A Computer Method for Pronation-Supination Assessment in Parkinson's Disease Based on Latent Space Representations of Biomechanical Indicators.

One problem in the quantitative assessment of biomechanical impairments in Parkinson's disease patients is the need for scalable and adaptable computing systems. This work presents a computational method that can be used for motor evaluations of pronation-supination hand movements, as described in item 3.6 of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The presented method can quickly adapt to new expert knowledge and includes new features that use a self-supervised training approach. The work uses wearable sensors for biomechanical measurements. We tested a machine-learning model on a dataset of 228 records with 20 indicators from 57 PD patients and eight healthy control subjects. The test dataset's experimental results show that the method's precision rates for the pronation and supination classification task achieved up to 89% accuracy, and the F1-scores were higher than 88% in most categories. The scores present a root mean squared error of 0.28 when compared to expert clinician scores. The paper provides detailed results for pronation-supination hand movement evaluations using a new analysis method when compared to the other methods mentioned in the literature. Furthermore, the proposal consists of a scalable and adaptable model that includes expert knowledge and affectations not covered in the MDS-UPDRS for a more in-depth evaluation.

Computer models evaluating hand tremors in Parkinson's disease patients.

One of the most characteristic signs of Parkinson's disease (PD) is hand tremor. The MDS-UPDRS scale evaluates different aspects of the disease. The tremor score is a part of the MDS-UPDRS scale, which provides instructions for rating it, by observation, with an integer from 0 to 4. Nevertheless, this form of assessment is subjective and dependent on visual acuity, clinical judgment, and even the mood of the individual examiner. On the other hand, in many cases, existing computational models proposed to resolve the disadvantages of the MDS-UPDRS scale may have uncertainty in differentiating a category of a slight Parkinson tremor from voluntary movements. In this study, 554 measurements from Parkinson's patients, and 60 measurements from healthy subjects, were recorded with inertial sensors placed on the back of each hand. Five biomechanical indicators characterised the hand tremor. With these indicators, the three fuzzy inference models proposed can differentiate, in the first instance, the presence of postural or resting tremor from a normal movement of the hand, and if detected, to determine its severity. The fuzzy inference models allowed following the criteria of the MDS-UPDRS scale, providing an evaluation with an accuracy of two decimal digits and which, due to its simplicity, can be implemented in clinical environments. The assessments of three experts validated the computer model.

Fuzzy inference model based on triaxial signals for pronation and supination assessment in Parkinson's disease patients.

Nowadays, the Unified Parkinson Disease Rating Scale supported by the Movement Disorder Society (MDS-UPDRS), is a standardized and widely accepted instrument to rate Parkinson's disease (PD). This work presents a thorough analysis of item 3.6 of the MDS-UPDRS scale which corresponds to the pronation and supination hand movements. The motivation for this work lies in the objective quantification of motor affectations not covered by the MDS-UPDRS scale such as unsteady oscillations and velocity decrements during the motor exploration. Overall, 12 different bio-mechanical features were quantified based on measurements performed by inertial measurement units (IMUs). After a feature selection process, the selected bio-mechanical features were used as inputs for a fuzzy inference model that predicts the stage of development of the disease in each patient. In addition to this model's output, the scores of three different expert examiners and the output of a fuzzy inference model which covers affectations strictly attached the MDS-UPDRS guidelines, were also considered to obtain an integrated computational model. The proposed integrated model was incorporated using the Analytic Hierarchy Process (AHP), which gives the novelty of a combined score that helps expert examiners to give a broader assessment of the disease that covers both affectations mentioned in the MDS-UPDRS guidelines and affectations not covered by it in an objective manner.