Impact of Exercise Intervention in Parkinson’s Disease can be Quantified Using Inertial Sensor Data and Clinical Tests

2019 ◽  
Author(s):  
Killian McManus ◽  
Denise McGrath ◽  
Barry R. Greene ◽  
Olive Lennon ◽  
Laura McMahon ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Exercise Intervention ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Clinical Tests

scholarly journals Quantitative and qualitative gait assessments in Parkinson’s disease patients

2014 ◽  
Vol 71 (9) ◽  
pp. 809-816 ◽  
Author(s):  
Milica Djuric-Jovicic ◽  
Nenad Jovicic ◽  
Sasa Radovanovic ◽  
Nikola Kresojevic ◽  
Vladimir Kostic ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensor ◽  
Freezing Of Gait ◽  
Assessment System ◽  
Sensor Data ◽  
Postural Adjustment ◽  
Gait Assessment ◽  
Graphical Tool ◽  
Gait Disturbances

Background/Aim. Postural impairments and gait disorders in Parkinson's disease (PD) affect limits of stability, impaire postural adjustment, and evoke poor responses to perturbation. In the later stage of the disease, some patients can suffer from episodic features such as freezing of gait (FOG). Objective gait assessment and monitoring progress of the disease can give clinicians and therapist important information about changes in gait pattern and potential gait deviations, in order to prevent concomitant falls. The aim of this study was to propose a method for identification of freezing episodes and gait disturbances in patients with PD. A wireless inertial sensor system can be used to provide follow-up of the treatment effects or progress of the disease. Methods. The system is simple for mounting a subject, comfortable, simple for installing and recording, reliable and provides high-quality sensor data. A total of 12 patients were recorded and tested. Software calculates various gait parameters that could be estimated. User friendly visual tool provides information about changes in gait characteristics, either in a form of spectrogram or by observing spatiotemporal parameters. Based on these parameters, the algorithm performs classification of strides and identification of FOG types. Results. The described stride classification was merged with an algorithm for stride reconstruction resulting in a useful graphical tool that allows clinicians to inspect and analyze subject?s movements. Conclusion. The described gait assessment system can be used for detection and categorization of gait disturbances by applying rule-based classification based on stride length, stride time, and frequency of the shank segment movements. The method provides an valuable graphical interface which is easy to interpret and provides clinicians and therapists with valuable information regarding the temporal changes in gait.

scholarly journals Predicting Fall Counts Using Wearable Sensors: A Novel Digital Biomarker for Parkinson’s Disease

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 54
Author(s):  
Barry R. Greene ◽  
Isabella Premoli ◽  
Killian McManus ◽  
Denise McGrath ◽  
Brian Caulfield
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Strong Association ◽  
Sensor Data ◽  
Clinical Population ◽  
Timed Up And Go ◽  
Falls Risk ◽  
Serious Injury

People with Parkinson’s disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson’s disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD. However, falls data can be unreliable, expensive and time-consuming to collect. We sought to validate and test a novel digital biomarker for PD that uses wearable sensor data obtained during the Timed Up and Go (TUG) test to predict the number of falls that will be experienced by a person with PD. Three datasets, containing a total of 1057 (671 female) participants, including 71 previously diagnosed with PD, were included in the analysis. Two statistical approaches were considered in predicting falls counts: the first based on a previously reported falls risk assessment algorithm, and the second based on elastic net and ensemble regression models. A predictive model for falls counts in PD showed a mean R2 value of 0.43, mean error of 0.42 and a mean correlation of 30% when the results were averaged across two independent sets of PD data. The results also suggest a strong association between falls counts and a previously reported inertial sensor-based falls risk estimate. In addition, significant associations were observed between falls counts and a number of individual gait and mobility parameters. Our preliminary research suggests that the falls counts predicted from the inertial sensor data obtained during a simple walking task have the potential to be developed as a novel digital biomarker for PD, and this deserves further validation in the targeted clinical population.

