Miracle-dz/UniFAR-FTUs huggingface dataset:
Circadian parameter as a possible indicator of gait performance and daily activity levels in chronic stroke survivors

Stroke recovery is a critical public health problem. It involves cognitive and physical impairments leading to physical inactivity. In addition, there are also sleep problems and disruption of the circadian rhythm. In this paper, we analyze the relation between cognitive impairments level, walking performance, sleep and circadian parameters of a group of chronic stroke survivors. Five stroke survivors enrolled in the study. The clinical assessments were: cognitive function (MoCA), the 10 meter-walking test (10MWT), and the 6 minutes walking test (6MWT). The circadian rhythm parameters were obtained for at least one week wearing the actimeters. There was a positive correlation between the duration of the main sleep episode and the MoCA scores. There were significant correlations between the 10MWT and 6MWT and the circadian parameters that were positive for most active period (M10), Interdaily stability (IS), Mesor and Amplitude and negative for intradaily variability (IV). Our results indicate that gait speed and resistance are correlated to circadian rhythm synchronization, lower rhythm variability and more defined activity-rest episodes. This preliminary study underscores the importance of including a continuous measure of sleep and activity cycles in the assessment of stroke survivors.
Integration of artificial vision with non-visual peripheral cues to guide mobility

Visual prostheses can improve vision for people with severe vision loss, but low image resolution and lack of peripheral vision limit their effectiveness. To address both problems, we developed a prototype advanced video processing system with a headworn depth camera and feature detection capabilities. We used computer vision algorithms to detect landmarks representing a goal and plan a path towards the goal, while removing unnecessary distractors from the video. If the landmark fell outside the visual prosthesis's field-of-view (20 degrees central vision) but within the camera's field-of-view (70 degrees), we provided vibrational cues to the left or right temple to guide the user in pointing the camera. We evaluated an Argus II retinal prosthesis participant with significant vision loss who could not complete the task (finding a door in a large room) with either his remaining vision or his retinal prosthesis. His success rate improved to 57%, 37.5%, and 100% while requiring 52.3, 83.0, and 58.8 seconds to reach the door using only vibration feedback, retinal prosthesis with modified video, and retinal prosthesis with modified video and vibration feedback, respectively. This case study demonstrates a possible means of augmenting artificial vision. Clinical Relevance— Retinal prostheses can be enhanced by adding computer vision and non-visual cues.
Strain evolution of flip-chip package with underfill during thermal cycling and high temperature isothermal aging

Underfill (UF) is an important material in advanced packaging and widely used for flip chip interconnection to improve reliability of the encapsulation. In flip-chip package, how UF interact with the encapsulation is of great importance in underfill selection and reliability assessment. In this study, the deformation behavior and strains of a cross-section of a flip-chip package with UF subject to thermal cycling (-55~125 °C, 15 cycles) and high temperature isothermal ageing (175°C, 8 hours) have been characterized by 2D Digital Image Correlation (DIC) method combined with a novel in situ characterization system. Results clearly captured the chip-package interaction (CPI) and strain accumulation behavior of the encapsulation during thermal cycling, and distinguished high temperature isothermal ageing behavior of the encapsulation, which may result from different physical properties and post curing behavior of UFs, have also been observed. Our results would deepen the understanding of how underfill contribute to the CPI in a practical encapsulation when thermal loading and be helpful in underfill design and selection and reliability assessment of flip chip packaging.
Characterization of Dry-Contact EEG Electrodes and an Empirical Comparison of Ag/AgCl and IrO2 Electrodes

