1,721,067 research outputs found
Evaluating the feasibility of chaos-based guidance systems in swarm projectiles
No full textThis study investigates the feasibility and performance implicationsof chaotic trajectory tracking for guided projectiles using PIDcontrollers under realistic environmental disturbances. Specifically, Lorenz,Sprott-A, and Halvorsen chaotic systems were used as reference trajectoriesin a three-dimensional projectile framework incorporating windperturbations and actuator dynamics. The methodology involved simulatingprojectile dynamics with gimbal angle feedback to compute pitchand yaw variations, angular velocities, and torque-based control effort.Simulation results revealed that all three chaotic trajectories were trackableusing PID control. Among them, the Sprott-A system demonstratedthe most favorable profile for energy-constrained applications, requiringthe least control effort (198.43 Nm·s) and angular change (4805.74°),along with the lowest energy consumption (18.24 kJ). In contrast, theHalvorsen system exhibited the highest torque demand (122696.79 Nm)and angular variability (31246.31°), making it less suitable for systemswith limited actuation capability. Lorenz presented intermediate performancein all metrics. The results confirm that chaotic references canenhance evasiveness while remaining within mechanical and energeticconstraints. These findings support the integration of chaos-informed trajectoryplanning into autonomous swarm projectile systems for improve
A home-based functional electrical stimulation system for upper-limb stroke rehabilitation
No full textDue to an increased population of stroke patients and subsequent demand on health providers, there is an urgent need for effective stroke rehabilitation technology that can be used in patients' own homes. Over recent years, systems employing functional electrical stimulation (FES) have shown the ability to provide effective therapy. However, there is currently no low-cost therapeutic system available which simultaneously supplies FES to muscles in the patient's shoulder, arm and wrist to provide co-ordinated functional movement. This restricts the effectiveness of treatment, and hence the ability to support activities of daily living.In this thesis a home-based low cost rehabilitation system is developed which substantially extends the current state of art in terms of sensing and control methodologies. In particular, it embeds novel non-contact sensing approaches; the first use of an electrode array within a closed-loop model based control scheme; an interactive task display system; and an integrated learning-based controller for multiple muscles within the upper-limb (UL), which supports co-ordinated tasks. The thesis then focuses on compacting the prototype by upgrading the depth sensor and using embedded systems to transfer it to the home environment.Currently available home-based systems employing FES for UL rehabilitation are first reviewed in terms of their underlying technology, operation, scope and clinical evidence. Motivated by this, a detailed examination of a prototype system is carried out that combines low cost non-contact sensors with closed-loop FES controllers. Then potential avenues to extend the technology are highlighted, with specific focus given to low-cost non-contact based sensors for the hand and wrist. Sensing approaches are then reviewed and evaluated in terms of their scope to support the intended system requirements. Electrode array hardware is developed in order to provide accurate movement capability. Biomechanical models of the combined stimulated arm and mechanical support are then formulated. Using these, model-based iterative learning control methodologies are then designed and implemented.The system is evaluated with both unimpaired participants and stroke patients undergoing a course of treatment. Finally, a home-based prototype is developed which integrates and extends the aforementioned components. Results confirm the system's scope to provide more effective stroke rehabilitation. Based on the achieved results, courses of future work necessary to continue this development are outlined
Machine learning interpretability in diabetes risk assessment: a SHAP analysis
