Online monitoring of dyskinesia in patients with Parkinson's disease

Parkinson's Disease Patient Monitoring: A Real-Time Tracking and Tremor Detection System Based on Magnetic Measurements

Reliable diagnosis of early-stage Parkinson’s disease is an important task, since it permits the administration of a timely treatment, slowing the progression of the disease. Together with non-motor symptoms, other important signs of disease can be retrieved from the measurement of the movement trajectory and from tremor appearances. To measure these signs, the paper proposes a magnetic tracking system able to collect information about translational and vibrational movements in a spatial cubic domain, using a low-cost, low-power and highly accurate solution. These features allow the usage of the proposed technology to realize a portable monitoring system, that may be operated at home or in general practices, enabling telemedicine and preventing saturation of large neurological centers. Validation is based on three tests: movement trajectory tracking, a rest tremor test and a finger tapping test. These tests are considered in the Unified Parkinson’s Disease Rating Scale and are provided as case studies to prove the system’s capabilities to track and detect tremor frequencies. In the case of the tapping test, a preliminary classification scheme is also proposed to discriminate between healthy and ill patients. No human patients are involved in the tests, and most cases are emulated by means of a robotic arm, suitably driven to perform required tasks. Tapping test results show a classification accuracy of about 93% using a k-NN classification algorithm, while imposed tremor frequencies have been correctly detected by the system in the other two tests.

Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques

Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.

Monitoring Motor Symptoms During Activities of Daily Living in Individuals With Parkinson's Disease

This literature review addressed wearable sensor systems to monitor motor symptoms in individuals with Parkinson's disease (PD) during activities of daily living (ADLs). Specifically, progress in monitoring tremor, freezing of gait, dyskinesia, bradykinesia, and hypokinesia was reviewed. Twenty-seven studies were found that met the criteria of measuring symptoms in a home or home-like setting, with some studies examining multiple motor disorders. Accelerometers, gyroscopes, and electromyography sensors were included, with some studies using more than one type of sensor. Five studies measured tremor, five studies examined bradykinesia or hypokinesia, thirteen studies included devices to measure dyskinesia or motor fluctuations, and ten studies measured akinesia or freezing of gait. Current sensor technology can detect the presence and severity of each of these symptoms; however, most systems require sensors on multiple body parts, which is challenging for remote or ecologically valid observation. Different symptoms are detected by different sensor placement, suggesting that the goal of detecting all symptoms with a reduced set of sensors may not be achievable. For the goal of monitoring motor symptoms during ADLs in a home setting, the measurement system should be simple to use, unobtrusive to the wearer and easy for an individual with PD to put on and take off. Machine learning algorithms such as neural networks appear to be the most promising way to detect symptoms using a small number of sensors. More work should be done validating the systems during unscripted and unconstrained ADLs rather than in scripted motions.

A New Evolutionary Algorithm-Based Home Monitoring Device for Parkinson's Dyskinesia

Parkinson's disease (PD) is a neurodegenerative movement disorder. Although there is no cure, symptomatic treatments are available and can significantly improve quality of life. The motor, or movement, features of PD are caused by reduced production of the neurotransmitter dopamine. Dopamine deficiency is most often treated using dopamine replacement therapy. However, this therapy can itself lead to further motor abnormalities referred to as dyskinesia. Dyskinesia consists of involuntary jerking movements and muscle spasms, which can often be violent. To minimise dyskinesia, it is necessary to accurately titrate the amount of medication given and monitor a patient's movements. In this paper, we describe a new home monitoring device that allows dyskinesia to be measured as a patient goes about their daily activities, providing information that can assist clinicians when making changes to medication regimens. The device uses a predictive model of dyskinesia that was trained by an evolutionary algorithm, and achieves AUC>0.9 when discriminating clinically significant dyskinesia.

Technologies for Assessment of Motor Disorders in Parkinson's Disease: A Review

Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.

