scholarly journals Remote Parkinson's disease monitoring system: from smartphone to cloud platform

2019 ◽  
pp. 21-25
Author(s):  
О.В. Бережний ◽  
T.O. Білобородова ◽  
І.С. Скарга-Бандурова
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Monitoring System ◽  
Data Transmission ◽  
Cloud Storage ◽  
Smartphone Application ◽  
Sensor Data ◽  
Common Symptom ◽  
Cloud Platform ◽  
Smartphone Sensors

Parkinson's disease (PD) is a slowly progressive disorder that affects movement, muscle control, and balance. The earlier treatment can prevent the disease from developing and to prolongate the diseases prodromal phase. In this context, home monitoring services are potentially powerful tools for remote diagnosis and can improve healthcare services. Tremor is the most common symptom of a PD disorder and it has several advantages for continuous PD symptoms monitoring. The developing of solution based on smartphone sensors that allow remote monitoring of the monitored user is present. The connection between the smartphone application and cloud platform for smartphone sensors data transmission for early tremor symptoms detection is developed. It includesdeveloping of configuration of smartphone application for sensor data transmission and developing of configuration of a cloud platform for tremor symptoms monitoring. The active tests were developed to capture a motor disorder, that indicates PD symptom such as tremor.  Initial trials of the developing demonstrated that the monitoring system has the ability to real-time data acquisition and transmission using smartphone sensors and cloud storage. The connection settings developed for the system proved to be efficient when sensor data transmitted from the smartphone to cloud storage. The period of time required to transfer data to the cloud equal to the period of time less than one second.

scholarly journals Monitoring Parkinson’s disease symptoms during daily life: a feasibility study

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Margot Heijmans ◽  
Jeroen G. V. Habets ◽  
Christian Herff ◽  
Jos Aarts ◽  
An Stevens ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Monitoring System ◽  
Daily Life ◽  
Wearable Sensors ◽  
Ground Truth ◽  
Sensor Data ◽  
Disease Monitoring ◽  
Subjective Character ◽  
Disease Symptoms

Abstract Parkinson’s disease symptoms are most often charted using the MDS-UPDRS. Limitations of this approach include the subjective character of the assessments and a discrepant performance in the clinic compared to the home situation. Continuous monitoring using wearable devices is believed to eventually replace this golden standard, but measurements often lack a parallel ground truth or are only tested in lab settings. To overcome these limitations, this study explores the feasibility of a newly developed Parkinson’s disease monitoring system, which aims to measure Parkinson’s disease symptoms during daily life by combining wearable sensors with an experience sampling method application. Twenty patients with idiopathic Parkinson’s disease participated in this study. During a period of two consecutive weeks, participants had to wear three wearable sensors and had to complete questionnaires at seven semi-random moments per day on their mobile phone. Wearable sensors collected objective movement data, and the questionnaires containing questions about amongst others Parkinson’s disease symptoms served as parallel ground truth. Results showed that participants wore the wearable sensors during 94% of the instructed timeframe and even beyond. Furthermore, questionnaire completion rates were high (79,1%) and participants evaluated the monitoring system positively. A preliminary analysis showed that sensor data could reliably predict subjectively reported OFF moments. These results show that our Parkinson’s disease monitoring system is a feasible method to use in a diverse Parkinson’s disease population for at least a period of two weeks. For longer use, the monitoring system may be too intense and wearing comfort needs to be optimized.

scholarly journals Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Gloria Vergara-Diaz ◽  
Jean-Francois Daneault ◽  
Federico Parisi ◽  
Chen Admati ◽  
Christina Alfonso ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Symptom Severity ◽  
Wearable Sensors ◽  
Motor Fluctuations ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Motor Tasks ◽  
Specific Symptom ◽  
At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

scholarly journals Predicting Parkinson’s Disease Medication Regimen Using Sensor Technology

2021 ◽  
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Rama Vasudevan ◽  
Fatta B. Nahab ◽  
Ritesh Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Classification Model ◽  
Sensor Data ◽  
Sensor Technology ◽  
Medication Regimen ◽  
Levodopa Dose ◽  
Subjective Data ◽  
Random Forest Classification

