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Prosthetic devices are vital for enhancing personal autonomy and the quality of life for amputees. However, the rejection rate for electric upper-limb prostheses remains high at around 30%, often due to issues like functionality, control, reliability, and cost. Thus, developing reliable, robust, and cost-effective human-machine interfaces is crucial for user acceptance. Machine learning algorithms using Surface Electromyography (sEMG) signal classification hold promise for natural prosthetic control. This study aims to enhance hand and wrist movement classification using sEMG signals, treated as time series data. A novel approach is employed, combining a variation of the Random Convolutional Kernel Transform (ROCKET) for feature extraction with a cross-validation ridge classifier. Traditionally, achieving high accuracy in time series classification required complex, computationally intensive methods. However, recent advances show that simple linear classifiers combined with ROCKET can achieve state-of-the-art accuracy with reduced computational complexity. The algorithm was tested on the UCI sEMG hand movement dataset, as well as on the Ninapro DB5 and DB7 datasets. We demonstrate how the proposed approach delivers high discrimination accuracy with minimal parameter tuning requirements, offering a promising solution to improve prosthetic control and user satisfaction.

Rapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on advanced image processing algorithms. We captured thermal images of the backs of individuals with and without COVID-19 using a portable thermal camera that connects directly to smartphones. Our novel image processing algorithms automatically extracted multiple texture and shape features of the thermal images and achieved an area under the curve (AUC) of 0.85 in COVID-19 detection with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. In summary, we show, for the first time, that a hand-held thermal imaging device can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings, especially in low-income regions with limited imaging resources.

Step length is an important diagnostic and prognostic measure of health and disease. Wearable devices can estimate step length continuously (e.g., in clinic or real-world settings), however, the accuracy of current estimation methods is not yet optimal. We developed machine-learning models to estimate step length based on data derived from a single lower-back inertial measurement unit worn by 472 young and older adults with different neurological conditions, including Parkinson’s disease and healthy controls. Studying more than 80,000 steps, the best model showed high accuracy for a single step (root mean square error, RMSE = 6.08 cm, ICC(2,1) = 0.89) and higher accuracy when averaged over ten consecutive steps (RMSE = 4.79 cm, ICC(2,1) = 0.93), successfully reaching the predefined goal of an RMSE below 5 cm (often considered the minimal-clinically-important-difference). Combining machine-learning with a single, wearable sensor generates accurate step length measures, even in patients with neurologic disease. Additional research may be needed to further reduce the errors in certain conditions.

Introduction Pediatric respiratory monitoring, crucial for assessing children's health, particularly those with respiratory diseases, often relies on invasive or cumbersome methods. Here, we propose a non-invasive approach using a video depth camera to measure breathing parameters in children, offering innovation and promise. Aims We aim to introduce and validate a straightforward remote procedure for measuring crucial breathing parameters in children. These include respiratory rate (RR), volumetric changes during inhalation and exhalation, and the phase angle (PA) between chest and abdomen expansions. Methods The proposed method involves detecting three feature points - nipples and navel - using a video depth camera. A 30- to 60-second video is recorded to track chest and abdomen movements. Analysis of feature point locations, distances between them, and signal frequencies is conducted to estimate respiratory parameters. To validate the accuracy of our method, we employed mechanical lung simulators within dolls for procedure testing and measurement accuracy verification. Additionally, recordings of pediatric patients, both healthy and with respiratory diseases, were analyzed to correlate computational parameter estimations with physician assessments, ensuring the reliability and effectiveness of our approach. Results Our results show a strong correlation between simulator inputs and algorithm estimations, validating our method's accuracy. Additionally, applying the procedure to pediatric patient recordings significantly correlates with physician assessments, notably, marking the first remote measurement of the respiratory PA. Conclusions This remote procedure presents a promising alternative for pediatric respiratory monitoring, offering accurate measurements without invasive techniques or extensive equipment. The robust correlation between computational estimations and physician assessments underscores its reliability, suggesting potential for broader clinical applications and advancements in pediatric respiratory care.