Dry-contact electrodes are increasingly being used for EEG recordings in both research studies and consumer products. They are more user-friendly and better suited for long-term recordings. However, dry-contact electrodes also bring challenges with respect to the stability and impedance of the electrode-skin interface. We propose a methodology to characterize and compare dry-contact electrodes. The characterization is based on measuring the electrode-skin impedance spectrum, fit a parametric model of the electrode-skin interface to the measured spectrum, and calculate the resulting thermal noise spectrum. Thereby it is possible to relate the noise of an EEG recording to the theoretical noise contribution from the electrode-skin interface. To demonstrate the methodology, we performed an empirical study comparing two types of dry-contact electrodes in an ear-EEG setup. The electrodes were IrO2, previously used for ear-EEG, and a new design based on Ag/AgCl. Here, we related the noise floor of an auditory steady-state response (ASSR) to the thermal noise spectrum of the electrode-skin interface. The study showed similar impedance and EEG recording quality for the two electrode types, and the thermal noise of the electrode-skin interface was below the noise floor of the EEG recordings for both electrode types. Dry-contact EEG is an enabling technology for long-term brain monitoring of patients. This may be relevant for example for monitoring of neurodegenerative diseases, stroke patients, patients with traumatic brain injuries, and psychiatric patients.
Dynamic Musculoskeletal Simulation of a Passive Exoskeleton for Simulating Contracture

Gait assessment scores are used for quantifying the abnormalities in the gait. Evaluation of the performance of these scores is a must for their clinical acceptance. However, current methods of assessing the performance of the gait assessment scores for clinically relevant gait abnormalities are prone to error. For example, values of intra-observer reliability, inter-observer reliability and sensitivity calculated for a gait assessment score change with the population of patients and observers. Therefore, there is a need for a methodology for replicating musculoskeletal deformations such as contracture in healthy individuals for objectively evaluating the performance of gait assessment scores with variable severity of musculoskeletal deformations. In this study, a series of dynamic musculoskeletal simulations are performed to simulate and verify a mathematical model of a passive exoskeleton for simulating contractures. The proposed model achieved a root mean square error of 1.864° and a correlation of coefficient of 0.984 while testing on five unique combinations of linear and non-linear torques and seven degrees of severity of hamstring contracture. To understand the tolerance of the proposed model to environmental noises, its performance is also tested at various perturbations. The results indicate that a passive exoskeleton attached to an unimpaired musculoskeletal model can accurately simulate the contracture of the targeted muscles. Clinical relevance - The proposed methodology has a utility in evaluating performances of gait assessment scores and understanding the effect of contractures on biomechanics of gait.
Using Operative Reports to Predict Heart Transplantation Survival*

Heart transplantation is a difficult procedure compared with other surgical operations, with a greater outcome uncertainty such as late rejection and death. We can model the success of heart transplants from predicting factors such as the age, sex, diagnosis, etc., of the donor and recipient. Although predictions can mitigate the uncertainty on the transplantation outcome, their accuracy is far from perfect. In this paper, we describe a new method to predict the outcome of a transplantation from textual operative reports instead of traditional tabular data. We carried out an experiment on 300 surgical reports to determine the survival rates at one year and five years. Using a truncated TF-IDF vectorization of the texts and logistic regression, we could reach a macro Fl of 59.1 %, respectively, 54.9% with a five-fold cross validation. While the size of the corpus is relatively small, our experiments show that the operative textual sources can discriminate the transplantation outcomes and could be a valuable additional input to existing prediction systems. Clinical Relevance- Heart transplantation involves a significant number of written reports including in the preoperative examinations and operative documentation. In this paper, we show that these written reports can predict the outcome of the transplantation at one and five years with macro 1s of 59.1 % and 54.9 %, respectively and complement existing prediction methods.
Orthotic Prescription for Pediatric Flexible Flat Feet using Convolutional Neural Networks

