acceptable gesture of greeting in many cultures throughout many centuries. To date, robotic arms are not capable of fully replicating this typical human gesture. Using multiple sensors that detect contact forces and displacements, we characterized the movements that occured during handshakes. A typical human-to-human handshake took around 3.63 s (SD = 0.45 s) to perform. It can be divided into three phases: reaching (M = 0.92 s, SD = 0.45 s), contact (M = 1.96 s, SD = 0.46 s), and return (M = 0.75 s, SD = 0.12 s). The handshake was further investigated to understand its subtle movements. Using a multiphase jerk minimization model, a smooth human-to-human handshake can be modelled with fifth or fourth degree polynomials at the reaching and return phases, and a sinusoidal function with exponential decay at the contact phase. We show that the contact phase (1.96 s) can be further divided according to the following subphases: preshake (0.06 s), main shake (1.31 s), postshake (0.06 s), and a period of no movement (0.52 s) just before both hands are retracted. We compared these to the existing handshake models that were proposed for physical human-robot interaction (pHRI). From our findings in human-to-human handshakes, we proposed guidelines for a more natural handshake movement between humanoid robots and their human partners.

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection.

Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.

Breath Analysis for the In Vivo Detection of Diabetic Ketoacidosis.

Human breath analysis of volatile organic compounds has gained significant attention recently because of its rapid and noninvasive potential to detect various metabolic diseases. The detection of ketones in the breath and blood is key to diagnosing and managing diabetic ketoacidosis (DKA) in patients with type 1 diabetes. It may also be of increasing importance to detect euglycemic ketoacidosis in patients with type 1 or type 2 diabetes or heart failure, treated with sodium-glucose transporter-2 inhibitors (SGLT2-i). The present research evaluates the efficiency of colorimetry for detecting acetone and ethanol in exhaled human breath with the response time, pH effect, temperature effect, concentration effect, and selectivity of dyes. Using the proposed multidye system, we obtained a detection limit of 0.0217 ppm for acetone and 0.029 ppm for ethanol in the detection range of 0.05-50 ppm. A smartphone-assisted unit consisting of a portable colorimetric device was used to detect relative red/green/blue values within 60 s of the interface for practical and real-time application. The developed method could be used for rapid, low-cost detection of ketones in patients with type 1 diabetes and DKA and patients with type 1 or type 2 diabetes or heart failure treated with SGLT2-I and euglycemic ketoacidosis.

Continuous glucose monitoring reveals a novel association between duration and severity of hypoglycemia, and small nerve fiber injury in patients with diabetes.

Objective: Continuous glucose monitoring (CGM) has revealed that glycemic variability and low time in range are associated with albuminuria and retinopathy. We have investigated the relationship between glucose metrics derived from CGM and a highly sensitive measure of neuropathy using corneal confocal microscopy in participants with type 1 and type 2 diabetes. Methods: A total of 40 participants with diabetes and 28 healthy controls underwent quantification of corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD), corneal nerve fiber length (CNFL) and inferior whorl length (IWL) and those with diabetes underwent CGM for four consecutive days. Results: CNBD was significantly lower in patients with high glycemic variability (GV) compared to low GV (median (range) (25.0 (19.0-37.5) vs 38.6 (29.2-46.9); P = 0.007); in patients who spent >4% compared to <4% time in level 1 hypoglycemia (54-69 mg/dL) (25.0 (22.9-37.5) vs 37.5 (29.2-46.9); P = 0.045) and in patients who spent >1% compared to <1% time in level 2 hypoglycemia (<54 mg/dL) (25.0 (19.8-41.7) vs 35.4 (28.1-44.8); P = 0.04). Duration in level 1 hypoglycemia correlated with CNBD (r = -0.342, P = 0.031). Duration in level 1 (181-250 mg/dL) and level 2 (>250 mg/dL) hyperglycemia did not correlate with CNFD (P > 0.05), CNBD (P > 0.05), CNFL (P > 0.05) or IWL (P > 0.05). Conclusions: Greater GV and duration in hypoglycemia, rather than hyperglycemia, are associated with nerve fiber loss in diabetes.

Prosthetic finger phalanges with lifelike skin compliance for low-force social touching interactions.

