Can a Novel Light Weight Minimal Support Lifting Exoskeleton Modify Lifting Movement in People without Low Back Pain?

Low back pain (LBP) is a major contributor to lifting-related disabilities. To minimize the risk of back pain, emerging technologies known as lifting exoskeletons were designed to optimize lifting movements. However, it is currently unknown whether a minimally supportive exoskeleton can alter the lifting movement in people without LBP. This study aims to investigate if wearing a novel lightweight exoskeleton that minimally supports the back, hip, and knee can alter the lifting range of motion and movement variations in people without LBP. This study also aims to investigate if wearing this novel exoskeleton can result in a reliable between-day lifting movement. In two separate sessions (each one week apart), fourteen participants lifted a box (that weighed 10% of their body weight) ten times, once while wearing an exoskeleton and once while not wearing an exoskeleton. Wearing the novel exoskeleton during lifting produced moderate-high, test-retest reliability (Trunk: ICC3,1 = 0.89, 95% CI [0.67, 0.96], SEM = 9.34°; Hip: ICC3,1 = 0.63, 95% CI [0.22, 0.88], SEM = 2.57°; Knee: ICC3,1 = 0.61, 95% CI [0.23, 0.87], SEM = 2.50°). Wearing an exoskeleton significantly decreased the range of motion of the knee (F1,4 = 4.83, p = 0.031, ηp2 = 0.06). Additionally, wearing an exoskeleton significantly decreased hip (diff = 8.38, p = 0.045) and knee (diff = -8.57, p = 0.038) movement variability; however, wearing an exoskeleton did not decrease the movement variability of the body's trunk (diff = 0.60, p = 1.00). Therefore, minimally supported lifting through the use of exoskeletons can modify movement in people without LBP and produce reliable lifting movements. Wearing the novel exoskeleton is also desirable for monitoring lifting movements. Future studies should investigate the use of sensors and IMU to monitor lifting movement at work with the least amount of intrusion on an individual's movement.

Hybrid Feature Extractor Using Discrete Wavelet Transform and Histogram of Oriented Gradient on Convolutional-Neural-Network-Based Palm Vein Recognition.

Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER.

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain.

Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP.

Source-Space Brain Functional Connectivity Features in Electroencephalogram-Based Driver Fatigue Classification.

This study examined the brain source space functional connectivity from the electroencephalogram (EEG) activity of 48 participants during a driving simulation experiment where they drove until fatigue developed. Source-space functional connectivity (FC) analysis is a state-of-the-art method for understanding connections between brain regions that may indicate psychological differences. Multi-band FC in the brain source space was constructed using the phased lag index (PLI) method and used as features to train an SVM classification model to classify driver fatigue and alert conditions. With a subset of critical connections in the beta band, a classification accuracy of 93% was achieved. Additionally, the source-space FC feature extractor demonstrated superiority over other methods, such as PSD and sensor-space FC, in classifying fatigue. The results suggested that source-space FC is a discriminative biomarker for detecting driving fatigue.

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators.

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.

Image Recovery from Synthetic Noise Artifacts in CT Scans Using Modified U-Net.

Computed Tomography (CT) is commonly used for cancer screening as it utilizes low radiation for the scan. One problem with low-dose scans is the noise artifacts associated with low photon count that can lead to a reduced success rate of cancer detection during radiologist assessment. The noise had to be removed to restore detail clarity. We propose a noise removal method using a new model Convolutional Neural Network (CNN). Even though the network training time is long, the result is better than other CNN models in quality score and visual observation. The proposed CNN model uses a stacked modified U-Net with a specific number of feature maps per layer to improve the image quality, observable on an average PSNR quality score improvement out of 174 images. The next best model has 0.54 points lower in the average score. The score difference is less than 1 point, but the image result is closer to the full-dose scan image. We used separate testing data to clarify that the model can handle different noise densities. Besides comparing the CNN configuration, we discuss the denoising quality of CNN compared to classical denoising in which the noise characteristics affect quality.

Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain.

This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward's method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward's method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy.

Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.

BACKGROUND: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. METHODS: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. RESULTS: Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.

Electric powered wheelchair control using user-independent classification methods based on surface electromyography signals.