Objective The goal of this study was to characterize the changes in the palm’s blood distribution in response to a decrease in blood pressure due to gravity-induced changes, using thermal imaging. Methods Thermal hands images were taken from ten healthy volunteers, without any known vascular pathologies, in three different stages: baseline, gravitation and recovery. In the baseline stage the hand was set on a table, at heart height. During the gravitation stage one hand was placed 40 cm above the table for 10 minutes, while the second hand was stayed on the table. The recovery stage, in which both hands were placed back on the table, was recorded for 10 minutes. Thermal images of both hands were taken every ten seconds throughout the experiment. Results Mean skin temperatures were increased during hand elevating in both the palm center and the distal phalanx of the middle finger by 2.57°C and 3.33°C, respectively. This increase was significant and remained high during the recovery period ( p < 0.01). A similar effect was also observed with the other hand, which remained on the table. Conclusions The temperature increase of the palm during gravity conditions reflects blood perfusion compensation due to high local oxygen consumption during decrease in local blood pressure. The bilateral effect indicates the central nervous system involvement. Thermal imaging allows characterization of the palm’s blood distribution under gravitational conditions. Since this technique is noncontact and safe, it could be useful for assessment of blood supply during physical effort.

Modeling and analyzing high-dimensional data has become a common task in various fields and applications. Often, it is of interest to learn a function that is defined on the data and then to extend its values to newly arrived data points. The Laplacian pyramids approach invokes kernels of decreasing widths to learns a given dataset and a function defined over it in a multi-scale manner. Extension of the function to new values may then be easily performed. In this work, we extend the Laplacian pyramids technique to model the data by considering two-directional connections. In practice, kernels of decreasing widths are constructed on the row-space and on the column space of the given dataset and in each step of the algorithm the data is approximated by considering the connections in both directions. Moreover, the method does not require solving a minimization problem as other common imputation techniques do, thus avoids the risk of a non-converging process. The method presented in this paper is general and may be adapted to imputation tasks. The numerical results demonstrate the ability of the algorithm to deal with a large number of missing data values. In addition, in most cases, the proposed method generates lower errors compared to existing imputation methods applied to benchmark dataset.

Background and Objective Infrared Thermal Imaging (ITI) is a noninvasive method to measure skin temperature (ST). The latter is determined by the microcirculatory blood flow and ambient temperature. Photobiostimulation has been shown to increase blood flow. The objective was to characterize the spatial and temporal changes in ST, in response to photobiostimulation using ITI. Methods A randomized-controlled clinical study with 20 healthy subjects (30 ± 8 years old, 10:10 male:female). Subjects were irradiated with either red (630 nm, 55 mW/cm 2 ) or near-infrared (830 nm, 70 mW/cm 2 ) light-emitting diodes for 5 minutes [min] over the wrist area. Thermal images of the hands were captured every minute before, during, and 20 min after irradiation. The ST change from baseline (ΔST) of each of five anatomical regions (wrist, palm center, arch [surrounding vascular arches], proximal phalanx, distal phalanx) was measured. Subjects who responded to photobiostimulation (ΔST ≥ 0.5°C) were included in this analysis. Mean measurement changes were modeled by ΔST = A * (1−exp[−time/tau]), to which non-linear regression was applied with the adjustable parameters of amplitude(A) and characteristic rise time (tau). Results Photobiostimulation caused ST increase that initiated during irradiation, reached a plateau during follow-up, and fitted the model by R > 0.9 for all regions. Following the anatomical path of the blood supply, from the wrist to the distal phalanx, the ΔST amplitude(A) increased while tau decreased: Mean ± SE for wrist ⇒ center ⇒ arch ⇒ proximal phalanx ⇒ distal phalanx: A = 1.03 ± 0.08 ⇒ 1.7 ± 0.03 ⇒ 2.1 ± 0.03 ⇒ 3.0 ± 0.05 ⇒ 3.4 ± 0.05, and tau = 7.9 ± 1.7 ⇒ 6.2 ± 0.4 ⇒ 5.7 ± 0.3 ⇒ 5.6 ± 0.3 ⇒ 4.3 ± 0.3, respectively. Conclusion Using ITI, a thermo-anatomical association in response to photobiostimulation was established. Thermal imaging may be used for quantitative characterization of blood re-distribution in response to vasoactive interventions with potential for vascular diagnostics.

