Upper limb motor pre-clinical assessment in Parkinson's disease using machine learning

Abstract Introduction Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. For example, idiopathic hyposmia (IH), which is a reduced olfactory sensitivity, is typical in >95% of PD patients and is a preclinical marker for the pathology. Methods In this work, a wearable inertial device, named SensHand V1, was used to acquire motion data from the upper limbs during the performance of six tasks selected by MDS-UPDRS III. Three groups of people were enrolled, including 30 healthy subjects, 30 IH people, and 30 PD patients. Forty-eight parameters per side were computed by spatiotemporal and frequency data analysis. A feature array was selected as the most significant to discriminate among the different classes both in two-group and three-group classification. Multiple analyses were performed comparing three supervised learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes, on three different datasets. Results Excellent results were obtained for healthy vs. patients classification (F-Measure 0.95 for RF and 0.97 for SVM), and good results were achieved by including subjects with hyposmia as a separate group (0.79 accuracy, 0.80 precision with RF) within a three-group classification. Overall, RF classifiers were the best approach for this application. Conclusion The system is suitable to support an objective PD diagnosis. Further, combining motion analysis with a validated olfactory screening test, a two-step non-invasive, low-cost procedure can be defined to appropriately analyze people at risk for PD development, helping clinicians to identify also subtle changes in motor performance that characterize PD onset

Biomechanical parameter assessment for classification of Parkinson's disease on clinical scale

The primary goal of this study was to investigate computerized assessment methods to classify motor dysfunctioning of patients with Parkinsonâ\u80\u99s disease on the clinical scale. In this proposed system, machine learningâ\u80\u93based computerized assessment methods were introduced to assess the motor performance of patients with Parkinsonâ\u80\u99s disease. Biomechanical parameters were acquired from six exercises through wearable inertial sensors: SensFoot V2 and SensHand V1. All patients were evaluated via neurologist by means of the clinical scale. The average rating was calculated from all exercise ratings given by clinicians to estimate overall rating for each patient. Patients were divided in two groups: slightâ\u80\u93mild patients with Parkinsonâ\u80\u99s disease and moderateâ\u80\u93severe patients with Parkinsonâ\u80\u99s disease according to average rating (â\u80\u9c0: slight and mildâ\u80\u9d and â\u80\u9c1: moderate and severeâ\u80\u9d). Feature selection methods were used for the selection of significant features. Selected features were trained in support vector machine, logistic regression, and neural network to classify the two groups of patients. The highest classification accuracy obtained by support vector machine classifier was 79.66%, with 0.8790 area under the curve. A 76.2% classification accuracy was obtained with 0.7832 area under the curve through logistic regression. A 83.10% classification accuracy was obtained by neural network classifier, with 0.889 area under the curve. Strong distinguishability of the models between the two groups directs the high possibility of motor impairment classification through biomechanical parameters in patients with Parkinsonâ\u80\u99s disease based on the clinical scale

Ambient Assisted Living and Ageing: Preliminary Results of RITA Project

The ageing of population is a social phenomenon that most of worldwide countries are facing. They are, and will be even more in the future, indeed trying to find solutions for improving quality of life of their elderly citizens. The project RITA wants to demonstrate that an update of the current socio-medical services with an Ambient Assisted Living (AAL) approach could improve the service efficiency and the quality of life of both elderly and caregiver. This paper presents the preliminary results obtained in RITA

Hands-feet wireless devices: Test-retest reliability and discriminant validity of motor measures in Parkinson's disease telemonitoring

Background Telemonitoring, a branch of telemedicine, involves the use of technological tools to remotely detect clinical data and evaluate patients. Telemonitoring of patients with Parkinson's disease (PD) should be performed using reliable and discriminant motor measures. Furthermore, the method of data collection and transmission, and the type of subjects suitable for telemonitoring must be well defined. Objective To analyze differences in patients with PD and healthy controls (HC) with the wearable inertial device SensHands-SensFeet (SH-SF), adopting a standardized acquisition mode, to verify if motor measures provided by SH-SF have a high discriminating capacity and high intraclass correlation coefficient (ICC). Methods Altogether, 64 patients with mild-to-moderate PD and 50 HC performed 14 standardized motor activities for assessing bradykinesia, postural and resting tremors, and gait parameters. SH-SF inertial devices were used to acquire movements and calculate objective motor measures of movement (total: 75). For each motor task, five or more biomechanical parameters were measured twice. The results were compared between patients with PD and HC. Results Fifty-eight objective motor measures significantly differed between patients with PD and HC; among these, 32 demonstrated relevant discrimination power (Cohen's d > 0.8). The test-retest reliability was excellent in patients with PD (median ICC = 0.85 right limbs, 0.91 left limbs) and HC (median ICC = 0.78 right limbs, 0.82 left limbs). Conclusion In a supervised environment, the SH-SF device provides motor measures with good results in terms of reliability and discriminant ability. The reliability of SH-SF measurements should be evaluated in an unsupervised home setting in future studies

A wearable system for stress detection through physiological data analysis

In the last years the impact of stress on the society has been increased, resulting in 77% of people that regularly experiences physical symptoms caused by stress with a negative impact on their personal and professional life, especially in aging working population. This paper aims to demonstrate the feasibility of detection and monitoring of stress, inducted by mental stress tests, through the analysis of physiological data collected by wearable sensors. In fact, the physiological features extracted from heart rate variability and galvanic skin response showed significant differences between stressed and not stressed people. Starting from the physiological data, the work provides also a cluster analysis based on Principal Components (PCs) able to showed a visual discrimination of stressed and relaxed groups. The developed system would support active ageing, monitoring and managing the level of stress in ageing workers and allowing them to reduce the burden of stress related to the workload on the basis of personalized interventions