Pediatric flexible flat foot (PFFF) is known to in-crease the foot structure's load, causing potential disability. Foot orthoses are one of the most common non-surgical methods to improve the medial longitudinal arch of the foot for improving PFFF. However, orthoses are not routinely prescribed due to their high cost, and discomfort caused by a restriction of foot movement. Furthermore, there are no quantitative standards or guidelines for an orthotic prescription, which makes the decision-making process of less experienced podiatrists chal-lenging. In this study, the authors investigated convolutional neural networks to classify the needs of orthotic prescription. Using image augmentation techniques and training a VGG-16 model, we achieved high precision and recall, 1 and 0.969 accordingly, to classify orthotic prescription needs.
Multilayer Network Framework Reveals Cross-Frequency Coupling Hubs in Cortical Olfactory Perception

Olfactory perception is shaped by dynamic in-teractions among networks of widely distributed brain regions involved in several neurocognitive processes. However, the neural mechanisms that enable effective coordination and integrative processing across these brain regions, which have different functions and operating characteristics, are not yet fully understood. In this study we use electroencephalography (EEG) signals and a multilayer network formalism to model cross-frequency coupling across the brain and identify brain regions that operate as connecting hubs, thus facilitating inte-grative function. To this goal, we investigate $\alpha-\gamma$ coupling and $\theta-\gamma$ coupling during exposure to olfactory stimuli of different pleasantness levels. We found that a wider distributed network of hubs emerges in the higher pleasantness condition and that significant differences in the hub connectivity are located in the middle frontal and central regions. Our results indicate the consistent functional role that $\gamma$ band activity plays in information integration in olfactory perception.
Smart Wearable Device for Quantification of Risk of Fall: Exploring Role of Gait Phases and Knee Bending Angle for Parkinson's Patients

Gait disturbances with falls are common among patients with Parkinson's disease. Falls commonly occur from slips while walking on pathways with turns. Gait phases namely Loading Response and Terminal Stance are linked with forward and backward slips. Also, postural deformities (connected with knee joint angles) are debilitating symptoms of Parkinson's patients and are related with falls. Here, we have focused on exploring the contribution of Loading Response and Terminal Stance to risk of fall along with the relevance of postural deformity (e.g., knee bending) while an individual walked overground on pathways (with 0° and 180° turn) under dual task condition. For this, we have used a wearable device consisting of a pair of Sensored shoes and Knee Bending Angle Recorder Units. The device was used to compute Coefficient of Variation of knee bending angle during different gait phases as an indicator of one's risk of fall that corroborated with clinical measure. Clinical Relevance- A study with age and gender matched healthy and Parkinson's individuals indicated the importance of Loading Response and pathway turn while assessing risk of fall. This can serve as important pre-clinical input while designing intervention paradigms.
Signal Quality Assessment of Photoplethysmogram Signals using Quantum Pattern Recognition Technique and lightweight CNN Module

Photoplethysmography (PPG) signal comprises physiological information related to cardiorespiratory health. High-quality PPG signals are necessary to extract cardiores-piratory information accurately. Motion artifacts can easily corrupt PPG signals due to human locomotion, leading to noise enriched, poor quality signals. Several rule-based and Machine-Learning (ML) - based approaches for PPG signal quality estimation are available, but those algorithms' efficacy is questionable. So, the authors propose a lightweight CNN architecture for signal quality assessment by employing a novel Quantum Pattern Recognition (QPR) technique. The proposed algorithm is validated on manually annotated data obtained from the University of Queensland database. A total of 28366, 5s signal segments are preprocessed and transformed into image files of 20 x 500 pixels for input to the 2D CNN architecture. The developed model classifies the PPG signal as ‘good’ and ‘bad’ with an accuracy of 98.3% with 99.3% sensitivity, 94.5% specificity and 98.9% F1-score. The experimental analysis concludes that slim module based architecture and novel Spatio-temporal pattern recognition technique improve the system's performance. The proposed approach is suitable for a resource-constrained wearable implementation.
Convolutional Neural Networks for Apnea Detection from Smartphone Audio Signals: Effect of Window Size