Detecting Elementary Arm Movements by Tracking Upper Limb Joint Angles With MARG Sensors

This paper reports an algorithm for the detection of three elementary upper limb movements, i.e., reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis. We employ two MARG sensors, attached at the elbow and wrist, from which the kinematic properties (joint angles, position) of the upper arm and forearm are calculated through data fusion using a quaternion-based gradient-descent method and a two-link model of the upper limb. By studying the kinematic patterns of the three movements on a small dataset, we derive discriminative features that are indicative of each movement; these are then used to formulate the proposed detection algorithm. Our novel approach of employing the joint angles and position to discriminate the three fundamental movements was evaluated in a series of experiments with 22 volunteers who participated in the study: 18 healthy subjects and four stroke survivors. In a controlled experiment, each volunteer was instructed to perform each movement a number of times. This was complimented by a seminaturalistic experiment where the volunteers performed the same movements as subtasks of an activity that emulated the preparation of a cup of tea. In the stroke survivors group, the overall detection accuracy for all three movements was 93.75% and 83.00%, for the controlled and seminaturalistic experiment, respectively. The performance was higher in the healthy group where 96.85% of the tasks in the controlled experiment and 89.69% in the seminaturalistic were detected correctly. Finally, the detection ratio remains close ( ±6%) to the average value, for different task durations further attesting to the algorithms robustness.

Kinematic parameters to evaluate functional performance of sit-to-stand and stand-to-sit transitions using motion sensor devices: a systematic review

Clinicians commonly use questionnaires and tests based on daily life activities to evaluate physical function. However, the outcomes are usually more qualitative than quantitative and subtle differences are not detectable. In this review, we aim to assess the role of body motion sensors in physical performance evaluation, especially for the sit-to-stand and stand-to-sit transitions. In total, 53 full papers and conference abstracts on related topics were included and 16 different parameters related to transition performance were identified as potentially meaningful to explain certain disabilities and impairments. Transition duration is the most used to evaluate chair-related tests in real clinical settings. High-fall-risk fallers and frail subjects presented longer and more variable transition duration. Other kinematic parameters have also been highlighted in the literature as potential means to detect age-related impairments. In particular, vertical linear velocity and trunk tilt range were able to differentiate between different frailty levels. Frequency domain measures such as spectral edge frequency were also higher for elderly fallers. Lastly, approximate entropy values were larger for subjects with Parkinson's disease and were significantly reduced after treatment. This information could help clinicians in their evaluations as well as in prescribing a physical fitness program to correct a specific deficit.

Smart health monitoring systems: an overview of design and modeling

Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.

An automated methodology for levodopa-induced dyskinesia: assessment based on gyroscope and accelerometer signals

OBJECTIVE

In this study, a methodology is presented for an automated levodopa-induced dyskinesia (LID) assessment in patients suffering from Parkinson's disease (PD) under real-life conditions.

METHODS AND MATERIAL

The methodology is based on the analysis of signals recorded from several accelerometers and gyroscopes, which are placed on the subjects' body while they were performing a series of standardised motor tasks as well as voluntary movements. Sixteen subjects were enrolled in the study. The recordings were analysed in order to extract several features and, based on these features, a classification technique was used for LID assessment, i.e. detection of LID symptoms and classification of their severity.

RESULTS

The results were compared with the clinical annotation of the signals, provided by two expert neurologists. The analysis was performed related to the number and topology of sensors used; several different experimental settings were evaluated while a 10-fold stratified cross validation technique was employed in all cases. Moreover, several different classification techniques were examined. The ability of the methodology to be generalised was also evaluated using leave-one-patient-out cross validation. The sensitivity and positive predictive values (average for all LID severities) were 80.35% and 76.84%, respectively.

CONCLUSIONS

The proposed methodology can be applied in real-life conditions since it can perform LID assessment in recordings which include various PD symptoms (such as tremor, dyskinesia and freezing of gait) of several motor tasks and random voluntary movements.

A web application for follow-up of results from a mobile device test battery for Parkinson's disease patients

This paper describes a web-based system for enabling remote monitoring of patients with Parkinson's disease (PD) and supporting clinicians in treating their patients. The system consists of a patient node for subjective and objective data collection based on a handheld computer, a service node for data storage and processing, and a web application for data presentation. Using statistical and machine learning methods, time series of raw data are summarized into scores for conceptual symptom dimensions and an "overall test score" providing a comprehensive profile of patient's health during a test period of about one week. The handheld unit was used quarterly or biannually by 65 patients with advanced PD for up to four years at nine clinics in Sweden. The IBM Computer System Usability Questionnaire was administered to assess nurses' satisfaction with the web application. Results showed that a majority of the nurses were quite satisfied with the usability although a sizeable minority were not. Our findings support that this system can become an efficient tool to easily access relevant symptom information from the home environment of PD patients.

