Parkinson’s disease is a neurodegenerative disorder impacting patients’ movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population’s movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson’s disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection.

Over the past few decades, surgeon training has changed dramatically. Surgical skills are now taught in a surgical skills laboratory instead of the operating room. Simulation-based training helps medical students improve their skills, but it has not revolutionized clinical education. One critical barrier to reaching such a desired goal is the lack of reliable, robust, and objective methods for assessing the effectiveness of training sessions and the development of students. In this paper, we will develop a new comparative analysis approach that employs network models as the central concept in establishing a new assessment tool for the evaluation of the surgical skills of trainees as well as the training processes. The model is populated using participants electromyography data while performing a simulation task. Furthermore, using NASA Task Load Index score, participants’ subjective overload levels are analyzed to examine the impact of participants’ perception of their mental demand, physical demand, temporal demand, performance, effort, and frustration on how participants perform each simulation task. Obtained results indicate that the proposed approach enables us to extract useful information from the raw data and provides an objective method for assessment the of surgical simulation tasks and how the participants’ perception of task impacts their performance.

Background: Laparoscopic surgery demands high precision and skill, necessitating effective training protocols that account for factors such as hand dominance. This study investigates the impact of hand dominance on the acquisition and proficiency of laparoscopic surgical skills, utilizing a novel assessment method that combines Network Models and electromyography (EMG) data. Methods: Eighteen participants, comprising both medical and non-medical students, engaged in laparoscopic simulation tasks, including peg transfer and wire loop tasks. Performance was assessed using Network Models to analyze EMG data, capturing muscle activity and learning progression. The NASA Task Load Index (TLX) was employed to evaluate subjective task demands and workload perceptions. Results: Our analysis revealed significant differences in learning progression and skill proficiency between dominant and non-dominant hands, suggesting the need for tailored training approaches. Network Models effectively identified patterns of skill acquisition, while NASA-TLX scores correlated with participants’ performance and learning progression, highlighting the importance of considering both objective and subjective measures in surgical training. Conclusions: The study underscores the importance of hand dominance in laparoscopic surgical training and suggests that personalized training protocols could enhance surgical precision, efficiency, and patient outcomes. By leveraging advanced analytical techniques, including Network Models and EMG data analysis, this research contributes to optimizing clinical training methodologies, potentially revolutionizing surgical education and improving patient care.

Generative Artificial Intelligence (GAI) significantly impacts various sectors, offering innovative solutions in consultation, self-education, and creativity. However, the trustworthiness of GAI outputs is questionable due to the absence of theoretical correctness guarantees and the opacity of Artificial Intelligence (AI) processes. These issues, compounded by potential biases and inaccuracies, pose challenges to GAI adoption. This paper delves into the trust dynamics in GAI, highlighting its unique capabilities to generate novel outputs and adapt over time, distinct from traditional AI. We introduce a model analyzing trust in GAI through user experience, operational capabilities, contextual factors, and task types. This work aims to enrich the theoretical discourse and practical approaches in GAI, setting a foundation for future research and applications.

Modern software systems, developed using object-oriented programming languages (OOPL), often rely on relational databases (RDB) for persistent storage, leading to the object-relational impedance mismatch problem (IMP). Although Object-Relational Mapping (ORM) tools like Hibernate and Django provide a layer of indirection, designing efficient application-specific data mappings remains challenging and error-prone. The selection of mapping strategies significantly influences data storage and retrieval performance, necessitating a thorough understanding of paradigms and systematic tradeoff exploration. The state-of-the-art systematic design tradeoff space exploration faces scalability issues, especially in large systems. This paper introduces a novel methodology, dubbed Leant, for learning-based analysis of tradeoffs, leveraging machine learning to derive domain knowledge autonomously, thus aiding the effective mapping of object models to relational schemas. Our preliminary results indicate a reduction in time and cost overheads associated with developing (Pareto-) optimal object-relational database schemas, showcasing Leant's potential in addressing the challenges of object-relational impedance mismatch and advancing object-relational mapping optimization and database design.