Combined analysis of sensor data from hand and gait motor function improves automatic recognition of Parkinson's disease

2012 ◽  
Author(s):  
J. Barth ◽  
M. Sunkel ◽  
K. Bergner ◽  
G. Schickhuber ◽  
J. Winkler ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Function ◽  
Automatic Recognition ◽  
Sensor Data ◽  
Combined Analysis

scholarly journals Hybrid Feature Extraction for Detection of Degree of Motor Fluctuation Severity in Parkinson’s Disease Patients

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 137 ◽  
Author(s):  
Murtadha D. Hssayeni ◽  
Joohi Jimenez-Shahed ◽  
Behnaz Ghoraani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Extraction ◽  
Motor Function ◽  
Motor Fluctuation ◽  
Classification Model ◽  
Sensor Data ◽  
Body Movements ◽  
Hybrid Feature Extraction ◽  
Free Body

The success of medication adjustment in Parkinson’s disease (PD) patients with motor fluctuation relies on the knowledge about their fluctuation severity. However, because of the temporal and spatial variability in motor fluctuations, a single clinical examination often fails to capture the spectrum of motor impairment experienced in routine daily life. In this study, we developed an algorithm to estimate the degree of motor fluctuation severity from two wearable sensors’ data during subjects’ free body movements. Specifically, we developed a new hybrid feature extraction method to represent the longitudinal changes of motor function from the sensor data. Next, we developed a classification model based on random forest to learn the changes in the patterns of the sensor data as the severity of the motor function changes. We evaluated our algorithm using data from 24 subjects with idiopathic PD as they performed a variety of daily routine activities. A leave-one-subject-out assessment of the algorithm resulted in 83.33% accuracy, indicating that our approach holds a great promise to passively detect degree of motor fluctuation severity from continuous monitoring of an individual’s free body movements. Such a sensor-based assessment system and algorithm combination could provide the objective and comprehensive information about the fluctuation severity that can be used by the treating physician to effectively adjust therapy for PD patients with troublesome motor fluctuation.

Effects of music-based movement therapy on motor function, balance, gait, mental health, and quality of life for patients with Parkinson’s disease: A systematic review and meta-analysis

2021 ◽  
pp. 026921552199052
Author(s):  
Zonglei Zhou ◽  
Ruzhen Zhou ◽  
Wen Wei ◽  
Rongsheng Luan ◽  
Kunpeng Li
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Function ◽  
Meta Analysis ◽  
Freezing Of Gait ◽  
Movement Therapy ◽  
Walking Velocity

Objective: To conduct a systematic review evaluating the effects of music-based movement therapy on motor function, balance, gait, mental health, and quality of life among individuals with Parkinson’s disease. Data sources: A systematic search of PubMed, Embase, Cochrane Library, Web of Science, PsycINFO, CINAHL, and Physiotherapy Evidence Database was carried out to identify eligible papers published up to December 10, 2020. Review methods: Literature selection, data extraction, and methodological quality assessment were independently performed by two investigators. Publication bias was determined by funnel plot and Egger’s regression test. “Trim and fill” analysis was performed to adjust any potential publication bias. Results: Seventeen studies involving 598 participants were included in this meta-analysis. Music-based movement therapy significantly improved motor function (Unified Parkinson’s Disease Rating Scale motor subscale, MD = −5.44, P = 0.002; Timed Up and Go Test, MD = −1.02, P = 0.001), balance (Berg Balance Scale, MD = 2.02, P < 0.001; Mini-Balance Evaluation Systems Test, MD = 2.95, P = 0.001), freezing of gait (MD = −2.35, P = 0.039), walking velocity (MD = 0.18, P < 0.001), and mental health (SMD = −0.38, P = 0.003). However, no significant effects were observed on gait cadence, stride length, and quality of life. Conclusion: The findings of this study show that music-based movement therapy is an effective treatment approach for improving motor function, balance, freezing of gait, walking velocity, and mental health for patients with Parkinson’s disease.

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 Cannabis and its derivatives for the use of motor symptoms in Parkinson’s disease: a systematic review and meta-analysis

2021 ◽  
Vol 14 ◽  
pp. 175628642110185
Author(s):  
Susan J. Thanabalasingam ◽  
Brandan Ranjith ◽  
Robyn Jackson ◽  
Don Thiwanka Wijeratne
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Function ◽  
Observational Studies ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Motor Symptom ◽  
Quality Data ◽  
Symptom Improvement ◽  
Motor Symptoms

Background: Recent changes to the legal status of cannabis across various countries have renewed interest in exploring its use in Parkinson’s disease (PD). The use of cannabinoids for alleviation of motor symptoms has been extensively explored in pre-clinical studies. Objective: We aim to systematically review and meta-analyze literature on the use of medical cannabis or its derivatives (MC) in PD patients to determine its effect on motor function and its safety profile. Methods: We reviewed and analyzed original, full-text randomized controlled trials (RCTs) and observational studies. Primary outcomes were change in motor function and dyskinesia. Secondary outcomes included adverse events and side effects. All studies were analyzed for risk of bias. Results: Fifteen studies, including six RCTs, were analyzed. Of these, 12/15 (80%) mention concomitant treatment with antiparkinsonian medications, most commonly levodopa. Primary outcomes were most often measured using the Unified Parkinson Disease Rating Scale (UPDRS) among RCTs and patient self-report of symptom improvement was widely used among observational studies. Most of the observational data lacking appropriate controls had effect estimates favoring the intervention. However, the controlled studies demonstrated no significant motor symptom improvement overall. The meta-analysis of three RCTs, including a total of 83 patients, did not demonstrate a statistically significant improvement in UPDRS III score variation (MD −0.21, 95% CI −4.15 to 3.72; p = 0.92) with MC use. Only one study reported statistically significant improvement in dyskinesia ( p < 0.05). The intervention was generally well tolerated. All RCTs had a high risk of bias. Conclusion: Although observational studies establish subjective symptom alleviation and interest in MC among PD patients, there is insufficient evidence to support its integration into clinical practice for motor symptom treatment. This is primarily due to lack of good quality data.