The VISTA datasets, a combination of inertial sensors and depth cameras data for activity recognition

This paper makes the VISTA database, composed of inertial and visual data, publicly available for gesture and activity recognition. The inertial data were acquired with the SensHand, which can capture the movement of wrist, thumb, index and middle fingers, while the RGB-D visual data were acquired simultaneously from two different points of view, front and side. The VISTA database was acquired in two experimental phases: in the former, the participants have been asked to perform 10 different actions; in the latter, they had to execute five scenes of daily living, which corresponded to a combination of the actions of the selected actions. In both phase, Pepper interacted with participants. The two camera point of views mimic the different point of view of pepper. Overall, the dataset includes 7682 action instances for the training phase and 3361 action instances for the testing phase. It can be seen as a framework for future studies on artificial intelligence techniques for activity recognition, including inertial-only data, visual-only data, or a sensor fusion approach

Daily gesture recognition during human-robot interaction combining vision and wearable systems

The recognition of human gestures is crucial for improving the quality of human-robot cooperation. This article presents a system composed of a Pepper robot that mounts an RGB-D camera and an inertial device called SensHand. The system acquired data from twenty people who performed five daily living activities (i.e. Having Lunch, Personal Hygiene, Working, House Cleaning, Relax). The activities were composed of at least two "basic" gestures for a total of 10 gestures. The data acquisition was performed by two cameras positioned laterally and frontally to mimic the real conditions. The acquired data were off-line classified considering different combinations of sensors to evaluate how the sensor fusion approach improves the recognition abilities. Specifically, the article presents an experimental study that evaluated four algorithms often used in computer vision, i.e. three classical machine learning and one belonging to the field of deep learning, namely Support Vector Machine, Random Forest, K-Nearest Neighbor and Long Short-Term Memory Recurrent Neural Network. The comparative analysis of the results shows a significant improvement of the accuracy when fusing camera and sensors data, i.e. 0.81 for the whole system configuration when the robot is in a frontal position with respect to the user (0.79 if we consider only the index finger sensors) and equal to 0.75 when the robot is in a lateral position. Interestingly, the system performs well in recognising the transitions between gestures when these are presented one after the other, a common event in the real-life that was often neglected in the previous studies

Studio e sviluppo di un sistema magnetico per il posizionamento di dispositivi nel trattamento mini-invasivo della fibrillazione atriale

Scopo del presente lavoro di tesi è lo studio e lo sviluppo di un dispositivo medico da utilizzare in fase operatoria per la cura della fibrillazione atriale, un’aritmia cardiaca di grande incidenza clinica, caratterizzata da circuiti elettrici caotici a livello atriale che rendono irregolare il battito cardiaco. Lo studio si basa su un’idea del Professor Claudio Muneretto, Direttore del Reparto di Cardiochirurgia UDA presso la struttura ospedaliera “Spedali Civili” di Brescia. Il dispositivo che si vuol realizzare prende spunto da dispositivi commerciali già esistenti della ditta statunitense Estech (COBRA Adhere XL e COBRA Bipolar Clamps and Inserts) dotati di elettrodi per sviluppare lesioni tessutali per la cura della fibrillazione atriale, e integra su di essi una componente magnetica al fine di permetterne l’utilizzo in operazioni di chirurgia mini-invasiva in assenza di radiazioni ionizzanti (raggi x). Lo strumento deve permettere l’ablazione a radiofrequenza dei tessuti cardiaci coinvolti nella formazione e nel sostentamento degli impulsi responsabili della fibrillazione atriale. Il dispositivo è composto di due cateteri inseriti tramite opportuni trocar nel torace del paziente durante un intervento di chirurgia mininvasiva. Il primo catetere, denominato esterno, è posizionato sotto guida endoscopica attorno all’atrio. Il secondo, denominato interno, è introdotto tramite trocar dentro la parete atriale e posizionato in corrispondenza dell’esterno. Il chirurgo, mediante l’ausilio di un toracoscopio, inserito tramite un terzo trocar nel petto del paziente, colloca il catetere esterno in modo adeguato attorno all’atrio in corrispondenza delle zone cardiache da ablare. Successivamente viene inserito il catetere interno nell’atrio tramite taglio nella parete cardiaca. Questo catetere, grazie alla presenza di un filo metallico che ne coadiuva l’apertura, e dei magneti che ne permettono il mantenimento della posizione, si colloca simmetricamente al componente esterno rispetto alla parete atriale. Si ottiene dunque un dispositivo bipolare in cui gli elettrodi possono effettuare ablazioni trans murali del tessuto cardiaco, così da interrompere i circuiti elettrici disordinati responsabili della fibrillazione atriale e ripristinare il normale ritmo sinusale. Il presente lavoro di tesi si è concentrato sullo sviluppo meccanico del dispositivo, dalla valutazione di varie configurazioni fino alla realizzazione di un prototipo, con la scelta dei magneti e lo studio delle interazioni magnetiche, il dimensionamento dei componenti e la progettazione del design ottimale. La componente elettronica per l’ablazione sarà integrata successivamente grazie all’ intervento della ditta produttrice di elettrodi

How wearable sensors can support parkinson's disease diagnosis and treatment: A systematic review

Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. Objectives: This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). Data sources: The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. Study eligibility criteria: Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. Results: Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease

Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review

Parkinson's disease is a common neurodegenerative pathology that significantly influences quality of life (QoL) of people affected. The increasing interest and development in telemedicine services and internet of things technologies aim to implement automated smart systems for remote assistance of patients. The wide variability of Parkinson's disease in the clinical expression, as well as in the symptom progression, seems to address the patients' care toward a personalized therapy