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Parkinson's disease (PD) is a progressive disorder that affects motor regulation. The Unified Parkinson's Disease Rating Scale sponsored by the Movement Disorder Society (MDS-UPDRS) quantifies the illness progression based on clinical observations. The leg agility is an item in this scale, yet only a visual detection of the features is used, leading to subjectivity. Overall, 50 patients (85 measurements) with varying motor impairment severity were asked to perform the leg agility item while wearing inertial sensor units on each ankle. We quantified features based on the MDS-UPDRS and designed a fuzzy inference model to capture clinical knowledge for assessment. The model proposed is capable of capturing all details regardless of the task speed, reducing the inherent uncertainty of the examiner observations obtaining a 92.35% of coincidence with at least one expert. In addition, the continuous scale implemented in this work prevents the inherent "floor/ceil" effect of discrete scales. This model proves the feasibility of quantification and assessment of the leg agility through inertial signals. Moreover, it allows a better follow-up of the PD patient state, due to the repeatability of our computer model and the continuous output, which are not objectively achievable through visual examination. Graphical abstract ᅟ.

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 classifi-cation 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.

An approach to automatic generation of a network of reference 'views' specifically structured for non-coordinate robot navigation is presented. The reference views are selected from the input flow while the robot moves along a random continuous trajectory in its environment. The network is organized as an atlas that represents inner model of the robot environment. It accumulates a 'rich' navigation knowledge extracted from the flow of 'poor' views. No coordinate transformation is involved, but fundamental topological concepts: the continuity of a robot trajectory and discontinuities ('heavy changes') detected in the input flow. Avoiding any explicit coordinate transformation becomes possible using a local inversion of the knowledge hidden in the relation 'robot control -change of view'. Both techniques -the inversion and detection of heavy changes -are specific for a particular robot architecture. The approach was verified on a virtual static 2D-workspace.