Abstract Parkinson’s disease (PD) medication treatment planning is generally based on subjective data through in-office, physicianpatient interactions. The Personal KinetiGraphTM (PKG) has shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to subtype patients based on levodopa regimens and response. We apply k-means clustering to a dataset of with-in-subject Parkinson’s medication changes—clinically assessed by the PKG and Hoehn & Yahr (H&Y) staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective PKG data and demographic information. Clinically relevant clusters were developed based on longitudinal dopaminergic regimens—partitioned by levodopa dose, administration frequency, and total levodopa equivalent daily dose—with the PKG increasing cluster granularity compared to the H&Y staging. A random forest classifier was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 87:9 ±1:3

scholarly journals DailyCog: A Real-World Functional Cognitive Mobile Application for Evaluating Mild Cognitive Impairment (MCI) in Parkinson’s Disease

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1788
Author(s):  
Sara Rosenblum ◽  
Ariella Richardson ◽  
Sonya Meyer ◽  
Tal Nevo ◽  
Maayan Sinai ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Neurodegenerative Disorder ◽  
Smartphone Application ◽  
Self Report ◽  
Substantial Impact ◽  
Patient Functioning ◽  
Visual Spatial

Parkinson’s disease (PD) is the second most common progressive neurodegenerative disorder affecting patient functioning and quality of life. Aside from the motor symptoms of PD, cognitive impairment may occur at early stages of PD and has a substantial impact on patient emotional and physical health. Detecting these early signs through actual daily functioning while the patient is still functionally independent is challenging. We developed DailyCog—a smartphone application for the detection of mild cognitive impairment. DailyCog includes an environment that simulates daily tasks, such as making a drink and shopping, as well as a self-report questionnaire related to daily events performed at home requiring executive functions and visual–spatial abilities, and psychomotor speed. We present the detailed design of DailyCog and discuss various considerations that influenced the design. We tested DailyCog on patients with mild cognitive impairment in PD. Our case study demonstrates how the markers we used coincide with the cognitive levels of the users. We present the outcome of our usability study that found that most users were able to use our app with ease, and provide details on how various features were used, along with some of the difficulties that were identified.

Combined Vision and Wearable Sensors-based System for Movement Analysis in Rehabilitation

2017 ◽  
Vol 56 (02) ◽  
pp. 95-111 ◽  
Author(s):  
Tihomir V. Ilić ◽  
Slađan Milanović ◽  
Veljko Potkonjak ◽  
Aleksandar Rodić ◽  
José Santos-Victor ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Performance Indicators ◽  
Wearable Sensors ◽  
Movement Analysis ◽  
Assessment Tools ◽  
Sensor Data ◽  
Disease Stage ◽  
Movement Performance ◽  
Rehabilitation Exercises

SummaryBackground: Traditional rehabilitation sessions are often a slow, tedious, disempowering and non-motivational process, supported by clinical assessment tools, i.e. evaluation scales that are prone to subjective rating and imprecise interpretation of patient’s performance. Poor patient motivation and insufficient accuracy are thus critical factors that can be improved by new sensing/processing technologies.Objectives: We aim to develop a portable and affordable system, suitable for home rehabilitation, which combines vision-based and wearable sensors. We introduce a novel approach for examining and characterizing the rehabilitation movements, using quantitative descriptors. We propose new Movement Performance Indicators (MPIs) that are extracted directly from sensor data and quantify the symmetry, velocity, and acceleration of the movement of different body/hand parts, and that can potentially be used by therapists for diagnosis and progress assessment.Methods: First, a set of rehabilitation exercises is defined, with the supervision of neurologists and therapists for the specific case of Parkinson’s disease. It comprises full-body movements measured with a Kinect device and fine hand movements, acquired with a data glove. Then, the sensor data is used to compute 25 Movement Performance Indicators, to assist the diagnosis and progress monitoring (assessing the disease stage) in Parkinson’s disease. A kinematic hand model is developed for data verification and as an additional resource for extracting supplementary movement information.Results: Our results show that the proposed Movement Performance Indicators are relevant for the Parkinson’s disease assessment. This is further confirmed by correlation of the proposed indicators with clinical tapping test and UPDRS clinical scale. Classification results showed the potential of these indicators to discriminate between the patients and controls, as well as between the stages that characterize the evolution of the disease.Conclusions: The proposed sensor system, along with the developed approach for rehabilitation movement analysis have a significant potential to support and advance traditional rehabilitation therapy. The main impact of our work is two-fold: (i) the proposition of an approach for supporting the therapists during the diagnosis and monitoring evaluations by reducing subjectivity and imprecision, and (ii) offering the possibility of the system to be used at home for rehabilitation exercises in between sessions with doctors and therapists.

