Parkinson\u27s Symptoms quantification using wearable sensors

Parkinson’s disease (PD) is a common neurodegenerative disorder affecting more than one million people in the United States and seven million people worldwide. Motor symptoms such as tremor, slowness of movements, rigidity, postural instability, and gait impairment are commonly observed in PD patients. Currently, Parkinsonian symptoms are usually assessed in clinical settings, where a patient has to complete some predefined motor tasks. Then a physician assigns a score based on the United Parkinson’s Disease Rating Scale (UPDRS) after observing the motor task. However, this procedure suffers from inter subject variability. Also, patients tend to show fewer symptoms during clinical visit, which leads to false assumption of the disease severity. The objective of this study is to overcome this limitations by building a system using Inertial Measurement Unit (IMU) that can be used at clinics and in home to collect PD symptoms data and build algorithms that can quantify PD symptoms more effectively. Data was acquired from patients seen at movement disorders Clinic at Sanford Health in Fargo, ND. Subjects wore Physilog IMUs and performed tasks for tremor, bradykinesia and gait according to the protocol approved by Sanford IRB. The data was analyzed using modified algorithm that was initially developed using data from normal subjects emulating PD symptoms. For tremor measurement, the study showed that sensor signals collected from the index finger more accurately predict tremor severity compared to signals from a sensor placed on the wrist. For finger tapping, a task measuring bradykinesia, the algorithm could predict with more than 80% accuracy when a set of features were selected to train the prediction model. Regarding gait, three different analysis were done to find the effective parameters indicative of severity of PD. Gait speed measurement algorithm was first developed using treadmill as a reference. Then, it was shown that the features selected could predict PD gait with 85.5% accuracy

The Urban Toolkit: A Grammar-based Framework for Urban Visual Analytics

While cities around the world are looking for smart ways to use new advances in data collection, management, and analysis to address their problems, the complex nature of urban issues and the overwhelming amount of available data have posed significant challenges in translating these efforts into actionable insights. In the past few years, urban visual analytics tools have significantly helped tackle these challenges. When analyzing a feature of interest, an urban expert must transform, integrate, and visualize different thematic (e.g., sunlight access, demographic) and physical (e.g., buildings, street networks) data layers, oftentimes across multiple spatial and temporal scales. However, integrating and analyzing these layers require expertise in different fields, increasing development time and effort. This makes the entire visual data exploration and system implementation difficult for programmers and also sets a high entry barrier for urban experts outside of computer science. With this in mind, in this paper, we present the Urban Toolkit (UTK), a flexible and extensible visualization framework that enables the easy authoring of web-based visualizations through a new high-level grammar specifically built with common urban use cases in mind. In order to facilitate the integration and visualization of different urban data, we also propose the concept of knots to merge thematic and physical urban layers. We evaluate our approach through use cases and a series of interviews with experts and practitioners from different domains, including urban accessibility, urban planning, architecture, and climate science. UTK is available at urbantk.org.Comment: Accepted at IEEE VIS 2023. UTK is available at http://urbantk.or

Predicting young imposter syndrome using ensemble learning

Background. Imposter syndrome (IS), associated with self-doubt and fear despite clear accomplishments and competencies, is frequently detected in medical students and has a negative impact on their well-being. This study aimed to predict the students' IS using the machine learning ensemble approach. Methods. This study was a cross-sectional design among medical students in Bangladesh. Data were collected from February to July 2020 through snowball sampling technique across medical colleges in Bangladesh. In this study, we employed three different machine learning techniques such as neural network, random forest, and ensemble learning to compare the accuracy of prediction of the IS. Results. In total, 500 students completed the questionnaire. We used the YIS scale to determine the presence of IS among medical students. The ensemble model has the highest accuracy of this predictive model, with 96.4%, while the individual accuracy of random forest and neural network is 93.5% and 96.3%, respectively. We used different performance matrices to compare the results of the models. Finally, we compared feature importance scores between neural network and random forest model. The top feature of the neural network model is Y7, and the top feature of the random forest model is Y2, which is second among the top features of the neural network model. Conclusions. Imposter syndrome is an emerging mental illness in Bangladesh and requires the immediate attention of researchers. For instance, in order to reduce the impact of IS, identifying key factors responsible for IS is an important step. Machine learning methods can be employed to identify the potential sources responsible for IS. Similarly, determining how each factor contributes to the IS condition among medical students could be a potential future direction

