An Experimental Investigation of Essential Hand Tremor Suppression via a Soft Exoskeletal Glove

In this article, a soft exoskeletal glove is proposed for investigating the potential of utilizing Pneumatic Artificial Muscles (PAMs) for suppressing hand tremor of Essential Tremor (ET) patients. The glove setup is first presented from a conceptual point, which is focused on the dual investigation of applying local kinesthetic forces exerted via PAMs on the user's finger and on the metacarpal region. An experimental protocol is being derived incorporating a force controller adjusted for both investigated cases, while the efficiency of the proposed setup is extensively evaluated under various motion scenarios performed by an ET-diagnosed volunteer. QC 20220620Part of proceedings: ISBN 978-3-90714-402-2</p

Towards Essential Hand Tremor Suppression via Pneumatic Artificial Muscles

Essential tremor (ET) is one of the most common movement disorders and can occur unexpectedly and develop indefinitely to any population unit. According to the recorded statistics of people suffering from ET, the disorder affects 5% of people worldwide, thus creating an ever-increasing need to investigate ways for its suppression and treatment. In this article, we investigate the capability of Pneumatic Artificial Muscles (PAMs) to reduce or even suppress ET leading to the relief of the sufferers. In our work, we designed and constructed two iterations of a glovelike setup and attempted to explore the possibility of suppressing ET on different parts of the hand by exerting force on the index finger and metacarpal region. For both glove iterations, we established an experimental protocol based on the adjustment of a force controller. Finally, we evaluated exhaustively the performance of our setup under multiple motion scenarios with the participation of an ET-diagnosed volunteer.QC 20211011</p

A Study on the Essential and Parkinson's Arm Tremor Classification

In this article, the challenge of discriminating between essential and Parkinson’s tremor is addressed. Although a variety of methods have been proposed for diagnosing the severity of these highly occurring tremor types, their rapid and effective identification, especially in their early stages, proves particularly difficult and complicated due to their wide range of causes and similarity of symptoms. To this goal, a clinical analysis was performed, where a number of volunteers including essential and Parkinson’s tremor-diagnosed patients underwent a series of pre-defined motion patterns, during which a wearable sensing setup was used to measure their lower arm tremor characteristics from multiple selected points. Extracted features from the acquired accelerometer signals were used to train classification algorithms, including decision trees, discriminant analysis, support vector machine (SVM), K-nearest neighbor (KNN) and ensemble learning algorithms, for providing a comparative study and evaluating the potential of utilizing machine learning to accurately discriminate among different tremor types. Overall, SVM related classifiers proved to be the most successful in terms of classifying between Parkinson’s, essential and no tremor diagnosed with percentages reaching up to 100% for a single accelerometer measurement at the metacarpal area. In general and in motion while holding an object position, Coarse Gaussian SVM classifier reached 82.62% accuracy.QC 20220620</p

Towards differential diagnosis of essential and parkinson's tremor via machine learning

In this article, the challenge of identifying between Essential and Parkinson's tremor is addressed. To this goal, a clinical analysis was performed, where a number of volunteers including Essential and Parkinson's tremor-diagnosed patients underwent a series of pre-defined motion patterns, during which a wearable sensing setup was used to measure their lower arm tremor characteristics from multiple selected points. Extracted features from the acquired accelerometer signals were used to train classification algorithms, including decision trees, discriminant analysis, support vector machine (SVM), K-nearest neighbor (KNN) and ensemble learning algorithms, for providing a comparative study and evaluating the potential of utilizing machine learning to accurately identify between different tremor types.QC 20220620Part of proceedings: ISBN 978-1-7281-5742-9</p

The Smart-Insole Dataset: Gait Analysis Using Wearable Sensors with a Focus on Elderly and Parkinson’s Patients

Gait analysis is crucial for the detection and management of various neurological and musculoskeletal disorders. The identification of gait events is valuable for enhancing gait analysis, developing accurate monitoring systems, and evaluating treatments for pathological gait. The aim of this work is to introduce the Smart-Insole Dataset to be used for the development and evaluation of computational methods focusing on gait analysis. Towards this objective, temporal and spatial characteristics of gait have been estimated as the first insight of pathology. The Smart-Insole dataset includes data derived from pressure sensor insoles, while 29 participants (healthy adults, elderly, Parkinson’s disease patients) performed two different sets of tests: The Walk Straight and Turn test, and a modified version of the Timed Up and Go test. A neurologist specialized in movement disorders evaluated the performance of the participants by rating four items of the MDS-Unified Parkinson’s Disease Rating Scale. The annotation of the dataset was performed by a team of experienced computer scientists, manually and using a gait event detection algorithm. The results evidence the discrimination between the different groups, and the verification of established assumptions regarding gait characteristics of the elderly and patients suffering from Parkinson’s disease

Evaluating Gait Impairment in Parkinson’s Disease from Instrumented Insole and IMU Sensor Data

Parkinson’s disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients’ mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment

Can Gait Features Help in Differentiating Parkinson&rsquo;s Disease Medication States and Severity Levels? A Machine Learning Approach

Parkinson&rsquo;s disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society&mdash;Unified Parkinson&rsquo;s Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively