Automated Assessment of Motor Impairments in Parkinson’s Disease (original) (raw)
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A Self-Managed System for Automated Assessment of UPDRS Upper Limb Tasks in Parkinson's Disease
Sensors (Basel, Switzerland), 2018
A home-based, reliable, objective and automated assessment of motor performance of patients affected by Parkinson's Disease (PD) is important in disease management, both to monitor therapy efficacy and to reduce costs and discomforts. In this context, we have developed a self-managed system for the automated assessment of the PD upper limb motor tasks as specified by the Unified Parkinson's Disease Rating Scale (UPDRS). The system is built around a Human Computer Interface (HCI) based on an optical RGB-Depth device and a replicable software. The HCI accuracy and reliability of the hand tracking compares favorably against consumer hand tracking devices as verified by an optoelectronic system as reference. The interface allows gestural interactions with visual feedback, providing a system management suitable for motor impaired users. The system software characterizes hand movements by kinematic parameters of their trajectories. The correlation between selected parameters and c...
Automatic assessment of Parkinson's Disease from natural hands movements using 3D depth sensor
2014 IEEE 28th Convention of Electrical & Electronics Engineers in Israel (IEEEI), 2014
Parkinson's Disease (PD) is a degenerative disease of the central nervous system with a profound effect on the motor system. Symptoms include slowness of movement, rigidity of motion and in some patients, tremor. The severity of the disease is quantified using the Unified Parkinson Disease Rating Scale (UPDRS) which is a subjective scale performed and scored by physicians. In this work, we present an automated, objective quantitative analysis of four UPDRS motor examinations of Hand Movement and Finger Taps.
Sensors
A self-managed, home-based system for the automated assessment of a selected set of Parkinson’s disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation of posture and motor tasks, which are specified according to the Unified Parkinson’s Disease Rating Scale (UPDRS). Posture, lower limb movements and postural instability are characterized by kinematic parameters of the patient movement. During an experimental campaign, the performances of patients affected by Parkinson’s disease were simultaneously scored by neurologists and analyzed by the system. The sets of parameters which best correlated with the UPDRS scores of subjects’ performances were then used to train supervised classifiers for the automated assessment of new instances of the tasks. Results on the system usability and the assessment accuracy, as compared to clinical evaluations, ind...
IEEE access, 2024
P ARKINSON'S disease (PD) is a neurological disorder caused by degeneration of dopaminergic neurons in the midbrain. PD patients mainly suffer from motor symptoms, which significantly impact their daily lives. The diagnostic criteria for PD include the presence of muscle rigidity, tremor, and postural reflex disturbances. The Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the standard tool for evaluating PD symptoms, part III of which is dedicated to motor symptoms. That part involves a comprehensive set of specific physical examinations, and physicians assign semi quantitative scores from 0 to 4. However, this approach faces notable challenges, including the requirement for movement-disorder experts proficient in using MDS-UPDRS and the presence of substantial inter rater variability even among experts. Overcoming these challenges requires a quantitative and objective assessment method. Given that the rating of motor symptoms predominantly involves assessing kinematic characteristics, the integration of sensor-based devices and machine learning techniques holds the potential to outperform human experts in symptom evaluations. This study used the Leap Motion optical motioncapture device to quantitatively measure and analyze hand movements while 45 PD patients performed the following 3 tasks from the MDS-UPDRS part III: finger tapping (FT), hand opening and closing (OC), and forearm pronation and supination (PS). Data from these tasks were collected and processed, resulting in the extraction of 31 movement patterns for each task. Additionally, 69 statistical features were extracted from each movement pattern, yielding 2139 features for each task. We subsequently employed a random forest algorithm to select the top 15% of features based on the reduction of Gini impurity. These selected features were subsequently fed into a sequential-forward-floating-selection algorithm, combined with a support vector machine, to identify relevant feature combinations and predict the severity of the motor symptoms. The classification accuracy was 87.0% for FT, 93.2% for OC, and 92.2% for PS. One-way analysis of variance identified 13 features of the OC task that were significantly more discriminative for classifying the movement disability of PD patients (p<0.05). This study highlights the effectiveness of combining sensorbased measurements with machine learning for symptom assessment, which demonstrated performance comparable to that of expert physicians. Implementing these findings in clinical settings holds the promise of applying objective and quantitative methods for evaluating the symptoms of PD and related disorders.
