The emergence of COVID-19 (SARS-CoV-2) has introduced significant global challenges for healthcare systems, healthcare professionals and patients. This current climate creates an opportunity to learn from equitable health systems and move toward making fundamental changes to healthcare systems. Our ethnographic analysis of Wakanda’s healthcare system inBlack Panther, from theMarvel Cinematic Universe, offers opportunities for system-level transformation across healthcare settings. We propose four healthcare system themes within the context of Wakandan identity: (1) technology as an instrument (blending bodies and technology, blending technology with tradition); (2) reimagining medication; (3) warfare and rehabilitation; and (4) preventative approaches to health (prioritising collective health, deprofessionalisation of healthcare services). The preceding themes represent core elements of Wakandan health systems that allow the people of Wakanda to thrive. Wakandans retain a strong identity and cultural traditions while embracing modern technologies. We found that effective upstream approaches to health for all are embedded in anti-colonial philosophies. Wakandans embrace innovation, embedding biomedical engineering and continuous improvement into care settings. For global health systems under strain, Wakanda’s health system identifies equitable possibilities for system change, reminding us that culturally relevant prevention strategies can both decrease pressure on health services and allow all people to thrive.

<p>The objective of this study is to propose a methodology for early detection of Parkinson's disease based on gait patterns. A set of novel features are developed based on self-similar, correlation, and compressibility properties extracted by multiscale features of gait data in the wavelet domain. The dataset used in this study is available in the physionet repository. This study considers only the VGRF data collected from subjects while walking at their normal pace for 2 minutes on a flat surface. </p>

<p>The objective of this study is to propose a methodology for early detection of Parkinson's disease based on gait patterns. A set of novel features are developed based on self-similar, correlation, and compressibility properties extracted by multiscale features of gait data in the wavelet domain. The dataset used in this study is available in the physionet repository. This study considers only the VGRF data collected from subjects while walking at their normal pace for 2 minutes on a flat surface. </p>

Objective: Parkinson's disease (PD) is a common neurodegenerative disorder among adult men and women. The analysis of abnormal gait patterns is among the most important techniques used in the early diagnosis of PD. The overall aim of this study is to identify PD patients using vertical ground reaction force (VGRF) data produced from subjects while walking at a normal pace. Methods and procedures: The current study proposes a novel set of features extracted on the basis of self-similar, correlation, and entropy properties that are characterized by multiscale features of VGRF data in the wavelet-domain. Five discriminatory features have been proposed. PD diagnosis performance of those features are investigated by using a publicly available VGRF dataset (93 controls and 73 cases) and standard classifiers. Logistic regression (LR), support vector machine (SVM) and k-nearest neighbor (KNN) are used for the performance evaluation. Results: The SVM classifier outperformed the LR and KNN classifiers with an average accuracy of 88.89%, sensitivity of 89%, and specificity of 88%. The integration of these five features from the wavelet domain of data, with three time domain features, stance time, swing time and maximum force strike at toe improved the PD diagnosis performance (approximately by 10%), which outperforms existing studies that are based on the same data set. Conclusion: with the previously published approaches, the proposed prediction methodology consisting of the multiscale features in combination with the time domain features shows better performance with fewer features, compared to the existing PD diagnostic techniques. Clinical impact: The findings suggest that the proposed diagnostic method involving multiscale (wavelet) features can improve the efficacy of PD diagnosis.