Although sleep apnea is one of the most prevalent sleep disorders, most patients remain undiagnosed and untreated. The gold standard for sleep apnea diagnosis, polysomnography, has important limitations such as its high cost and complexity. This leads to a growing need for novel cost-effective systems. Mobile health tools and deep learning algorithms are nowadays being proposed as innovative solutions for automatic apnea detection. In this work, a convolutional neural network (CNN) is trained for the identification of apnea events from the spectrograms of audio signals recorded with a smartphone. A systematic comparison of the effect of different window sizes on the model performance is provided. According to the results, the best models are obtained with 60 s windows $\textrm{(sensitivity-0.72, specilicity-0.89, AUROC} =0.88)$, For smaller windows, the model performance can be negatively impacted, because the windows become shorter than most apnea events, by which sound reductions can no longer be appreciated. On the other hand, longer windows tend to include multiple or mixed events, that will confound the model. This careful trade-off demonstrates the importance of selecting a proper window size to obtain models with adequate predictive power. This paper shows that CNNs applied to smartphone audio signals can facilitate sleep apnea detection in a realistic setting and is a first step towards an automated method to assist sleep technicians. Clinical Relevance— The results show the effect of the window size on the predictive power of CNNs for apnea detection. Furthermore, the potential of smartphones, audio signals, and deep neural networks for automatic sleep apnea screening is demonstrated.
Gene Expression Markers of Prognostic Importance for Prostate Cancer Risk in Patients with Benign Prostate Hyperplasia

Comparative analyses utilizing publicly available big data have the potential to generate novel hypotheses and knowledge. However, this approach is underutilized in the realm of cancer research, particularly for prostate cancer. While the general progression of prostate cancer is now well understood, how individual cell types transition from healthy, to pre-cancerous, to cancerous cell types, remains to be further elucidated. To address this, we re-analyzed two publicly available single-cell RNA-seq datasets of prostate cancer and benign prostate hyperplasia cell types. The differential expression analysis of 15,505 epithelial cell profiles across 18,638 genes revealed 791 genes that were up regulated in prostate cancer epithelial cells. Here we report six markers that show significant upregulation in prostate cancer cells relative to BPH epithelial cells: HPN (5.62X), RAC3 (3.51X), CD24 (2.18X), HOXC6 (1.77X), AGR2 (1.71X), and IGFBP2 (1.28X). In particular, the significant differential expression of AGR2 further supports its clinical relevance in supplementing prostate-specific antigen screening for detecting prostate cancer. These findings have the potential to further advance our knowledge of genes governing the development of cancer in prostate epithelial cells. Clinical Relevance– Our results establish the importance of 6 prostate cancer markers (HPN, RAC3, CD24, HOXC6, AGR2, and IGFBP3) in distinguishing between prostate cancer epithelial cells and benign prostate hyperplasia epithelial cells.
Carrier Frequency Offset Estimation and Compensation Technique for Massive MIMO-OFDM Wireless Communication System

Massive Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (Massive MIMO-OFDM) plays a crucial role in wireless communication networks. However, the massive MIMO-OFDM wireless communication system’s performance is affected by Carrier Frequency Offset (CFO), which breaks the orthogonality among the subcarriers. This paper proposes a pilot-based CFO estimation and compensation method for the massive MIMO-OFDM wireless communication system. In the proposed method, the CFO is estimated and compensated by the extracted pilot tones at the receiver. The proposed method is applied to the massive MIMO-OFDM wireless communication system which propagates through the Rayleigh channel with the various modulation schemes, different ranges of frequency offsets, and different numbers of receive antennas. The simulation results indicate that the effectiveness of the massive MIMO-OFDM wireless communication system affected by the Doppler effect is significantly enhanced by applying the proposed pilot-based CFO estimation and compensation technique. The system would achieve the best bit error rate performance when using the BPSK modulation scheme and a large number of receive antennas. Moreover, when compared to some existing methods, the proposed technique shows a better performance for the massive MIMO wireless communication system.
Overview and Comparison of Methodologies for Extraction of the Specific current of a MOS Transistor

