Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning

Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality

Respiration-Based COPD Detection Using UWB Radar Incorporation with Machine Learning

COPD is a progressive disease that may lead to death if not diagnosed and treated at an early stage. The examination of vital signs such as respiration rate is a promising approach for the detection of COPD. However, simultaneous consideration of the demographic and medical characteristics of patients is very important for better results. The objective of this research is to investigate the capability of UWB radar as a non-invasive approach to discriminate COPD patients from healthy subjects. The non-invasive approach is beneficial in pandemics such as the ongoing COVID-19 pandemic, where a safe distance between people needs to be maintained. The raw data are collected in a real environment (a hospital) non-invasively from a distance of 1.5 m. Respiration data are then extracted from the collected raw data using signal processing techniques. It was observed that the respiration rate of COPD patients alone is not enough for COPD patient detection. However, incorporating additional features such as age, gender, and smoking history with the respiration rate lead to robust performance. Different machine-learning classifiers, including Naïve Bayes, support vector machine, random forest, k nearest neighbor (KNN), Adaboost, and two deep-learning models—a convolutional neural network and a long short-term memory (LSTM) network—were utilized for COPD detection. Experimental results indicate that LSTM outperforms all employed models and obtained 93% accuracy. Performance comparison with existing studies corroborates the superior performance of the proposed approach

Non-invasive measurement of a metabolic marker of infant brain function

While near-infrared spectroscopy (NIRS) haemodynamic measures have proven to be vastly useful in investigating human brain development, the haemodynamic response function (HRF) in infants is not yet fully understood. NIRS measurements of the oxidation state of mitochondrial enzyme cytochrome-c-oxidase (oxCCO) have the potential to yield key information about cellular oxygen utilisation and therefore energy metabolism. We used a broadband NIRS system to measure changes in oxCCO, in addition to haemodynamic changes, during functional activation in a group of 33 typically developing infants aged between 4 and 6 months. The responses were recorded over the right temporal lobe while the infants were presented with engaging videos containing social content. A significant increase in oxCCO was found in response to the social stimuli, with maximum increase of 0.238 ± 0.13 μM. These results are the first reported significant change in oxCCO in response to stimulus-evoked activation in human infants and open new vistas for investigating human infant brain function and its energy metabolism

Extended Huckel theory for bandstructure, chemistry, and transport. II. Silicon

In this second paper, we develop transferable semi-empirical parameters for the technologically important material, silicon, using Extended Huckel Theory (EHT) to calculate its electronic structure. The EHT-parameters areoptimized to experimental target values of the band dispersion of bulk-silicon. We obtain a very good quantitative match to the bandstructure characteristics such as bandedges and effective masses, which are competitive with the values obtained within an $sp^3 d^5 s^*$ orthogonal-tight binding model for silicon. The transferability of the parameters is investigated applying them to different physical and chemical environments by calculating the bandstructure of two reconstructed surfaces with different orientations: Si(100) (2x1) and Si(111) (2x1). The reproduced $\pi$- and $\pi^*$-surface bands agree in part quantitatively with DFT-GW calculations and PES/IPES experiments demonstrating their robustness to environmental changes. We further apply the silicon parameters to describe the 1D band dispersion of a unrelaxed rectangular silicon nanowire (SiNW) and demonstrate the EHT-approach of surface passivation using hydrogen. Our EHT-parameters thus provide a quantitative model of bulk-silicon and silicon-based materials such as contacts and surfaces, which are essential ingredients towards a quantitative quantum transport simulation through silicon-based heterostructures.Comment: 9 pages, 9 figure

A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis.

Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%

A novel case of MSTO1 gene related congenital muscular dystrophy with progressive neurological involvement

Recessive mutations in the MSTO1 gene, encoding for a mitochondrial distribution and morphology regulator, have been recently described in a very limited number of patients with multisystem involvement, mostly characterized by myopathy or dystrophy, cerebellar ataxia, pigmentary retinopathy and raised creatine kinase levels. Here we report an additional patient with recessive MSTO1-related muscular dystrophy (MSTO1-RD), and clinical and radiological evidence of progressive cerebellar involvement. Whole-exome sequencing identified two novel MSTO1 missense variants, c.766C > T (p. (Arg256Trp) and c.1435C > T (p. (Pro479Ser), predicted as damaging by in silico tools. We also report a distinct pattern of selective involvement on muscle MRI in MSTO1-RD. This case confirms a consistent MSTO1-related neuromuscular phenotype and in addition suggests a progressive neurological component at least in some patients, in keeping with the mitochondrial role of the defective protein

An Approach to Detect Chronic Obstructive Pulmonary Disease Using UWB Radar-Based Temporal and Spectral Features

Chronic obstructive pulmonary disease (COPD) is a severe and chronic ailment that is currently ranked as the third most common cause of mortality across the globe. COPD patients often experience debilitating symptoms such as chronic coughing, shortness of breath, and fatigue. Sadly, the disease frequently goes undiagnosed until it is too late, leaving patients without the care they desperately need. So, COPD detection at an early stage is crucial to prevent further damage to the lungs and improve quality of life. Traditional COPD detection methods often rely on physical examinations and tests such as spirometry, chest radiography, blood gas tests, and genetic tests. However, these methods may not always be accurate or accessible. One of the key vital signs for detecting COPD is the patient’s respiration rate. However, it is crucial to consider a patient’s medical and demographic characteristics simultaneously for better detection results. To address this issue, this study aims to detect COPD patients using artificial intelligence techniques. To achieve this goal, a novel framework is proposed that utilizes ultra-wideband (UWB) radar-based temporal and spectral features to build machine learning and deep learning models. This new set of temporal and spectral features is extracted from respiration data collected non-invasively from 1.5 m distance using UWB radar. Different machine learning and deep learning models are trained and tested on the collected dataset. The findings are promising, with a high accuracy score of 100% for COPD detection. This means that the proposed framework could potentially save lives by identifying COPD patients at an early stage. The k-fold cross-validation technique and performance comparison with the state-of-the-art studies are applied to validate its performance, ensuring that the results are robust and reliable. The high accuracy score achieved in the study implies that the proposed framework has the potential for the efficient detection of COPD at an early stage