No full textDiabetes continues to be a complicated and prevalent metabolic illness, providing a serious burden to public health. While machine learning approaches like extreme gradient boosting (XGBoost) provide intriguing options for diabetes prediction, their 'black-box' nature typically limits clinical interpretability. To overcome this gap, our work applied SHapley Additive exPla-nations (SHAP) to give insights into the XGBoost model's predictions. The dataset utilized in this research comprised of 253,680 patients and contained 21 parameters, such as General Health Status, High Blood Pressure Status, Age, and Body Mass Index. After feature selection using Recursive Feature Elimination (RFE), 15 important characteristics were discovered. In the test set, the XGBoost model obtained an accuracy of 86.6%, precision of 54.1%, recall of 17.0%, and an F1-score of 25.9% for the Original dataset. For the RFE dataset, the model displayed an accuracy of 86.6%, precision of 54.9%, recall of 16.5%, and an F1-score of 25.3%. SHAP analysis found that General Health Status, High Blood Pressure Status, Age, and Body Mass Index were the most important characteristics in both the Original and RFE datasets. This work provides as a platform for transparent and clinically applicable predictive modeling, assisting in early diabetes identification and preventive healthcare
A home-based FES system for upper-limb stroke rehabilitation with iterative learning control
No full textRespiratory diseases prediction from a novel chaotic system
No full textPandemics can have a significant impact on international health systems. Researchers have found that there is a correlation between weather conditions and respiratory diseases. This paper focuses on the non-linear analysis of respiratory diseases and their relationship to weather conditions. Chaos events may appear random, but they may actually have underlying patterns. Edward Lorenz referred to this phenomenon in the context of weather conditions as the butterfly effect. This inspired us to define a chaotic system that could capture the properties of respiratory diseases. The chaotic analysis was performed and was related to the difference in the daily number of cases received from real data. Stability analysis was conducted to determine the stability of the system and it was found that the new chaotic system was unstable. Lyapunov exponent analysis was performed and found that the new chaotic system had Lyapunov exponents of (+, 0, -, -). A dynamic neural architecture for input-output modeling of nonlinear dynamic systems was developed to analyze the findings from the chaotic system and real data. A NARX network with inputs (maximum temperature, pressure, and humidity) and one output was used to to overcome any delay effects and analyze derived variables and real data (patients number). Upon solving the system equations, it was found that the correlation between the daily predicted number of patients and the solution of the new chaotic equation was 90.16%. In the future, this equation could be implemented in a real-time warning system for use by national health services
Demystifying fractional order chaotic respiratory disease system with XAI
No full textThe current study delves into the intricate association between meteorological conditions and the incidence of Upper Respiratory Tract Infections (URTIs), leveraging the advanced capabilities of the CatBoost machine learning algorithm in conjunction with a Fractional Order Chaotic System and cutting-edge Explainable Artificial Intelligence (XAI) techniques. By analyzing comprehensive meteorological and health data collected from the Pamukova District (Marmara Region, Turkey), this research paper employs the SHapley Additive exPlanations (SHAP) values to elucidate the model’s predictions, emphasizing the consequential effects of mean temperature, humidity, and atmospheric pressure over a 5-day period on the occurrence of URTIs [1, 2]. The findings obtained by the related analyses demonstrate that mean temperature holds a dominant influence on URTI predictions, with SHAP values peaking at 5.6, thus underscoring its critical role as a predictive marker for increased URTI cases. Similarly, the mean humidity is identified as a pivotal factor, manifesting a maximum SHAP value of 3.2, which signifies its substantial impact on the prevalence of URTIs. In contrast, mean pressure exhibits a wide array of SHAP values, indicating a multifaceted and somewhat indirect correlation with URTI incidences [3].Integral to our approach is the incorporation of a fractional-order system that meticulously accounts for the history of data, thereby offering a nuanced understanding of the temporal dynamics influencing URTI trends. This aspect of our methodology not only enriches the predictive model with a deeper temporal context but also aligns with the foundational principles of chaotic systems as described byLorenz, enhancing the robustness and accuracy of our predictions [1].The predictive prowess of our model is evidenced by an accuracy rate of 75.21%, complemented by precision and recall metrics of 0.75 and 0.5217, respectively. Such metrics highlight the feasibility and effectiveness of our integrated approach in forecasting URTI occurrences with considerable reliability.The implications of our study are far-reaching for the domain of public health, accentuating the imperative to integrate extended weather data within disease prediction frameworks and to inform efficient and timely targeted preventive measures and strategies
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