Respiratory ailments such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer are life-threatening. Respiration rate (RR) is a vital indicator of the wellness of a patient. Continuous monitoring of RR can provide early indication and thereby save lives. However, a real-time continuous RR monitoring facility is only available at the intensive care unit (ICU) due to the size and cost of the equipment. Recent researches have proposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR estimation however, the usage of ECG is limited due to the unavailability of it in wearable devices. Due to the advent of wearable smartwatches with built-in PPG sensors, it is now being considered for continuous monitoring of RR. This paper describes a novel approach for RR estimation using motion artifact correction and machine learning (ML) models with the PPG signal features. Feature selection algorithms were used to reduce computational complexity and the chance of overfitting. The best ML model and the best feature selection algorithm combination was fine-tuned to optimize its performance using hyperparameter optimization. Gaussian Process Regression (GPR) with Fit a Gaussian process regression model (Fitrgp) feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.63, 1.97, and 5.25 breaths per minute, respectively. Patients would be able to track RR at a lower cost and with less inconvenience if RR can be extracted efficiently and reliably from the PPG signal.

Total hip replacement (THR) is a common orthopedic surgery technique that helps thousands of individuals to live normal lives each year. A hip replacement replaces the shattered cartilage and bone with an implant. Most hip implants fail after 10–15 years. The material selection for the total hip implant systems is a major research field since it affects the mechanical and clinical performance of it. Stress shielding due to excessive contact stress, implant dislocation due to a large deformation, aseptic implant loosening due to the particle propagation of wear debris, decreased bone remodeling density due to the stress shielding, and adverse tissue responses due to material wear debris all contribute to the failure of hip implants. Recent research shows that pre-clinical computational finite element analysis (FEA) can be used to estimate four mechanical performance parameters of hip implants which are connected with distinct biomaterials: von Mises stress and deformation, micromotion, wear estimates, and implant fatigue. In vitro, in vivo, and clinical stages are utilized to determine the hip implant biocompatibility and the unfavorable local tissue reactions to different biomaterials during the implementation phase. This research summarizes and analyses the performance of the different biomaterials that are employed in total hip implant systems in the pre-clinical stage using FEA, as well as their performances in in vitro, in vivo, and in clinical studies, which will help researchers in gaining a better understanding of the prospects and challenges in this field.

No abstract

Respiratory ailments are a very serious health issue and can be life-threatening, especially for patients with COVID. Respiration rate (RR) is a very important vital health indicator for patients. Any abnormality in this metric indicates a deterioration in health. Hence, continuous monitoring of RR can act as an early indicator. Despite that, RR monitoring equipment is generally provided only to intensive care unit (ICU) patients. Recent studies have established the feasibility of using photoplethysmogram (PPG) signals to estimate RR. This paper proposes a deep-learning-based end-to-end solution for estimating RR directly from the PPG signal. The system was evaluated on two popular public datasets: VORTAL and BIDMC. A lightweight model, ConvMixer, outperformed all of the other deep neural networks. The model provided a root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of 1.75 breaths per minute (bpm), 1.27 bpm, and 0.92, respectively, for VORTAL, while these metrics were 1.20 bpm, 0.77 bpm, and 0.92, respectively, for BIDMC. The authors also showed how fine-tuning a small subset could increase the performance of the model in the case of an out-of-distribution dataset. In the fine-tuning experiments, the models produced an average R of 0.81. Hence, this lightweight model can be deployed to mobile devices for real-time monitoring of patients.