The purpose of this study was to find an appropriate solution in reducing inflammation around the ankle joint for Rheumatoid Arthritis (RA) patients, so they are able to increase their endurance and improve their overall quality of life. RA patients have to deal with a significant amount of complications that include chronic inflammation, continuous pain in their joints, and overwhelming stress. In addition, it is very common for RA patients to develop severe mental issues that only makes matters worse. As a result, it is imperative that treatments are provided to RA patients to improve their current situation. Three devices from the current market, made for reducing inflammation of the ankle, were chosen for evaluating the effectiveness of each device. It was determined that with 95% confidence that the Gonicc Professional Foot Sleeve was the most effective in reducing inflammation. A prototype was developed based on the feedback of the participants. Further improvements, the prototype will be compared against the Gonicc Professional Foot Sleeve to determine which is the best solution to improve millions of RA patients' lives.

A dynamical approach is used to avoid isolating systems and instead view systems as interacting together. The current study applied a dynamical approach to heart rate variability and personality. There were two main research questions that this study sought to answer with a dynamical analysis of heart rate variability and personality: “Can we listen to a heartbeat and draw connections to behavior and personality?” and “Is dynamical analysis more effective than traditional analysis at finding correlations between heart rate variability and personality?” To answer these questions a dynamical analysis of heart rate variability was conducted (detrended fluctuation analysis; DFA) along with traditional analysis (standard deviations of NN intervals, SDNN, and root mean squared of successive deviations, RMSSD) and then correlations between heart rate variability measures and personality traits from the Big Five Inventory, Positive and Negative Affect schedule, and State-Trait Anxiety Inventory were examined. Data for this study came from the Rapid Automatic & Adaptive Model for Performance Prediction (RAAMP2) Dataset that was part of The Multimodal Objective Sensing to Assess Individuals with Context (MOSAIC) project. There were no statistically significant correlations between heart rate variability and personality. However, there were notable correlations between extraversion and SDNN and RMSSD and between positive affect and SDNN and RMSSD. We found that SDNN and RMSSD were more closely correlated to each other compared to DFA to either measure. This suggests that DFA can provide information that SDNN and RMSSD do not. Future research can explore dynamic analysis of heart rate variability and other nested systems.