Gait analysis in patients with Parkinson's disease and motor fluctuations: Influence of levodopa and comparison with other measures of motor function

1998 ◽  
Vol 13 (6) ◽  
pp. 900-906 ◽  
Author(s):  
John D. O'Sullivan ◽  
Catherine M. Said ◽  
Louise C. Dillon ◽  
Marion Hoffman ◽  
Andrew J. Hughes
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Motor Function ◽  
Motor Fluctuations

scholarly journals Intensive Rehabilitation Enhances Lymphocyte BDNF-TrkB Signaling in Patients With Parkinson’s Disease

2015 ◽  
Vol 30 (5) ◽  
pp. 411-418 ◽  
Author(s):  
Cecilia Fontanesi ◽  
Svetlana Kvint ◽  
Giuseppe Frazzitta ◽  
Rossana Bera ◽  
Davide Ferrazzoli ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Function ◽  
Rating Scale ◽  
Cortical Plasticity ◽  
Day Treatment ◽  
Human Study ◽  
Exercise Program ◽  
Peripheral Lymphocytes ◽  
Intensive Rehabilitation

Background. In a combined animal and human study, we have previously found that a 5-day treatment that enhances cortical plasticity also facilitates brain-derived neurotrophic factor (BDNF)-tyrosine receptor kinase B (TrkB) signaling and increases activated TrkB and N-methyl-d-aspartate receptor (NMDAR) association in both the cortex and the peripheral lymphocytes. Patients with Parkinson’s disease (PD), in general, show decreased cortical plasticity, as demonstrated by electrophysiological and behavioral studies. Here, we test the hypothesis that an exercise program that improves motor function and seems to slow down symptom progression can enhance BDNF-TrkB signaling in lymphocytes. Methods. A total of 16 patients with PD underwent a 4-week multidisciplinary intensive rehabilitation treatment (MIRT), which included aerobic training and physical and occupational therapy. Blood was collected before and after 2 and 4 weeks of MIRT. Lymphocytes were isolated to examine BDNF-TrkB signaling induced by incubation with recombinant human BDNF. TrkB signaling complexes, extracellular-signal-regulated kinase-2 and protein-kinase-B were immunoprecipitated; the content of immunocomplexes was determined by Western blotting. Results. After MIRT, all patients showed improvement in motor function. TrkB interaction with NMDAR and BDNF-TrkB signaling increased in peripheral lymphocytes at receptor, intracellular mediator, and downstream levels. The decrements in Unified Parkinson’s Disease Rating Scale II (UPDRSII) and total scores were significantly correlated with the increases in TrkB signaling at receptor, intracellular mediator, and NMDAR interaction levels. Conclusions. The significant correlation between reduced UPDRS scores and the changes in lymphocyte activity suggest that enhanced BDNF-TrkB signaling in lymphocyte and reduced severity of PD symptoms may be related.

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 Resveratrol in Rodent Models of Parkinson’s Disease: A Systematic Review of Experimental Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
Cheng-Fu Su ◽  
Li Jiang ◽  
Xiao-Wen Zhang ◽  
Ashok Iyaswamy ◽  
Min Li
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Animal Models ◽  
Motor Function ◽  
Quantitative Methods ◽  
Experimental Studies ◽  
Future Research ◽  
Rodent Models ◽  
Neuroprotective Effects ◽  
Systematic Analysis

Parkinson’s disease (PD) is a common neurodegenerative disease featured by progressive degeneration of nigrostriatal dopaminergic neurons (DA) accompanied with motor function impairment. Accumulating evidence has demonstrated that natural compounds from herbs have potent anti-PD efficacy in PD models. Among those compounds, resveratrol, a polyphenol found in many common plants and fruits, is more effective against PD. Resveratrol has displayed a potent neuroprotective efficacy in several PD animal models. However, there is still no systematic analysis of the quality of methodological design of these studies, nor of their results. In this review, we retrieved and analyzed 18 studies describing the therapeutic effect of resveratrol on PD animal models. There are 5 main kinds of PD rodent models involved in the 18 articles, including chemical-induced (MPTP, rotenone, 6-OHDA, paraquat, and maneb) and transgenic PD models. The neuroprotective mechanisms of resveratrol were mainly concentrated on the antioxidation, anti-inflammation, ameliorating mitochondrial dysfunction, and motor function. We discussed the disadvantages of different PD animal models, and we used meta-analysis approach to evaluate the results of the selected studies and used SYRCLE’s risk of bias tool to evaluate the methodological quality. Our analytical approach minimized the bias of different studies. We have also summarized the pharmacological mechanisms of resveratrol on PD models as reported by the researchers. The results of this study support the notion that resveratrol has significant neuroprotective effects on different PD models quantified using qualitative and quantitative methods. The collective information in our review can guide researchers to further plan their future experiments without any hassle regarding preclinical and clinical studies. In addition, this collective assessment of animal studies can provide a qualitative analysis of different PD animal models, either to guide further testing of these models or to avoid unnecessary duplication in their future research.

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