Sensors
Objective assessment of the motor evaluation test for Parkinson’s disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system composed of four inertial devices (two SensHand and two SensFoot), and related processing algorithms for extracting parameters from limbs motion was tested on 40 healthy subjects and 40 PD patients. Seventy-eight and 96 kinematic parameters were measured from lower and upper limbs, respectively. Statistical and correlation analysis allowed to define four datasets that were used to train and test five supervised learning classifiers. Excellent discrimination between the two groups was obtained with all the classifiers (average accuracy ranging from 0.936 to 0.960) and all the datasets (average accuracy ranging from 0.953 to 0.966), over three conditions that included parameters derived from lower, upper or...
humansensing.cs.cmu.edu
Recent advancements in the portability and affordability of optical motion capture systems have opened the doors to various clinical applications. In this paper, we look into the potential use of motion capture data for the quantitative analysis of motor symptoms in Parkinson's Disease (PD). The standard of care, human observer-based assessments of the motor symptoms, can be very subjective and are often inadequate for tracking mild symptoms. Motion capture systems, on the other hand, can potentially provide more objective and quantitative assessments. In this pilot study, we perform full-body motion capture of Parkinson's patients with deep brain stimulator off-drugs and with stimulators on and off. Our experimental results indicate that the quantitative measure on spatio-temporal statistics learnt from the motion capture data reveal distinctive differences between mild and severe symptoms. We used a Support Vector Machine (SVM) classifier for discriminating mild vs. severe symptoms with an average accuracy of approximately 90%. Finally, we conclude that motion capture technology could potentially be an accurate, reliable and effective tool for statistical data mining on motor symptoms related to PD. This would enable us to devise more effective ways to track the progression of neurodegenerative movement disorders.
Feasibility of home-based automated Parkinson's disease motor assessment
Journal of Neuroscience Methods, 2012
Patients with Parkinson's disease (PD) receive therapies aimed at addressing a diverse range of motor symptoms. Motor complications in the form of symptom fluctuations and dyskinesias that commonly occur with chronic PD medication use may not be effectively captured by Unified Parkinson's Disease Rating Scale (UPDRS) assessments performed in the clinic. Therefore, home monitoring may be a viable adjunct tool to provide insight into PD motor symptom response to treatment. In this pilot study, we sought to evaluate the feasibility of capturing PD motor symptoms at home using a computer-based assessment system. Ten subjects diagnosed with idiopathic PD used the system at home and ten non-PD control subjects used the system in a laboratory. The Kinesia system consists of a wireless finger-worn motion sensor and a laptop computer with software for automated tremor and bradykinesia severity score assessments. Data from control subjects were used to develop compliance algorithms for rejecting motor tasks performed incorrectly. These algorithms were then applied to data collected from the PD subjects who used the Kinesia system at home to complete motor exams 3-6 times per day over 3-6 days. Motor tasks not rejected by the compliance algorithms were further processed for symptom severity. PD subjects successfully completed motor assessments at home, with approximately 97% of all motor task data files (1222/1260) accepted. These findings suggest that objective home monitoring of PD motor fluctuations is feasible.
At-home assessment of postural stability in parkinson’s disease: a vision-based approach
Journal of Ambient Intelligence and Humanized Computing
Postural instability is one of the most disabling symptoms of Parkinson’s Disease, with important impacts on people safety and quality of life since it increases the risk of falls and injuries. Home monitoring of changes in postural stability, as a consequence of therapies and disease progression, is highly desirable for the safety of the patient and better disease management. In this context, we present a system for the automatic evaluation of postural stability that is suitable for self-managing by people with motor impairment directly at home. The system is based on an optical RGB-Depth device, which tracks the body movements both for system’s interaction, thanks to a gesture-based human-machine interface, and the automated assessment of postural stability. A set of tasks, based on standard clinical scales, has been designed for the assessment. The user controls the delivery of the tasks through the system interface. A machine learning approach is adopted, and some kinematic para...
High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity
Movement Disorders, 2013
Parkinson's disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paperbased measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity. The purpose of this study was to advance the current state of the art by (1) introducing hybrid sensor technology to concurrently acquire surface electromyographic (sEMG) and accelerometer data during unconstrained activity and (2) analyzing the data using dynamic neural network algorithms to capture the evolving temporal characteristics of the sensor data and improve motor disorder recognition of tremor and dyskinesia. Algorithms were trained (n 5 11 patients) and tested (n 5 8 patients; n 5 4 controls) to recognize tremor and dyskinesia at 1-second resolution based on sensor data features and expert annotation of video recording during 4-hour monitoring periods of unconstrained daily activity. The algorithms were able to make accurate distinctions between tremor, dyskinesia, and normal movement despite the presence of diverse voluntary activity. Motor disorder severity classifications averaged 94.9% sensitivity and 97.1% specificity based on 1 sensor per symptomatic limb. These initial findings indicate that new sensor technology and software algorithms can be effective in enhancing wearable sensor-based system performance for monitoring PD motor disorders during unconstrained activities. V