Physiological response to Vibro-Acoustic stimulation in healthy subjects: a preliminary study

Over the last decade Vibro-Acoustic Therapy (VAT) was used for several clinical applications. This paper investigates the use of AcusticA^®, an innovative VAT solution represented by a wooden chaise longue that follows the construction principles of a "musical instrument that stimulates the whole body" in relation to the sound frequencies produced by the music tracks. Ten healthy young subjects were enrolled for this study. Wearable sensors were used to monitor the human physiological response during the VAT session but also during a traditional acoustic therapy (AT) to highlight similarity and differences of those stimulations. Signals from heart activity, brain activity and electrodermal activity were analyzed to investigate the response during the non-stimulated and the stimulated phases. Additionally, two supervised classification algorithms were used to investigate whether the extracted instances could be grouped into two different groups. The results identify a trend of the attention and meditation features extracted from brain activity, which pointed out the relax efficacy of the VAT.Clinical Relevance - There are not significant differences (p<0.05) in the physiological response between the VAT and the AT stimulation, but during the VAT the alpha coefficients were significant different during the stimulated phase. Finally, the classification algorithms were able to classify the groups with an accuracy equal to 100% in the best case.

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.

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.