A Wavelet-Based Approach To Monitoring Parkinson's Disease Symptoms

Parkinson's disease is a neuro-degenerative disorder affecting tens of millions of people worldwide. Lately, there has been considerable interest in systems for at-home monitoring of patients, using wearable devices which contain inertial measurement units. We present a new wavelet-based approach for analysis of data from single wrist-worn smart-watches, and show high detection performance for tremor, bradykinesia, and dyskinesia, which have been the major targets for monitoring in this context. We also discuss the implication of our controlled-experiment results for uncontrolled home monitoring of freely behaving patients.Comment: ICASSP 201

How Does Technology Development Influence the Assessment of Parkinson’s Disease? A Systematic Review

abstract: Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The pathology for PD is difficult and expensive. Furthermore, it depends on patient diaries and the neurologist’s subjective assessment of clinical scales. Objective, accurate, and continuous patient monitoring have become possible with the advancement in mobile and portable equipment. Consequently, a significant amount of work has been done to explore new cost-effective and subjective assessment methods or PD symptoms. For example, smart technologies, such as wearable sensors and optical motion capturing systems, have been used to analyze the symptoms of a PD patient to assess their disease progression and even to detect signs in their nascent stage for early diagnosis of PD. This review focuses on the use of modern equipment for PD applications that were developed in the last decade. Four significant fields of research were identified: Assistance diagnosis, Prognosis or Monitoring of Symptoms and their Severity, Predicting Response to Treatment, and Assistance to Therapy or Rehabilitation. This study reviews the papers published between January 2008 and December 2018 in the following four databases: Pubmed Central, Science Direct, IEEE Xplore and MDPI. After removing unrelated articles, ones published in languages other than English, duplicate entries and other articles that did not fulfill the selection criteria, 778 papers were manually investigated and included in this review. A general overview of PD applications, devices used and aspects monitored for PD management is provided in this systematic review.Dissertation/ThesisMasters Thesis Computer Engineering 201

Unsupervised Parkinson’s Disease Assessment

Parkinson’s Disease (PD) is a progressive neurological disease that affects 6.2 million people worldwide. The most popular clinical method to measure PD tremor severity is a standardized test called the Unified Parkinson’s Disease Rating Scale (UPDRS), which is performed subjectively by a medical professional. Due to infrequent checkups and human error introduced into the process, treatment is not optimally adjusted for PD patients. According to a recent review there are two devices recommended to objectively quantify PD symptom severity. Both devices record a patient’s tremors using inertial measurement units (IMUs). One is not currently available for over the counter purchases, as they are currently undergoing clinical trials. It has also been used in studies to evaluate to UPDRS scoring in home environments using an Android application to drive the tests. The other is an accessible product used by researchers to design home monitoring systems for PD tremors at home. Unfortunately, this product includes only the sensor and requires technical expertise and resources to set up the system. In this paper, we propose a low-cost and energy-efficient hybrid system that monitors a patient’s daily actions to quantify hand and finger tremors based on relevant UPDRS tests using IMUs and surface Electromyography (sEMG). This device can operate in a home or hospital environment and reduces the cost of evaluating UPDRS scores from both patient and the clinician’s perspectives. The system consists of a wearable device that collects data and wirelessly communicates with a local server that performs data analysis. The system does not require any choreographed actions so that there is no need for the user to follow any unwieldy peripheral. In order to avoid frequent battery replacement, we employ a very low-power wireless technology and optimize the software for energy efficiency. Each collected signal is filtered for motion classification, where the system determines what analysis methods best fit with each period of signals. The corresponding UPDRS algorithms are then used to analyze the signals and give a score to the patient. We explore six different machine learning algorithms to classify a patient’s actions into appropriate UPDRS tests. To verify the platform’s usability, we conducted several tests. We measured the accuracy of our main sensors by comparing them with a medically approved industry device. The our device and the industry device show similarities in measurements with errors acceptable for the large difference in cost. We tested the lifetime of the device to be 15.16 hours minimum assuming the device is constantly on. Our filters work reliably, demonstrating a high level of similarity to the expected data. Finally, the device is run through and end-to-end sequence, where we demonstrate that the platform can collect data and produce a score estimate for the medical professionals

Inertial sensor based full body 3D kinematics in the differential diagnosis between Parkinson’s Disease and mimics

The differential diagnosis of Parkinson’s Disease (PD) remains challenging with frequent mis and underdiagnosis. DAT-Scan has been a useful technique for assessing the lost integrity of the nigrostriatal pathway in PD and differentiating true parkinsonism from mimics. However, DAT-Scan remains unavailable in most non-specialized clinical centres, making imperative the search for other easy and low-cost solutions. This dissertation aimed to investigate the role of inertial sensors in distinguishing between the denervated and the non-denervated individuals. In this dissertation, we've used Inertial Sensor Based 3D Full Body Kinematics (FBK) and tested if this technique was able to distinguish between patients with changes in the DAT-Scan from those without. This was divided into two parts, being that firstly, a group of individuals was referred by the attending physician for DAT-Scan (123I-FP-CIT SPECT) to be able to compare FBK in those with and without evidence of dopaminergic depletion. Second, it was tested whether FBK could be used as a metric for the severity of dopaminergic depletion. Twenty-one patients participated in this study, being recruited from the Nuclear Medicine Unit in the Champalimaud Clinical Centre (CCC), Lisbon. Within these 21 patients, 10 of them had denervation (mean age, 68.4 ± 7.8 years) and the remaining 11 (mean age, 66.6 ± 7.4 years) did not present denervation. The analysis between the worst uptake ratio features and dimensional features, as well as the asymmetry indexes in the striatum revealed significant differences between denervated and non-denervated individuals. On the contrary, the kinematics did not do it. Overall, based on the collected kinematics data, it was identified that there was not any significant correlation between the kinematics and the DAT-Scan. What means that these kinematics variables were not able to explain the DAT-Scan. On the other hand, it was also checked that the kinematics data were strongly correlated to the motor symptoms (MDS-UPDRS III). This way, it was concluded that the classical biomechanics did not distinguish denervated from non-denervated individuals. Therefore, the kinematics could not give the same answer as the DAT-Scan. In spite of these results it would be relevant to keep researching other methods in order to find out the distinction between the denervation and no denervation in a low-cost way

