Development, validation and use of custom software for the analysis of pain trajectories

In chronic musculoskeletal conditions, the prognosis tends to be more informative than the diagnosis for the future course of the disease. Many studies have identified clusters of patients who seemingly share similar pain trajectories. In a dataset of low back pain (LBP) patients, pain trajectories have been identified, and distinct trajectory types have been defined, making it possible to create pattern recognition software that can classify patients into respective pain trajectories reflecting their condition. It has been suggested that the classification of pain trajectories may create clinically meaningful subgroups of patients in an otherwise heterogeneous population of patients with LBP. A software tool was created that combined the ability to recognise the pain trajectory of patients with a system that could create subgroups of patients based on their characteristics. This tool is primarily meant for researchers to analyse trends in large heterogeneous datasets without large losses of data. Prospective analysis of pain trajectories is not directly helpful for clinicians. However, the tool might aid in the identification of patient characteristics which have predictive capabilities of the most likely trajectory a patient might experience in the future. This will help clinicians to tailor their advice and treatment for a specific patient.</p

Robust fuzzy decentralized control for nonlinear large-scale systems with parametric uncertainties

This paper addresses the problem of robust fuzzy decentralized control for a class of nonlinear large-scale systems in the presence of parametric uncertainties. The Takagi-Sugeno (T-S) fuzzy system is adopted for modeling such systems. Both fuzzy state feedback decentralized controller and fuzzy observer-based decentralized controller are developed. Sufficient conditions are derived for robust stabilization in the sense of Lyapunov asymptotic stability and formulated in the format of linear matrix inequalities (LMIs). The effectiveness of the proposed fuzzy controller is finally demonstrated through numerical simulations on a two-machine interconnected system.Shaocheng Tong, Peng Shi and Hasan Al-Madfa