Wheelchairs are one of the most popular assistive technology (AT) among individuals with motor impairments due to their comfort and mobility. People with finger problems may find it difficult to operate wheelchairs using the conventional joystick control method. Therefore, in this research study, a hand gesture-based control method is developed for operating an electric-powered wheelchair (EPW). This study selected a comfort-based hand position to determine the stop maneuver. An additional exploration was undertaken to investigate four gesture recognition methods: linear regression (LR), regularized linear regression (RLR), decision tree (DT), and multi-class support vector machine (MC-SVM). The first two methods, LR and RLR, have promising accuracy values of 94.85% and 95.88%, respectively, but each new user must be trained. To overcome this limitation, this study explored two user-independent classification methods: MC-SVM and DT. These methods effectively addressed the finger dependency issue and demonstrated remarkable success in recognizing gestures across different users. MC-SVM has about 99.05% of both precision and accuracy, and the DT has about 97.77% accuracy and precision. All six participants were successful in controlling the EPW without any collisions. According to the experimental results, the proposed approach has high accuracy and can address finger dependency issues.

Combining Multistaged Filters and Modified Segmentation Network for Improving Lung Nodules Classification.

Advancements in computational technology have led to a shift towards automated detection processes in lung cancer screening, particularly through nodule segmentation techniques. These techniques employ thresholding to distinguish between soft and firm tissues, including cancerous nodules. The challenge of accurately detecting nodules close to critical lung structures such as blood vessels, bronchi, and the pleura highlights the necessity for more sophisticated methods to enhance diagnostic accuracy. This paper proposed combined processing filters for data preparation before using one of the modified Convolutional Neural Networks (CNN) as the classifier. With refined filters, the nodule targets are solid, semi-solid, and ground glass, ranging from low-stage cancer (cancer screening data) to high-stage cancer. Furthermore, two additional works were added to address juxta-pleural nodules while the pre-processing end and classification are done in a 3-dimensional domain in opposition to the usual image classification. The accuracy output indicates that even using a simple Segmentation Network if modified correctly, can improve the classification result compared to the other eight models. The proposed sequence total accuracy reached 99.7%, with 99.71% cancer class accuracy and 99.82% non-cancer accuracy, much higher than any previous research, which can improve the detection efforts of the radiologist.

Estimation of Lumbar Spine Loading of Low Back Pain Participant During Lifting Using an Open Source Musculoskeletal Model.

Biomechanical modeling of spinal load during lifting in OpenSim has the potential for rehabilitation and clinical assessment. In the literature, several spinal models have been developed and validated with movement data from healthy individuals. Although these models are valid for predicting spinal load in healthy individuals, it is unknown whether these models are applicable for people with chronic low back pain (CLBP). This study aims to compare the application of the lifting full body (LFB) model between a healthy participant and a participant with CLBP. The participants performed the lifting activity, and the motion capture data was used to analyze how an open-source model predicts the loading of the lumbar spine. Peak spinal loading at L5/S1 joint was estimated as 3.9 kN for the healthy participant and 3.1 kN for the CLBP participant. The results suggest that a longer duration of lift and lower lumbar range of motion reduces lumbar spinal loading.

Implementing Natural Image Quality Evaluator for Performance Indicator on Noise Artefacts Recovery in CT Scan.

The two most common evaluators for CT scan denoising are Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). This paper offers an alternative evaluator by utilizing of Natural Image Quality Evaluator (NIQE) assessment to determine the performance of denoising work on noise artefact. The noise artefact was obtained during the cancer screening process and had a particular noise density pattern across the image. NIQE is one of the blind image assessments which rely on the measurable deviation of image patch as a reference; it can determine the improved quality of denoising image. Due to the method of comparison in NIQE, the two parameters: patch size and sharpness threshold, will play an essential part in getting the score compared with the result from the other evaluators (PSNR and SSIM).

Microwave Imaging Based on a Subspace-based Two-step Iterative Shrinkage/Thresholding Method.

This paper presents a subspace-based two-step iterative shrinkage/thresholding method(S-TwIST) based on the Distorted Born iterative method (DBIM) to improve the performance of the original TwIST inverse algorithm. This method retrieves the deterministic part of the induced current from inhomogeneous Green's function operator leading to more accurate total field calculation at each iteration step than that of the original TwIST. Both inverse algorithms have been evaluated with a set of synthetic geometries with fine structures. Compared with TwIST, the results show that S-TwIST has superior accuracy in multiple objects profile (εerr=0.1454%) and 1/16λ resolution at 2GHz. Also, S-TwIST is more robust to initial guess, which means it is less likely to become unstable when the inversion procedure starts without initial guess.

Machine Learning Derived Lifting Technique in People without Low Back Pain.