Vertical Ground Reaction Force Marker for Parkinson’s Disease

Parkinson’s disease (PD) patients regularly exhibit abnormal gait patterns. Automated differentiation of abnormal gait from normal gait can serve as a potential tool for early diagnosis as well as monitoring the effect of PD treatment. The aim of current study is to differentiate PD patients from healthy controls, on the basis of features derived from plantar vertical ground reaction force (VGRF) data during walking at normal pace. The current work presents a comprehensive study highlighting the efficacy of different machine learning classifiers towards devising an accurate prediction system. Selection of meaningful feature based on sequential forward feature selection, the swing time, stride time variability, and center of pressure features facilitated successful classification of control and PD gaits. Support Vector Machine (SVM), K-nearest neighbor (KNN), random forest, and decision trees classifiers were used to build the prediction model. We found that SVM with cubic kernel outperformed other classifiers with an accuracy of 93.6%, the sensitivity of 93.1%, and specificity of 94.1%. In comparison to other studies, utilizing same dataset, our designed prediction system improved the classification performance by approximately 10%. The results of the current study underscore the ability of the VGRF data obtained non-invasively from wearable devices, in combination with a SVM classifier trained on meticulously selected features, as a tool for diagnosis of PD and monitoring effectiveness of therapy post pathology

Depression and anxiety among university students during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional survey

The purpose of this study was to investigate the prevalence of depression and anxiety among Bangladeshi university students during the COVID-19 pandemic. It also aimed at identifying the determinants of depression and anxiety. A total of 476 university students living in Bangladesh participated in this cross-sectional web-based survey. A standardized e-questionnaire was generated using the Google Form, and the link was shared through social media—Facebook. The information was analyzed in three consecutive levels, such as univariate, bivariate, and multivariate analysis. Students were experiencing heightened depression and anxiety. Around 15% of the students reportedly had moderately severe depression, whereas 18.1% were severely suffering from anxiety. The binary logistic regression suggests that older students have greater depression (OR = 2.886, 95% CI = 0.961–8.669). It is also evident that students who provided private tuition in the pre-pandemic period had depression (OR = 1.199, 95% CI = 0.736–1.952). It is expected that both the government and universities could work together to fix the academic delays and financial problems to reduce depression and anxiety among university students

Prevalence and determinants of physical violence against doctors in Bangladeshi tertiary care hospitals

Abstract Background The increasing physical violence against doctors in the health sector has become an alarming global problem and a key concern for the health system in Bangladesh. This study aimed to determine the prevalence and associated factors of physical violence against doctors in Bangladeshi tertiary care hospitals. Methods A cross-sectional survey was performed among 406 doctors working in tertiary care hospitals. Data were collected using a self-administered questionnaire and the binary logistic regression model was employed for predicting physical violence against doctors. Results Of the participants, 50 (12.3%) doctors reported being exposed to physical violence in 12 months prior to the survey. According to logistic regression analysis, aged less than 30 years or younger, male and never-married doctors were prone to physical violence. Similarly, doctors from public hospitals and those worked in emergency departments were at higher risk of physical violence. More than 70% of victims reported that patients’ relatives were the main perpetrators. Two-thirds of the victims referred to violence in the hospitals as a grave concern. Conclusions Physical violence against doctors is relatively common in the emergency departments and public hospitals in Bangladesh. This study found that male and younger doctors were at high risk of exposing physical violence. To prevent hospital violence, authorities must develop human resources, bolster patient protocol and offer physician training

Investigation of Computing Students’ Performances in a Fully Online Environment During COVID-19 Pandemic

COVID-19 hit the world unexpectedly, forcing humans to isolate themselves. It has placed the lives of people in jeopardy with its fury. The COVID-19 has a detrimental effect on the world's education spheres. It has imposed a global lockdown, with a negative impact on the students' lives. Continuing regular classes’ on-campus were out of the question. At that moment, online learning came to us as a savior. The quality of online education was yet to be tested on a large scale compared to regular education. This paper reveals the factors that influence four programming courses from Computing students at X University and puts them on the table with an analysis backing it up so that, in future, authorities can design the proper structure for high quality online education

Comparison of different feature selection methods.

<p>Comparison of different feature selection methods.</p