Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson’s Disease

Freezing of Gait (FoG) is a common symptom in Parkinson’s Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessment protocol organized into eight multiple video recorded sessions, both in clinical and ecological settings and both in the ON and OFF state. We compared the FoG episodes detected using the processed data gathered from the insoles with those tagged by a clinician on video recordings. The algorithm correctly detected 90% of the episodes. The false positive rate was 6% and the false negative rate 4%. The algorithm reliably detects freezing of gait in clinical settings while performing ecological tasks. This result is promising for freezing of gait detection in everyday life via wearable instrumented insoles that can be integrated into a more complex system for comprehensive motor symptom monitoring in PD.

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A Cross-Sectional Study of Set Shifting Impairments and Falling in Individuals with and without Parkinson’s Disease

10.1101/146332 ◽

2017 ◽

Author(s):

J. Lucas McKay ◽

Kimberly C. Lang ◽

Lena H. Ting ◽

Madeleine E. Hackney

Keyword(s):

Older Adults ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Function ◽

Freezing Of Gait ◽

Cross Sectional Study ◽

Set Shifting ◽

Sectional Study ◽

Cross Sectional ◽

Increased Risk

AbstractINTRODUCTION. Individuals with Parkinson’s disease (PD) are at increased risk for falls, and exhibit deficits in executive function, including Set Shifting, which can be measured as the difference between parts B and A of the Trailmaking Test. METHODS. We conducted a cross-sectional study using baseline data of PD patients with and without freezing of gait (FOG) (n=69) and community-dwelling neurologically-normal older adults (NON-PD) (n=84) who had volunteered to participate in clinical rehabilitation research. Multivariate logistic regression analyses were performed to determine associations between Set Shifting, PD, and faller status, as determined by ≥1 self-reported falls in the previous 6 months, after adjusting for demographic and cognitive factors and clinical disease characteristics. RESULTS. Impaired Set Shifting was associated with previous falls after controlling for age, sex, overall cognitive function, PD, FOG, and PD disease duration (OR=1.29 [1.03-1.60]; P=0.02). In models controlling for age, sex, and overall cognitive function, PD was associated with increased fall prevalence among the study sample (OR=4.15 [95% CI 1.65-10.44], P<0.01) and FOG was associated with increased fall prevalence among the PD sample (OR=3.63 [1.22-10.80], P=0.02). Although the strongest associations between Set Shifting and falling were observed among PD without FOG (OR=2.11) compared to HOA (OR=1.14) and PD with FOG (OR=1.46) in a multivariate model that allowed for interaction between set shifting and PD status, there was insufficient evidence to reject the null hypothesis of no interaction. CONCLUSIONS. Set Shifting is associated with previous falls in non-demented older adults with and without PD.HighlightsIndividuals with PD are at increased risk for falls, although causes are unclear.Impaired Set Shifting was associated with falls in older adults with and without PD.Associations were strongest among those with PD but without freezing of gait.

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Safety and Tolerability of Burst-Cycling Deep Brain Stimulation for Freezing of Gait in Parkinson’s Disease

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.651168 ◽

2021 ◽

Vol 15 ◽

Author(s):

Joshua K. Wong ◽

Wei Hu ◽

Ryan Barmore ◽

Janine Lopes ◽

Kathryn Moore ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Brain Stimulation ◽

Brain Stimulation ◽

Freezing Of Gait ◽

Functional Mobility ◽

Common Symptom ◽

Burst Frequency ◽

Serious Adverse Events ◽

Deep Brain

Background: Freezing of gait (FOG) is a common symptom in Parkinson’s disease (PD) and can be difficult to treat with dopaminergic medications or with deep brain stimulation (DBS). Novel stimulation paradigms have been proposed to address suboptimal responses to conventional DBS programming methods. Burst-cycling deep brain stimulation (BCDBS) delivers current in various frequencies of bursts (e.g., 4, 10, or 15 Hz), while maintaining an intra-burst frequency identical to conventional DBS.Objective: To evaluate the safety and tolerability of BCDBS in PD patients with FOG.Methods: Ten PD subjects with STN or GPi DBS and complaints of FOG were recruited for this single center, single blinded within-subject crossover study. For each subject, we compared 4, 10, and 15 Hz BCDBS to conventional DBS during the PD medication-OFF state.Results: There were no serious adverse events with BCDBS. It was feasible and straightforward to program BCDBS in the clinic setting. The benefit was comparable to conventional DBS in measures of FOG, functional mobility and in PD motor symptoms. BCDBS had lower battery consumption when compared to conventional DBS.Conclusions: BCDBS was feasible, safe and well tolerated and it has potential to be a viable future DBS programming strategy.