Inertia sensor-based guidance system for upperlimb posture correction

Stroke rehabilitation is labor-intensive and time-consuming. To assist patients and therapists alike, we propose a wearable system that measures orientation and corrects arm posture using vibrotactile actuators. The system evaluates user posture with respect to a reference and gives feedback in the form of vibration patterns. Users correct their arm posture, one DOF at a time, by following a protocol starting from the shoulder up to the forearm. Five users evaluated the proposed system by replicating ten different postures. Experimental results demonstrated system robustness and showed that some postures were easier to mimic depending on their naturalness.

Computer-based assessment of movement difficulties in Parkinson's disease

The prevalence of Parkinson's disease (PD) is increasing due to an ageing population. It is an unpredictable disease which requires regular assessment and monitoring. Current techniques used to assess PD are subjective. Clinicians observe movements made by a patient and subsequently rate the level of severity of, for example tremor or slowness of movement. Within this work, we have developed and evaluated a prototype computer-based assessment tool capable of collecting information on the movement difficulties present in PD. Twenty participants took part in an assessment of the tool, 10 of whom were diagnosed with PD and 10 were without the disease. Following the usage of the tool, it was found that there was a significant difference (p = 0.038) in the speed of movement between the two groups. We envisage that this tool could have the potential to enable more objective clinical conclusions to be made.

Feature selection for accelerometer-based posture analysis in Parkinson's disease

Posture analysis in quiet standing is a key component of the clinical evaluation of Parkinson's disease (PD), postural instability being one of PD's major symptoms. The aim of this study was to assess the feasibility of using accelerometers to characterize the postural behavior of early mild PD subjects. Twenty PD and 20 control subjects, wearing an accelerometer on the lower back, were tested in five conditions characterized by sensory and attentional perturbation. A total of 175 measures were computed from the signals to quantify tremor, acceleration, and displacement of body sway. Feature selection was implemented to identify the subsets of measures that better characterize the distinctive behavior of PD and control subjects. It was based on different classifiers and on a nested cross validation, to maximize robustness of selection with respect to changes in the training set. Several subsets of three features achieved misclassification rates as low as 5%. Many of them included a tremor-related measure, a postural measure in the frequency domain, and a postural displacement measure. Results suggest that quantitative posture analysis using a single accelerometer and a simple test protocol may provide useful information to characterize early PD subjects. This protocol is potentially usable to monitor the disease's progression.

On automated assessment of Levodopa-induced dyskinesia in Parkinson's disease

A method for the analysis of accelerometer and gyroscope signals in order to automatically assess the Levodopa-induced dyskinesia (LID) in patients with Parkinson's disease is presented in this paper. Several accelerometers and gyroscopes are placed on certain positions on the subject's body and the obtained signals are analyzed in order to extract several features that depict the energy distribution over the frequency spectrum and the non-linear properties of the signal. These features are fed into an artificial neural network which is used for LID detection and severity classification. The method has been evaluated using a group of 29 subjects. Results are presented related to the body locations that the accelerometers and the gyroscopes are placed. The obtained results indicate high classification ability (84.3% average classification accuracy).

Automated Levodopa-induced dyskinesia assessment

An automated methodology for Levodopa-induced dyskinesia (LID) assessment is presented in this paper. The methodology is based on the analysis of the signals recorded from accelerometers and gyroscopes, which are placed on certain positions on the subject's body. The obtained signals are analyzed and several features are extracted. Based on these features a classification technique is used for LID detection and classification of its severity. The method has been evaluated using a group of 10 subjects. Results are presented related to each individual sensor as well as for various sensor combinations. The obtained results indicate high classification ability (93.73% classification accuracy).

Joint angle tracking with inertial sensors

Many wearable inertial systems have been used to continuously track human movement in and outside of a laboratory. The number of sensors and the complexity of the algorithms used to measure position and orientation vary according to the clinical application. To calculate changes in orientation, researchers often integrate the angular velocity. However, a relatively small error in measured angular velocity leads to large integration errors. This restricts the time of accurate measurement to a few minutes. We have combined kinematic models designed for control of robotic arms with state space methods to directly and continuously estimate the joint angles from inertial sensors. These algorithms can be applied to any combination of sensors, can easily handle malfunctions or the loss of some sensor inputs, and can be used in either a real-time or an off-line processing mode with higher accuracy.

A wireless system for the objective assessment of dyskinesia

In this paper, we describe a novel wireless system to facilitate the objective assessments of neurological movement disorders like dyskinesia. The ability of the prototype system to provide precise, objective biomechanical data about human motor performance has been demonstrated via controlled tests using a robotic arm. The system is designed to be used in clinical settings and will not require an extensive setup or training. The system may be used to supplement the clinical routine examinations by providing objective performance data to aid diagnosis or to monitor therapeutic success. In addition, it can be a useful tool for research on neurological movement disorders.