BACKGROUND: Prosthetic arms and hands that can be controlled by the user's electromyography (EMG) signals are emerging. Eventually, these advanced prosthetic devices will be expected to touch and be touched by other people. As realistic as they may look, the currently available prosthetic hands have physical properties that are still far from the characteristics of human skins because they are much stiffer. In this paper, different configurations of synthetic finger phalanges have been investigated for their skin compliance behaviour and have been compared with the phalanges of the human fingers and a phalanx from a commercially available prosthetic hand. METHODS: Handshake tests were performed to identify which areas on the human hand experience high contact forces. After these areas were determined, experiments were done on selected areas using an indenting probe to obtain the force-displacement curves. Finite element simulations were used to compare the force-displacement results of the synthetic finger phalanx designs with that of the experimental results from the human and prosthetic finger phalanges. The simulation models were used to investigate the effects of (a) varying the internal topology of the finger phalanx and (b) varying different materials for the internal and external layers. RESULTS AND CONCLUSIONS: During handshake, the high magnitudes of contact forces were observed at the areas where the full grasping enclosure of the other person's hand can be achieved. From these areas, the middle phalanges of the (a) little, (b) ring, and (c) middle fingers were selected. The indentation experiments on these areas showed that a 2 N force corresponds to skin tissue displacements of more than 2 mm. The results from the simulation model show that introducing an open pocket with 2 mm height on the internal structure of synthetic finger phalanges increased the skin compliance of the silicone material to 235% and the polyurethane material to 436%, as compared to a configuration with a solid internal geometry. In addition, the study shows that an indentation of 2 N force on the synthetic skin with an open pocket can also achieve a displacement of more than 2 mm, while the finger phalanx from a commercially available prosthetic hand can only achieve 0.2 mm.

Artificial skin ridges enhance local tactile shape discrimination.

One of the fundamental requirements for an artificial hand to successfully grasp and manipulate an object is to be able to distinguish different objects' shapes and, more specifically, the objects' surface curvatures. In this study, we investigate the possibility of enhancing the curvature detection of embedded tactile sensors by proposing a ridged fingertip structure, simulating human fingerprints. In addition, a curvature detection approach based on machine learning methods is proposed to provide the embedded sensors with the ability to discriminate the surface curvature of different objects. For this purpose, a set of experiments were carried out to collect tactile signals from a 2 × 2 tactile sensor array, then the signals were processed and used for learning algorithms. To achieve the best possible performance for our machine learning approach, three different learning algorithms of Naïve Bayes (NB), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) were implemented and compared for various parameters. Finally, the most accurate method was selected to evaluate the proposed skin structure in recognition of three different curvatures. The results showed an accuracy rate of 97.5% in surface curvature discrimination.

Datasets for recognition of aggressive interactions of children toward robotic toys.

The data is related to unwanted interactions between a person and a small robotic toy based on acceleration sensor embedded within the robotic toy. Three toys were considered namely, a stuffed panda, a stuffed robot, and an excavator. Each toy was embedded with an accelerometer to record the interactions. Five different unwanted interactions were performed by adult participants and children. The considered interactions were hit, shake, throw, pickup, drop, and idle for the no interaction case. The collected data contains the magnitude of the resultant acceleration from the interactions. The data was processed by extracting the instances of interactions. A secondary dataset was created from the original one by creating artificial sequences. This data article contains the processed data that can be used to explore different machine learning models and techniques in classifying such interactions. Online repository contains the files: https://doi.org/10.7910/DVN/FHOO0Q.

Rupture of an Industrial GFRP Composite Mitered Elbow Pipe.

This paper examines the immature rupture of glass fiber reinforced plastic composite (GFRP) mitered elbow pipes. The GFRP composite mitered elbow pipe's lifespan was twenty-five years; however, the pipes in question experienced immature failures, resulting in the reduction of their lifetimes to seven, nine, and ten years, respectively. The GFRP cooling water mitered elbow pipe's service conditions operate at a pressure of up to 7 bar and temperatures between 15-36 °C. The root cause of failure was determined using visual inspection, analytical, microstructural, mechanical characterizations, and chemical analysis. The initial visualization inspection revealed an improper joint between the composite overwrapped and the straight pipe sections. Mechanical properties along the axial, hoop and 45° from the axial direction were obtained. The results from the analytical analysis indicated that the elbow might withstand the operating pressure depending on the quality factor, which was confirmed to be low due to the elbow joint's improper fabrication process. As evidence of this, the numerical analyses' results indicated that the safety factor in withstanding the operating pressure of 5 bar is dropped down in the radial region where the thickness is reduced to simulate the failure zone. This study's findings recommend that thickness of less than 15 mm be reinforced using overwrapped composites. It is recommended for future installations that the fabrication process be appropriately monitored and controlled and avoids using 45°/-45° fiber orientation and multiple layers of chopped strand mat glass fiber.

A Comparative Study between Polymer and Metal Additive Manufacturing Approaches in Investigating Stiffened Hexagonal Cells.