Parkinson\u27s Symptoms quantification using wearable sensors

Parkinson’s disease (PD) is a common neurodegenerative disorder affecting more than one million people in the United States and seven million people worldwide. Motor symptoms such as tremor, slowness of movements, rigidity, postural instability, and gait impairment are commonly observed in PD patients. Currently, Parkinsonian symptoms are usually assessed in clinical settings, where a patient has to complete some predefined motor tasks. Then a physician assigns a score based on the United Parkinson’s Disease Rating Scale (UPDRS) after observing the motor task. However, this procedure suffers from inter subject variability. Also, patients tend to show fewer symptoms during clinical visit, which leads to false assumption of the disease severity. The objective of this study is to overcome this limitations by building a system using Inertial Measurement Unit (IMU) that can be used at clinics and in home to collect PD symptoms data and build algorithms that can quantify PD symptoms more effectively. Data was acquired from patients seen at movement disorders Clinic at Sanford Health in Fargo, ND. Subjects wore Physilog IMUs and performed tasks for tremor, bradykinesia and gait according to the protocol approved by Sanford IRB. The data was analyzed using modified algorithm that was initially developed using data from normal subjects emulating PD symptoms. For tremor measurement, the study showed that sensor signals collected from the index finger more accurately predict tremor severity compared to signals from a sensor placed on the wrist. For finger tapping, a task measuring bradykinesia, the algorithm could predict with more than 80% accuracy when a set of features were selected to train the prediction model. Regarding gait, three different analysis were done to find the effective parameters indicative of severity of PD. Gait speed measurement algorithm was first developed using treadmill as a reference. Then, it was shown that the features selected could predict PD gait with 85.5% accuracy

Mobile Phone Sensors Can Discern Medication-related Gait Quality Changes in Parkinson\u27s Patients in the Home Environment

Patients with Parkinson\u27s Disease (PD) experience daytime symptom fluctuations, which result in small amplitude, slow and unstable walking during times when medication attenuates. The ability to identify dysfunctional gait patterns throughout the day from raw mobile phone acceleration and gyroscope signals would allow the development of applications to provide real-time interventions to facilitate walking performance by, for example, providing external rhythmic cues. Patients (n = 20, mean Hoehn and Yahr: 2.25) had their ambulatory data recorded and were directly observed twice during one day: once after medication abstention, (OFF) and once approximately 30 min after intake of their medication (ON). Regularized generalized linear models (RGLM), neural networks (NN), and random forest (RF) classification models were individually trained for each participant. Across all subjects, our best performing classifier on average achieved an accuracy of 92.5%. This study demonstrated that smartphone accelerometers and gyroscopes can be used to distinguish between ON versus OFF times, potentially making smartphones useful intervention tools

Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders:A Systematic Review

Neurodegenerative disorders (NDDs) constitute an increasing global burden and can significantly impair an individual’s mobility, physical activity (PA), and independence. Remote monitoring has been difficult without relying on diaries/questionnaires which are more challenging for people with dementia to complete. Wearable global positioning system (GPS) sensors and accelerometers present a cost-effective and noninvasive way to passively monitor mobility and PA. In addition, changes in sensor-derived outcomes (such as walking behaviour, sedentary, and active activity) may serve as potential biomarkers of disease onset, progression, and response to treatment. We performed a systematic search across four databases to identify papers published within the past 5 years, in which wearable GPS or accelerometers were used to monitor mobility or PA in patients with common NDDs (Parkinson’s disease, Alzheimer’s disease, motor neuron diseases/amyotrophic lateral sclerosis, vascular parkinsonism, and vascular dementia). Disease and technology-specific vocabulary were searched singly, and then in combination, identifying 4985 papers. Following deduplication, we screened 3115 papers and retained 28 studies following a full text review. One study used wearable GPS and accelerometers, while 27 studies used solely accelerometers in NDDs. GPS-derived measures had been validated against current gold standard measures in one Parkinson’s cohort, suggesting that the technology may be applicable to other NDDs. In contrast, accelerometers are widely utilised in NDDs and have been operationalised in well-designed clinical trials

Long-term unsupervised mobility assessment in movement disorders

Mobile health technologies (wearable, portable, body-fixed sensors, or domestic-integrated devices) that quantify mobility in unsupervised, daily living environments are emerging as complementary clinical assessments. Data collected in these ecologically valid, patient-relevant settings can overcome limitations of conventional clinical assessments, as they capture fluctuating and rare events. These data could support clinical decision making and could also serve as outcomes in clinical trials. However, studies that directly compared assessments made in unsupervised and supervised (eg, in the laboratory or hospital) settings point to large disparities, even in the same parameters of mobility. These differences appear to be affected by psychological, physiological, cognitive, environmental, and technical factors, and by the types of mobilities and diagnoses assessed. To facilitate the successful adaptation of the unsupervised assessment of mobility into clinical practice and clinical trials, clinicians and researchers should consider these disparities and the multiple factors that contribute to them