This paper presents a method for determining the number of lifting techniques used by healthy individuals through the analysis of kinematic data collected from 115 participants utilizing an motion capture system. The technique utilizes a combination of feature extraction and Ward's method to analyse the range of motion in the sagittal plane of the knee, hip, and trunk. The findings identified five unique lifting techniques in people without low back pain. The multivariate analysis of variance statistical analysis reveals a significant difference in the range of motion in the trunk, hip and knee between each cluster for healthy people (F (12, 646) = 125.720, p < 0.0001).Clinical Relevance- This information can assist healthcare professionals in choosing effective treatments and interventions for those with occupational lower back pain by focusing rehabilitation on specific body parts associated with problematic lifting techniques, such as the trunk, hip, or knee, which may lead to improved pain and disability outcomes, exemplifying precision medicine.

Electrodermal Activity for Measuring Cognitive and Emotional Stress Level.

Stress can lead to harmful conditions in the body, such as anxiety disorders and depression. One of the promising noninvasive methods, which has been widely used in detecting stress and emotion, is electrodermal activity (EDA). EDA has a tonic and phasic component called skin conductance level and skin conductance response (SCR). However, the components of the EDA cannot be directly extracted and need to be deconvolved to obtain it. The EDA signals were collected from 18 healthy subjects that underwent three sessions - Stroop test with increasing stress levels. The EDA signals were then deconvoluted by using continuous deconvolution analysis (CDA) and convex optimization approach to electrodermal activity (cvxEDA). Four features from the result of the deconvolution process were collected, namely sample average, standard deviation, first absolute difference, and normalized first absolute difference. Those features were used as the input of the classification process using the extreme learning machine (ELM). The output of classification was the stress level; mild, moderate, and severe. The visual of the phasic component using cvxEDA is more precise or smoother than the CDA's result. However, both methods could separate SCR from the original skin conductivity raw and indicate the small peaks from the SCR. The classification process results showed that both CDA and cvxEDA methods with 50 hidden layers in ELM had a high accuracy in classifying the stress level, which was 95.56% and 94.45%, respectively. This study developed a stress level classification method using ELM and the statistical features of SCR. The result showed that EDA could classify the stress level with over 94% accuracy. This system could help people monitor their mental health during overworking, leading to anxiety and depression because of untreated stress.

The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients.

The potential of using an electroencephalogram (EEG) to detect hypoglycemia in patients with type 1 diabetes (T1D) has been investigated in both time and frequency domains. Under hyperinsulinemic hypoglycemic clamp conditions, we have shown that the brain's response to hypoglycemic episodes could be described by the centroid frequency and spectral gyration radius evaluated from spectral moments of EEG signals. The aim of this paper is to investigate the effect of hypoglycemia on spectral moments in EEG epochs of different durations and to propose the optimal time window for hypoglycemia detection without using clamp protocols. The incidence of hypoglycemic episodes at night time in five T1D adolescents was analyzed from selected data of ten days of observations in this study. We found that hypoglycemia is associated with significant changes (P < 0.05) in spectral moments of EEG segments in different lengths. Specifically, the changes were more pronounced on the occipital lobe. We used effect size as a measure to determine the best EEG epoch duration for the detection of hypoglycemic episodes. Using Bayesian neural networks, this study showed that 30 second segments provide the best detection rate of hypoglycemia. In addition, Clarke's error grid analysis confirms the correlation between hypoglycemia and EEG spectral moments of this optimal time window, with 86% of clinically acceptable estimated blood glucose values. These results confirm the potential of using EEG spectral moments to detect the occurrence of hypoglycemia.

Infrared Thermal Imaging for Evaluation of Clubfoot After the Ponseti Casting Method-An Exploratory Study.

Background: Conservative treatment, Ponseti method, has been considered as a standard method to correct the clubfoot deformity among Orthopedic society. Although the result of conservative methods have been reported with higher success rates than surgical methods, many more problems have been reported due to improper casting, casting pressure or bracing discomfort. Nowadays, infrared thermography (IRT) is widely used as a diagnostic tool to assess musculoskeletal disorders or injuries by detecting temperature abnormalities. Similarly, the foot skin temperature evaluation can be added along with the current subjective evaluation to predict if there is any casting pressure, excessive manipulation, or overcorrections of the foot, and other bracing pressure-related complications. Purpose: The main purpose of this study was to explore the foot skin temperature changes before and after using of manipulation and weekly castings. Methods: This is an explorative study design. Infrared Thermography (IRT), E33 FLIR thermal imaging camera model, was used to collect the thermal images of the clubfoot before and after casting intervention. A total of 120 thermal images (Medial region of the foot-24, Lateral side of the foot-24, Dorsal side of the foot-24, Plantar side of the foot-24, and Heel area of the foot-24) were collected from the selected regions of the clubfoot. Results: The results of univariate statistical analysis showed that significant temperature changes in some regions of the foot after casting, especially, at the 2nd (M = 32.05°C, SD = 0.77, p = 0.05), 3rd (M = 31.61, SD = 1.11; 95% CI: 31.27-31.96; p = 0.00), and 6th week of evaluation on the lateral side of the foot (M = 31.15°C, SD = 1.59; 95% CI: 30.75-31.54, p = 0.000). There was no significant temperature changes throughout the weekly casting in the medial side of the foot. In the heel side of the foot, significant temperature changes were noticed after the third and fourth weeks of casting. Conclusion: This study found that a decreased foot skin temperature on the dorsal and lateral side of the foot at the 6th week of thermography evaluation. The finding of this study suggest that the infrared thermography (IRT) might be useful as an adjunct assessment tool to evaluate the thermophysiological changes, which can be used to predict the complications caused by improper casting, over manipulative or stretching and casting-pressure related complications.

