Computer model for leg agility quantification and assessment for Parkinson's disease patients.

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.

Pronation and supination analysis based on biomechanical signals from Parkinson's disease patients.

In this work, a fuzzy inference model to evaluate hands pronation/supination exercises during the MDS-UPDRS motor examination is proposed to analyze different extracted features from the bio-mechanical signals acquired from patients with Parkinson's disease (PD) in different stages of severity. Expert examiners perform visual assessments to evaluate several aspects of the disease. Some previous work on this subject does not contemplate the MDS-UPDRS guidelines. The method proposed in this work quantifies the biomechanical features examiners evaluate. The extracted characteristics are used as inputs of a fuzzy inference model to perform an assessment strictly attached to the MDS-UPDRS. The acquired signals are processed by techniques of digital signal processing and statistical analysis. The experiments were performed in collaboration with clinicians and patients from different PD associations and institutions. In total, 210 different measurements of patients with PD, plus 20 different measurements of healthy control subjects were performed. With objective values rated by several feature extraction procedures there is the possibility to track down the disease evolution in a patient, and to detect subtle changes in the patient's condition.

Fuzzy inference model evaluating turn for Parkinson's disease patients.

Parkinson's disease is a chronic illness that affects motor skills. The Unified Parkinson's Disease Rating Scale sponsored by the Movement Disorder Society (MDS-UPDRS) quantifies the current state of the disease based on clinician's observations. In this scale, turning is part of the gait assessment, yet specific guidelines on which features to observe and rate are still unclear. What is more, only visual impairment detection is used as the main subjective rating tool. In this respect, four biomechanical features are extracted from sensors worn on the lower limbs in this work. Afterwards, a turning assessment score is computed by means of a fuzzy inference model constructed based on the examiners knowledge. Overall, 46 patients with varying motor impairment severity underwent a full MDS-UPDRS motor examination and were monitored using a measurement system that includes inertial sensors on each ankle. Turning rating scores computed are reasonably consistent with examiners opinions. Nevertheless, the model proposed herein will always output the same score given the same inputs; whereas the subjective nature of examiners observations translates into uncertainty and variability in the rating scores. Furthermore, the continuous scale implemented in this work prevents the floor/ceiling effect inherent of discrete scales.