Automatic speech recognition is the main form of man–machine communication. Recently, several studies have shown the ability to automatically recognize speech based on electromyography (EMG) signals of the facial muscles using machine learning methods. The objective of this study was to utilize machine learning methods for automatic identification of speech based on EMG signals. EMG signals from three facial muscles were measured from four healthy female subjects while pronouncing seven different words 50 times. Short time Fourier transform features were extracted from the EMG data. Principle component analysis (PCA) and locally linear embedding (LLE) methods were applied and compared for reducing the dimensions of the EMG data. K-nearest-neighbors was used to examine the ability to identify different word sets of a subject based on his own dataset, and to identify words of one subject based on another subject's dataset, utilizing an affine transformation for aligning between the reduced feature spaces of two subjects. The PCA and LLE achieved average recognizing rate of 81% for five words-sets in the single-subject approach. The best average recognition success rates for three and five words-sets were 88.8% and 74.6%, respectively, for the multi-subject classification approach. Both the PCA and LLE achieved satisfactory classification rates for both the single-subject and multi-subject approaches. The multi-subject classification approach enables robust classification of words recorded from a new subject based on another subject’s dataset and thus can be applicable for people who have lost their ability to speak.

The distal ischemic steal syndrome (ISS) is a complication following the construction of an arteriovenous (A-V) access for hemodialysis. The ability to non-invasively monitor changes in skin microcirculation improves both the diagnosis and treatment of vascular diseases. In this study, we propose a novel technique for evaluating the palms' blood distribution following arteriovenous access, based on thermal imaging. Furthermore, we utilize the thermal images to identify typical recovery patterns of patients that underwent this surgery and show that thermal images taken post-surgery reflect the patient's follow-up status. Thermal photographs were taken by a portable thermal camera from both hands before and after the A-V access surgery, and one month following the surgery, from ten dialysis patients. A novel term “Thermo-Anatomical Segmentation”, which enables a functional assessment of palm blood distribution was defined. Based on this segmentation it was shown that the greatest change after surgery was in the most distal region, the fingertips (p < 0.05). In addition, the changes in palm blood distribution in both hands were synchronized, which indicates a bilateral effect. An unsupervised machine learning model revealed two variables that determine the recovery pattern following the surgery: the palms' temperature difference pre- and post-surgery and the post-surgery difference between the treated and untreated hand. Our proposed framework provides a new technique for quantitative assessment of the palm's blood distribution. This technique may improve the clinical treatment of patients with vascular disease, particularly the patient-specific follow-up, in clinics as well as in homecare.

Non-alcoholic fatty liver disease (NAFLD) comprises a spectrum of progressive liver pathologies, ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis and cirrhosis. A liver biopsy is currently required to stratify high-risk patients, and predicting the degree of liver inflammation and fibrosis using non-invasive tests remains challenging. Here, we sought to develop a novel, cost-effective screening tool for NAFLD based on thermal imaging. We used a commercially available and non-invasive thermal camera and developed a new image processing algorithm to automatically predict disease status in a small animal model of fatty liver disease. To induce liver steatosis and inflammation, we fed C57/black female mice (8 weeks old) a methionine-choline deficient diet (MCD diet) for 6 weeks. We evaluated structural and functional liver changes by serial ultrasound studies, histopathological analysis, blood tests for liver enzymes and lipids, and measured liver inflammatory cell infiltration by flow cytometry. We developed an image processing algorithm that measures relative spatial thermal variation across the skin covering the liver. Thermal parameters including temperature variance, homogeneity levels and other textural features were fed as input to a t-SNE dimensionality reduction algorithm followed by k-means clustering. During weeks 3,4, and 5 of the experiment, our algorithm demonstrated a 100% detection rate and classified all mice correctly according to their disease status. Direct thermal imaging of the liver confirmed the presence of changes in surface thermography in diseased livers. We conclude that non-invasive thermal imaging combined with advanced image processing and machine learning-based analysis successfully correlates surface thermography with liver steatosis and inflammation in mice. Future development of this screening tool may improve our ability to study, diagnose and treat liver disease.