Recent polymer and metal additive manufacturing technologies were proven capable of building complex structures with high accuracy. Although their final products differ significantly in terms of mechanical properties and building cost, many structural optimization studies were performed with either one without systematic justification. Therefore, this study investigated whether the Direct Metal Laser Sintering (DMLS) and Fused Deposition Modelling (FDM) methodologies can provide similar conclusions when performing geometrical manipulations for optimizing structural crashworthiness. Two identical sets of four shapes of stiffened hexagonal cells were built and crushed under quasi-static loading. The results were compared in terms of collapsing behavior, load-carrying performance, and energy-absorption capability. Although the observed failure modes were different since the base-materials differ, similar improvement trends in performance were observed between both fabrication approaches. Therefore, FDM was recommended as a fabrication method to optimize thin-walled cellular hexagonal parameters since it was 80% more time-efficient and 53.6% cheaper than the DMLS technique.

Superior Non-Invasive Glucose Sensor Using Bimetallic CuNi Nanospecies Coated Mesoporous Carbon.

The assessment of blood glucose levels is necessary for the diagnosis and management of diabetes. The accurate quantification of serum or plasma glucose relies on enzymatic and nonenzymatic methods utilizing electrochemical biosensors. Current research efforts are focused on enhancing the non-invasive detection of glucose in sweat with accuracy, high sensitivity, and stability. In this work, nanostructured mesoporous carbon coupled with glucose oxidase (GOx) increased the direct electron transfer to the electrode surface. A mixed alloy of CuNi nanoparticle-coated mesoporous carbon (CuNi-MC) was synthesized using a hydrothermal process followed by annealing at 700 °C under the flow of argon gas. The prepared catalyst's crystal structure and morphology were explored using X-ray diffraction and high-resolution transmission electron microscopy. The electrocatalytic activity of the as-prepared catalyst was investigated using cyclic voltammetry (CV) and amperometry. The findings show an excellent response time of 4 s and linear range detection from 0.005 to 0.45 mM with a high electrode sensitivity of 11.7 ± 0.061 mA mM cm-2 in a selective medium.

Suitability of the Openly Accessible 3D Printed Prosthetic Hands for War-Wounded Children.

The field of rehabilitation and assistive devices is being disrupted by innovations in desktop 3D printers and open-source designs. For upper limb prosthetics, those technologies have demonstrated a strong potential to aid those with missing hands. However, there are basic interfacing issues that need to be addressed for long term usage. The functionality, durability, and the price need to be considered especially for those in difficult living conditions. We evaluated the most popular designs of body-powered, 3D printed prosthetic hands. We selected a representative sample and evaluated its suitability for its grasping postures, durability, and cost. The prosthetic hand can perform three grasping postures out of the 33 grasps that a human hand can do. This corresponds to grasping objects similar to a coin, a golf ball, and a credit card. Results showed that the material used in the hand and the cables can withstand a 22 N normal grasping force, which is acceptable based on standards for accessibility design. The cost model showed that a 3D printed hand could be produced for as low as $19. For the benefit of children with congenital missing limbs and for the war-wounded, the results can serve as a baseline study to advance the development of prosthetic hands that are functional yet low-cost.

Progress of Advanced Nanomaterials in the Non-Enzymatic Electrochemical Sensing of Glucose and H2O2.

Non-enzymatic sensing has been in the research limelight, and most sensors based on nanomaterials are designed to detect single analytes. The simultaneous detection of analytes that together exist in biological organisms necessitates the development of effective and efficient non-enzymatic electrodes in sensing. In this regard, the development of sensing elements for detecting glucose and hydrogen peroxide (H2O2) is significant. Non-enzymatic sensing is more economical and has a longer lifetime than enzymatic electrochemical sensing, but it has several drawbacks, such as high working potential, slow electrode kinetics, poisoning from intermediate species and weak sensing parameters. We comprehensively review the recent developments in non-enzymatic glucose and H2O2 (NEGH) sensing by focusing mainly on the sensing performance, electro catalytic mechanism, morphology and design of electrode materials. Various types of nanomaterials with metal/metal oxides and hybrid metallic nanocomposites are discussed. A comparison of glucose and H2O2 sensing parameters using the same electrode materials is outlined to predict the efficient sensing performance of advanced nanomaterials. Recent innovative approaches to improve the NEGH sensitivity, selectivity and stability in real-time applications are critically discussed, which have not been sufficiently addressed in the previous reviews. Finally, the challenges, future trends, and prospects associated with advanced nanomaterials for NEGH sensing are considered. We believe this article will help to understand the selection of advanced materials for dual/multi non-enzymatic sensing issues and will also be beneficial for researchers to make breakthrough progress in the area of non-enzymatic sensing of dual/multi biomolecules.