The tremor coherence analyzer (TCA): a portable tool to assess instantaneous inter-muscle coupling in tremor

This paper proposes a novel system for pathological tremor study and diagnosis. The system described called TCA (tremor coherence analyzer) is based on a electronic device developed for wireless monitoring of physiological variables. The device uses Bluetooth technology to communicate. The proposed technique for pathological tremor analysis uses surface EMG signals. The EMG sensors are located on forearm muscles to measure muscular activity due to pathological tremor. The coherence function between these signals is calculated. The application of the coherence function allows to determine linear dependencies between two signals.

A multiparameter wearable physiologic monitoring system for space and terrestrial applications

A novel, unobtrusive and wearable, multiparameter ambulatory physiologic monitoring system for space and terrestrial applications, termed LifeGuard, is presented. The core element is a wearable monitor, the crew physiologic observation device (CPOD), that provides the capability to continuously record two standard electrocardiogram leads, respiration rate via impedance plethysmography, heart rate, hemoglobin oxygen saturation, ambient or body temperature, three axes of acceleration, and blood pressure. These parameters can be digitally recorded with high fidelity over a 9-h period with precise time stamps and user-defined event markers. Data can be continuously streamed to a base station using a built-in Bluetooth RF link or stored in 32 MB of on-board flash memory and downloaded to a personal computer using a serial port. The device is powered by two AAA batteries. The design, laboratory, and field testing of the wearable monitors are described.

Advances in wearable technology and applications in physical medicine and rehabilitation

The development of miniature sensors that can be unobtrusively attached to the body or can be part of clothing items, such as sensing elements embedded in the fabric of garments, have opened countless possibilities of monitoring patients in the field over extended periods of time. This is of particular relevance to the practice of physical medicine and rehabilitation. Wearable technology addresses a major question in the management of patients undergoing rehabilitation, i.e. have clinical interventions a significant impact on the real life of patients? Wearable technology allows clinicians to gather data where it matters the most to answer this question, i.e. the home and community settings. Direct observations concerning the impact of clinical interventions on mobility, level of independence, and quality of life can be performed by means of wearable systems. Researchers have focused on three main areas of work to develop tools of clinical interest: 1)the design and implementation of sensors that are minimally obtrusive and reliably record movement or physiological signals, 2)the development of systems that unobtrusively gather data from multiple wearable sensors and deliver this information to clinicians in the way that is most appropriate for each application, and 3)the design and implementation of algorithms to extract clinically relevant information from data recorded using wearable technology. Journal of NeuroEngineering and Rehabilitation has devoted a series of articles to this topic with the objective of offering a description of the state of the art in this research field and pointing to emerging applications that are relevant to the clinical practice in physical medicine and rehabilitation.

The accuracy of measuring the kinematics of rising from a chair with accelerometers and gyroscopes

The purpose of this study was to assess the accuracy of measuring angle and angular velocity of the upper body and upper leg during rising from a chair with accelerometers, using low-pass filtering of the accelerometer signal. Also, the improvement in accuracy of the measurement with additional use of high-pass filtered gyroscopes was assessed. Two uni-axial accelerometers and one gyroscope (DynaPort) per segment were used to measure angles and angular velocities of upper body and upper leg. Calculated angles and angular velocities were compared to a high-quality optical motion analysis system (Optotrak), using root mean squared error (RMS) and correlation coefficient (r) as parameters. The results for the sensors showed that two uni-axial accelerometers give a reasonable accurate measurement of the kinematics of rising from a chair (RMS = 2.9, 3.5, and 2.6 degrees for angle and RMS = 9.4, 18.4, and 11.5 degrees /s for angular velocity for thorax, pelvis, and upper leg, respectively). Additional use of gyroscopes improved the accuracy significantly (RMS = 0.8, 1.1, and 1.7 degrees for angle and RMS = 2.6, 4.0 and 4.9 degrees /s for angular velocity for thorax, pelvis and upper leg, respectively). The low-pass Butterworth filter had optimal cut-off frequencies of 1.05, 1.3, and 1.05 for thorax, pelvis, and upper leg, respectively. For the combined signal, the optimal cut-off frequencies were 0.18, 0.2, and 0,38 for thorax, pelvis and upper leg, respectively. The filters showed no subject specificity. This study provides an accurate, inexpensive and simple method to measure the kinematics of movements similar to rising from a chair.