scholarly journals Exploring Bottom-Up Visual Processing and Visual Hallucinations in Parkinson's Disease With Dementia

2021 ◽  
Vol 11 ◽  
Author(s):  
Nicholas Murphy ◽  
Alison Killen ◽  
Rajnish Kumar Gupta ◽  
Sara Graziadio ◽  
Lynn Rochester ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Visual System ◽  
Visual Processing ◽  
Pattern Reversal ◽  
Common Symptom ◽  
Visual Hallucinations ◽  
Bottom Up ◽  
Parkinson’S Disease With Dementia ◽  
System Physiology

Visual hallucinations (VH) are a common symptom of Parkinson's disease with dementia (PDD), affecting up to 65% of cases. Integrative models of their etiology posit that a decline in executive control of the visuo-perceptual system is a primary mechanism of VH generation. The role of bottom-up processing in the manifestation of VH in this condition is still not clear although visual evoked potential (VEP) differences have been associated with VH at an earlier stage of PD. Here we compared the amplitude and latency pattern reversal VEPs in healthy controls (n = 21) and PDD patients (n = 34) with a range of VH severities. PDD patients showed increased N2 latency relative to controls, but no significant differences in VEP measures were found for patients reporting complex VH (CVH) (n = 17) compared to those without VH. Our VEP findings support previous reports of declining visual system physiology in PDD and some evidence of visual system differences between patients with and without VH. However, we did not replicate previous findings of a major relationships between the integrity of the visual pathway and VH.

scholarly journals Subthalamic-Cortical Network Reorganization during Parkinson’s Tremor

2021 ◽  
Author(s):  
Peter M. Lauro ◽  
Shane Lee ◽  
Umer Akbar ◽  
Wael F. Asaad
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Subthalamic Nucleus ◽  
Sensorimotor Cortex ◽  
Motor Task ◽  
Common Symptom ◽  
Time Changes ◽  
First Time ◽  
Intracranial Recordings ◽  
Parkinson's Tremor

ABSTRACTTremor, a common and often primary symptom of Parkinson’s disease, has been modeled with distinct onset and maintenance dynamics. To identify the neurophysiologic correlates of each state, we acquired intraoperative cortical and subthalamic nucleus recordings from ten patients performing a naturalistic visual-motor task. From this task we isolated short epochs of tremor onset and sustained tremor. Comparing these epochs, we found that the subthalamic nucleus was central to tremor onset, as it drove both motor cortical activity and tremor output. Once tremor became sustained, control of tremor shifted to cortex. At the same time, changes in directed functional connectivity across sensorimotor cortex further distinguished the sustained tremor state.SIGNIFICANCE STATEMENTTremor is a common symptom of Parkinson’s disease (PD). While tremor pathophysiology is thought to involve both basal ganglia and cerebello-thalamic-cortical circuits, it is unknown how these structures functionally interact to produce tremor. In this manuscript, we analyzed intracranial recordings from the subthalamic nucleus and sensorimotor cortex in patients with PD undergoing deep brain stimulation (DBS) surgery. Using an intraoperative task, we examined tremor in two separate dynamic contexts: when tremor first emerged, and when tremor was sustained. We believe that these findings reconcile several models of Parkinson’s tremor, while describing the short-timescale dynamics of subcortical-cortical interactions during tremor for the first time. These findings may describe a framework for developing proactive and responsive neurostimulation models for specifically treating tremor.

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