This paper presents an overview and comparison of four methodologies used for empirical determination of the specific current Is of a MOS transistor. The goal of the paper is to evaluate the accuracy of these methodologies and determine whether the obtained results could be used for hand calculations in the early stage of the design of a CMOS circuit. For this purpose, the methodologies are applied for the extraction of the specific current of a NMOS transistor in a 45nm CMOS technology. The obtained results for Is are used to calculate the transfer characteristics of the transistor and are compared with the transfer characteristics obtained with SPICE simulation to verify the accuracy of the methodologies.
Soldering of Ag-coated 5A06-Al substrates using Ni foam/SAC305 composite solders

Ag coated 5A06-Al alloy substrates were joined using Ni foam strengthened SAC305 solder. Effects of Ni foam porosity and joining time on the microstructure and shear strength of the joints were studied. Results showed that the initial reaction product was mainly (Cu,Ni)6Sn5 phase, and layered- as well as plate-like Ag3Sn phases were formed at the interface in the joint using 80%-porosity Ni foam. When the porosity of Ni foam was changed to 60%, (Ni,Cu)3Sn4 phase was largely formed in the joint, which blocked the growth of Ag3Sn phase on the 5A06-Al substrate surface. The brittle (Ni,Cu)3Sn4 phase and the ductile Ni skeletons could form a continuous interpenetrating structure, which improved the shear strength of joints. The joint using 60%-porosity Ni foam/SAC305 solder obtained a shear strength of 50.25 MPa at 10 min.
Identifying universal safety signs using computer vision for an assistive feedback mobile application

When walking on the road or navigating unfamiliar areas, the elderly, visually handicapped, and persons with hearing loss encounter challenges. The information on numerous safety signs such as road signs, traffic signs, workplace safety signs or industrial hazard signs is usually unhelpful to them. This paper proposes a solution that can detect a comprehensive set of safety signs in real-time. Our app runs a deep learning model pre-trained on a custom-built dataset. The deep learning model we have used is explicitly built for object detection to find regions of interest, create appropriate bounding boxes, and classify the signs with three different levels of severity - Danger, Caution, and Prohibitory. The camera-equipped smartphone relays haptic or audio feedback upon the successful detection of a safety sign.
A Continuous Deep Learning Model for Brain PET Image Denoising in Medical Internet of Things

Deployment of the Internet of Things (IoT) in medical imaging is critical for cross-modality tasks, such as PET image denoising. IoT helps to gather innumerable day-to-day medical big data, which is essential for training robust and well-optimized deep learning models. In this work, we propose a modified cycle-consistent generative adversarial network (CycleGAN) as a cloud-based continuous learning approach for synthesizing full-dose (FD) PET images from low-dose (LD) PET images. In continuous learning first a model trained with a limited number of actual data includes LD and corresponding FD images on a cloud computing platform of IoT-based PET devices. Thereafter, it continually trains with its new predictions those that pass through a criteria/filter. In this study, the criteria for accepting or rejecting predictions are based on a comparison between the pixel-wise correlation of predicted FD and actual LD and the average correlation between reference FD and LD images in the first training. If the bias between the correlations was under 5%, the predicted FD and actual LD enter to training process otherwise it will reject them. The clinical dataset includes 140 brain PET/CT images were employed for LD to FD PET transformation. The main model (M-50) was trained with 50 cases and evaluated on the main test dataset (T-1). Then the model was fed with 14 separate LD images and to generate 10 FD images with the abovementioned criteria. M-50 were again trained with these 10 new datasets (M-60) and tested on T-1. This approach was repeated 4 times and each new model (M-70, M-80, and M-90) was tested on T-1. The results indicated that in each step the bias decreased by 7.6%, 11.96%, 18.48%, and 21.74% relative to the M-50, thus confirming the potential of the proposed approach.