The influence of mental fatigue on brain activity: Evidence from a systematic review with meta-analyses.

The occurrence of mental fatigue during tasks like driving a vehicle increases risk of injury or death. Changes in electroencephalographic (EEG) activity associated with mental fatigue has been frequently studied and considered a promising biomarker of mental fatigue. This is despite differences in methodologies and outcomes in prior research. A systematic review with meta-analyses was conducted to establish the influence of mental fatigue on EEG activity spectral bands, and to determine in which regions fatigue-related EEG spectral changes are likely to occur. A high-yield search strategy identified 21 studies meeting inclusion criteria for investigating the change in EEG spectral activity in non-diseased adults engaged in mentally fatiguing tasks. A medium effect size (using Cohen's g) of 0.68 (95%CI: 0.24-1.13) was found for increase in overall EEG activity following mental fatigue. Further examination of individual EEG spectral bands and regions using network meta-analyses indicated large increases in theta (g = 1.03; 95%CI: 0.79-1.60) and alpha bands (g = 0.85; 95%CI: 0.47-1.43), with small to moderate changes found in delta and beta bands. Central regions of the scalp showed largest change (g = 0.80; 95%CI: 0.46-1.21). Sub-group analyses indicated large increases in theta activity in frontal, central and posterior sites (all g > 1), with moderate changes in alpha activity in central and posterior sites. Findings have implications for fatigue monitoring and countermeasures with support for change in theta activity in frontal, central and posterior sites as a robust biomarker of mental fatigue and change in alpha wave activity considered a second line biomarker to account for individual variability.

Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia.

Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. For people with diabetes, the mismatch between the insulin therapy and the body's physiology could increase the risk of hypoglycemia. Nocturnal hypoglycemia is particularly dangerous for type-1 diabetes patients because its symptoms may obscure during sleep. The early onset detection of hypoglycemia at night time is necessary because it can result in unconsciousness and even death. This paper presents new electroencephalogram spectral features for nocturnal hypoglycemia detection. The system uses high-order spectral moments for feature extraction and Bayesian neural network for classification. From a clinical study of hypoglycemia of eight patients with type-1 diabetes at night, we find that these spectral moments of theta band and alpha band changed significantly. During hypoglycemia episodes, the theta moments increased significantly (P < 0.001) while the features of alpha band reduced significantly (P < 0.001). Using the optimal Bayesian neural network, the classification results were 85% and 52% in sensitivity and specificity, respectively. The significant correlation (P < 0.001) with real blood glucose profiles shows the effectiveness of the proposed features for the detection of nocturnal hypoglycemia.

Electroencephalogram Reactivity to Hyperglycemia in Patients with Type 1 Diabetes.

This paper is concerned with a study of hyperglycemia on four patients with type 1 diabetes at night time. We investigated the association between hyperglycemic episodes and electroencephalogram (EEG) signals using data from the central and occipital areas. The power spectral density of the brain waves was estimated to compare the difference between hyperglycemia and euglycemia using the hyperglycemic threshold of 8.3 mmol/L. The statistical results showed that alpha and beta bands were more sensitive to hyperglycemic episodes than delta and theta bands. During hyperglycemia, whereas the alpha power increased significantly in the occipital lobe (P<0.005), the power of the beta band increased significantly in all observed channels (P<0.01). Using the Pearson correlation, we assessed the relationship between EEG signals and glycemic episodes. The estimated EEG power levels of the alpha band and the beta band produced a significant correlation against blood glucose levels (P<0.005). These preliminary results show the potential of using EEG signals as a biomarker to detect hyperglycemia.