On the Use of Non-Contact Capacitive Sensors for the Assessment of Postural Hand Tremor of Individuals with Parkinson's Disease.

Parkinsonian tremor manifests in different types: rest, postural, and action tremors. The postural tremor occurs while a body part is held straight out from the body in a stable position against gravity. The Unified Parkinson's Disease Rating Scale (UPDRS), which is a subjective assessment performed by the qualitative judgment of neurologists, is the clinical standard for parkinsonian tremor assessment. Despite the common use of subjective methods, inertial measurement unit (IMU) sensors are largely used in many studies as a motion capture system to objective assessment of tremors. However, this kind of sensor must be attached to the patient's body, it limits the patient's movements and requires specific techniques for correct positioning in the limb. In this sense, non-contact capacitive (NCC) sensors are an alternative proposed in this research to record the motor activity of the hand and wrist during a pose against gravity. In order to assess the postural tremor and evaluate this novel sensing technology, data from ten subjects, five with Parkinson's disease (PD) and five neurologically healthy (H) matched in age and sex, were collected. We analyzed the instantaneous mean frequency (IMNF) of the signals from NCC and gyroscope sensors for both groups. The selected descriptive statistical variables allowed discrimination (p <; 0.05) among subjects from H and PD groups while using the gyroscope or the NCC sensor. The obtained results indicate that the NCC sensor can measure the postural hand tremor, and also that frequency features extracted from the collected signals can be used to discriminate subjects from both groups.

On the Use of t-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative disorder that remains incurable. The available treatments for the disorder include pharmacologic therapies and deep brain stimulation (DBS). These approaches may cause distinct side effects and motor responses. This work presents the application of t-distributed stochastic neighbor embedding (t-SNE), which is a machine learning algorithm for nonlinear dimensionality reduction and data visualization, for the problem of discriminating neurologically healthy individuals from those suffering from PD (treated with levodopa and DBS). Furthermore, the assessment of classification methods is presented. Inertial and electromyographic data were collected while the subjects executed a sequence of four motor tasks. The results were focused on the comparison of the classification performance of a support vector machine (SVM) while discriminating two-dimensional feature sets estimated from Principal Component Analysis (PCA), Sammon's mapping, and t-SNE. The results showed visual and statistical differences for all three investigated groups. Classification accuracy for PCA, Sammon's mapping, and t-SNE was, respectively, 73.5%, 78.6%, and 96.9% for the training set and 67.8%, 74.1%, and 76.6% for the test set. The possibility of discriminating healthy individuals from those with PD treated with levodopa and DBS highlights the fact that each treatment method produces distinct motor behavior. The scatter plots resulting from t-SNE could be used in the clinical practice as an objective tool for measuring the discrepancy between normal and abnormal motor behaviors, being thus useful for the adjustment of treatments and the follow-up of the disorder.