Classification of walking patterns in Parkinson's disease patients based on inertial sensor data

2010 ◽  
Author(s):  
Milica Djuric-Jovicic ◽  
Nenad S. Jovicic ◽  
Ivana Milovanovic ◽  
Sasa Radovanovic ◽  
Nikola Kresojevic ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensor ◽  
Sensor Data

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

Three simple clinical tests to accurately predict falls in people with Parkinson's disease

2013 ◽  
Vol 28 (5) ◽  
pp. 655-662 ◽  
Author(s):  
Serene S. Paul ◽  
Colleen G. Canning ◽  
Catherine Sherrington ◽  
Stephen R. Lord ◽  
Jacqueline C. T. Close ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Tests

scholarly journals The Impact of Mind-Body Exercises on Motor Function, Depressive Symptoms, and Quality of Life in Parkinson’s Disease: A Systematic Review and Meta-Analysis

2019 ◽  
Vol 17 (1) ◽  
pp. 31 ◽  
Author(s):  
Xiaohu Jin ◽  
Lin Wang ◽  
Shijie Liu ◽  
Lin Zhu ◽  
Paul Dinneen Loprinzi ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Depressive Symptoms ◽  
Motor Function ◽  
Exercise Intervention ◽  
Meta Analysis ◽  
Cochrane Library ◽  
The Impact

Purpose: To systematically evaluate the effects of mind-body exercises (Tai Chi, Yoga, and Health Qigong) on motor function (UPDRS, Timed-Up-and-Go, Balance), depressive symptoms, and quality of life (QoL) of Parkinson’s patients (PD). Methods: Through computer system search and manual retrieval, PubMed, Web of Science, The Cochrane Library, CNKI, Wanfang Database, and CQVIP were used. Articles were retrieved up to the published date of June 30, 2019. Following the Cochrane Collaboration System Evaluation Manual (version 5.1.0), two researchers independently evaluated the quality and bias risk of each article, including 22 evaluated articles. The Pedro quality score of 6 points or more was found for 86% (19/22) of these studies, of which 21 were randomized controlled trials with a total of 1199 subjects; and the trial intervention time ranged from 4 to 24 weeks. Interventions in the control group included no-intervention controls, placebo, waiting-lists, routine care, and non-sports controls. Meta-analysis was performed on the literature using RevMan 5.3 statistical software, and heterogeneity analysis was performed using Stata 14.0 software. Results: (1) Mind-body exercises significantly improved motor function in PD patients, including UPDRS (SMD = −0.61, p < 0.001), TUG (SMD = −1.47, p < 0.001) and balance function (SMD = 0.79, p < 0.001). (2) Mind-body exercises also had significant effects on depression (SMD = −1.61, p = 0.002) and QoL (SMD = 0.66, p < 0.001). (3) Among the indicators, UPDRS (I2 = 81%) and depression (I2 = 91%) had higher heterogeneity; according to the results of the separate combined effect sizes of TUG (I2 = 29%), Balance (I2 = 16%) and QoL (I2 = 35%), it shows that the heterogeneity is small; (4) After meta-regression analysis of the age limit and other possible confounding factors, further subgroup analysis showed that the reason for the heterogeneity of UPDRS motor function may be related to the sex of PD patients and severity of the disease; the outcome of depression was heterogeneous. The reason for this may be the use of specific drugs in the experiment and the duration of intervention in the trial. Conclusion: (1) Mind-body exercises were found to have significant improvements in motor function, depressive symptoms, and quality of life in patients with Parkinson’s disease, and can be used as an effective method for clinical exercise intervention in PD patients. (2) Future clinical intervention programs for PD patients need to fully consider specific factors such as gender, severity of disease, specific drug use, and intervention cycle to effectively control heterogeneity factors, so that the clinical exercise intervention program for PD patients is objective, scientific, and effective.

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