Objective and automatic classification of Parkinson disease with Leap Motion controller

Background: The main objective of this paper is to develop and test the ability of the Leap Motion controller (LMC) to assess the motor dysfunction in patients with Parkinson disease (PwPD) based on the MDS-UPDRSIII exercises. Four exercises (thumb forefinger tapping, hand opening/closing, pronation/supination, postural tremor) were used to evaluate the characteristics described in MDS-UPDRSIII. Clinical ratings according to the MDS/UPDRS-section III items were used as target. For that purpose, 16 participants with PD and 12 healthy people were recruited in Ospedale Cisanello, Pisa, Italy. The participants performed standardized hand movements with camera-based marker. Time and frequency domain features related to velocity, angle, amplitude, and frequency were derived from the LMC data. Results: Different machine learning techniques were used to classify the PD and healthy subjects by comparing the subjective scale given by neurologists against the predicted diagnosis from the machine learning classifiers. Feature selection methods were used to choose the most significant features. Logistic regression (LR), naive Bayes (NB), and support vector machine (SVM) were trained with tenfold cross validation with selected features. The maximum obtained classification accuracy with LR was 70.37%; the average area under the ROC curve (AUC) was 0.831. The obtained classification accuracy with NB was 81.4%, with AUC of 0.811. The obtained classification accuracy with SVM was 74.07%, with AUC of 0.675. Conclusions: Results revealed that the system did not return clinically meaningful data for measuring postural tremor in PwPD. In addition, it showed limited potential to measure the forearm pronation/supination. In contrast, for finger tapping and hand opening/closing, the derived parameters showed statistical and clinical significance. Future studies should continue to validate the LMC as updated versions of the software are developed. The obtained results support the fact that most of the set of selected features contributed significantly to classify the PwPD and healthy subjects

Analisi strumentale degli eventi motori deambulatori in parkinsoniani ed in soggetti sani di controllo

studio chinesiologico delle principali alterazioni del passo in soggetti con sindromi extrapiramidal

Studio di un prototipo di game interattivo per la riabilitazione motoria del parkinsoniano

Si descrive un prototipo di game interattivo per la riabilitazione cognitivo-motoria di soggetti affetti da malattia di Parkinso

Diaphragm ultrasonography in amyotrophic lateral sclerosis: a diagnostic tool to assess ventilatory dysfunction and disease severity

BACKGROUND: Respiratory failure represents an unavoidable step in patients with amyotrophic lateral sclerosis (ALS) and other motor neuron diseases (MND). The development of diaphragm ultrasonography (DUS) provides an alternative useful and risk-free tool to supply clinical, functional, and neurophysiological assessment of respiratory muscle weakness. Our aim was to evaluate if sonographic changes (thickness and echogenicity in the costal portion of the diaphragm, at rest and during respiratory movements) may be used in ALS patients to assess disease severity over time, to rule out any risk or discomfort due to traditional neurophysiological investigations. METHODS: Twenty ALS patients (mean age, 64.6\u2009\ub1\u200910.5 years) were enrolled and data were compared with age-matched healthy volunteers; DUS data were correlated with respiratory function and disease severity scale. Examinations were performed using Telemed Echo-wave II or Esaote MyLabGamma devices in conventional B-Mode. RESULTS: Mean resting thickness was reduced in all cases; changes in thickness during inspiration and expiration were also reduced (p\u2009<\u20090.0001) and lost in severe cases (n\u2009=\u20093). In bulbar-onset disease, respiratory scores were strictly correlated with the difference in diaphragm thickness between full inspiration-and expiration-as well as on the diaphragm thickness in expiration (p\u2009<\u20090.001). CONCLUSIONS: DUS represents a simple, painless, and risk-free tool; moreover, it provides useful functional and structural insights to the understanding of diaphragm function and the degree of respiratory failure in ALS

Leap motion evaluation for assessment of upper limb motor skills in Parkinson's disease

The main goal of this study is to investigate the potential of the Leap Motion Controller (LMC) for the objective assessment of motor dysfunctioning in patients with Parkinson's disease (PwPD). The most relevant clinical signs in Parkinson's Disease (PD), such as slowness of movements, frequency variation, amplitude variation, and speed, were extracted from the recorded LMC data. Data were clinically quantified using the LMC software development kit (SDK). In this study, 16 PwPD subjects and 12 control healthy subjects were involved. A neurologist assessed the subjects during the task execution, assigning them a score according to the MDS/UPDRS-Section III items. Features of motor performance from both subject groups (patients and healthy controls) were extracted with dedicated algorithms. Furthermore, to find out the significance of such features from the clinical point of view, machine learning based methods were used. Overall, our findings showed the moderate potential of LMC to extract the motor performance of PwPD

Automatically recognized postural transitions in TUG test using machine learning methods

In this work is described the automatic recognition of postural transition during TUG test, i.e. sit to stand and stand to sit, from inertial sensors dat