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Amyloid ß Impacts Future Freezing of Gait in Parkinson’s Disease Via White Matter Hyperintensities

10.1101/2020.10.29.360552 ◽

2020 ◽

Author(s):

Mahsa Dadar ◽

Janis Miyasaki ◽

Simon Duchesne ◽

Richard Camicioli

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

White Matter ◽

White Matter Hyperintensities ◽

Freezing Of Gait ◽

Amyloid Β ◽

Common Symptom ◽

Activity Levels ◽

Lewy Body Pathology ◽

The Impact

AbstractBackgroundFreezing of gait (FOG) is a common symptom in Parkinson’s Disease (PD) patients. Previous studies have reported relationships between FOG, substantia nigra (SN) degeneration, dopamine transporter (DAT) concentration, as well as amyloid β deposition. However, there is a paucity of research on the concurrent impact of white matter damage.ObjectivesTo assess the inter-relationships between these different co-morbidities, their impact on future FOG and whether they act independently of each other.MethodsWe used baseline MRI and longitudinal gait data from the Parkinson’s Progression Markers Initiative (PPMI). We used deformation based morphometry (DBM) from T1-weighted MRI to measure SN atrophy, and segmentation of white matter hyperintensities (WMH) as a measure of WM pathological load. Putamen and caudate DAT levels from SPECT as well as cerebrospinal fluid (CSF) amyloid β were obtained directly from the PPMI. Following correlation analyses, we investigated whether WMH burden mediates the impact of amyloid β on future FOG.ResultsSN DBM, WMH load, putamen and caudate DAT activity and CSF amyloid β levels were significantly different between PD patients with and without future FOG (p < 0.008). Mediation analysis demonstrated an effect of CSF amyloid β levels on future FOG via WMH load, independent of SN atrophy and striatal DAT activity levels.ConclusionsAmyloid β might impact future FOG in PD patients through an increase in WMH burden, in a pathway independent of Lewy body pathology.

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Falls in Parkinson’s Disease Subtypes: Risk Factors, Locations and Circumstances

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16122216 ◽

2019 ◽

Vol 16 (12) ◽

pp. 2216 ◽

Cited By ~ 8

Author(s):

Paulo H. S. Pelicioni ◽

Jasmine C. Menant ◽

Mark D. Latt ◽

Stephen R. Lord

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Postural Sway ◽

Freezing Of Gait ◽

Quadriceps Muscle ◽

Cognitive Status ◽

Functional Impairments ◽

Timed Up And Go ◽

Functional Measures ◽

Increased Risk

People with Parkinson’s disease (PD) can be classified into those with postural instability and gait difficulty (PIGD subtype) and those manifesting tremor as the main symptoms (non-PIGD subtype). In a prospective cohort study of 113 people with PD we aimed to contrast fall rates and circumstances as well as a range of disease-related, clinical, and functional measures between the PD subtypes. Compared with non-PIGD participants, PIGD participants were significantly more likely to suffer more falls overall as well as more falls due to freezing of gait, balance-related falls and falls at home. The PIGD group also performed significantly worse in a range of fall-related clinical and functional measures including general cognitive status, executive function, quadriceps muscle strength, postural sway and the timed up and go test. These findings document the extent to which people with the PIGD subtype are at increased risk of falls, the circumstances in which they fall and their disease-related, clinical and functional impairments.

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Optimization of SVM Parameters with Hybrid CS-PSO Algorithms for Parkinson’s Disease in LabVIEW Environment

Parkinson s Disease ◽

10.1155/2019/2513053 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Duygu Kaya

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

False Positive Rate ◽

False Negative ◽

False Negative Rate ◽

Optimization Methods ◽

Optimization Method ◽

Training Samples ◽

Svm Model ◽

Positive Rate

Optimization is the process of achieving the best solution for a problem. LabVIEW based on an SVM model is proposed in this paper to get the best SVM parameters using the hybrid CS and PSO method. PCA is used as a preprocessor of SVM for reducing the dimension of data and extracting features of training samples. Also, SVM parameters are optimized for Parkinson’s disease data by combining CS and PSO. The designed system is used to determine the best SVM parameters, and it is compared to PSO and CS optimization methods and found that the used CS-PSO hybrid optimization method is better. The hybrid model shows that the accuracy of the performance achieved is 97.4359%. Also, the data classification results obtained by using SVM parameters determined by optimization are measured by precision, recall, F1 score, false positive rate (FPR), false discovery rate (FDR), false negative rate (FNR), negative predictive value (NPV), and Matthews’ correlation coefficient (MCC) parameters.

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Falls risk is predictive of dysphagia in Parkinson’s disease

Neurological Sciences ◽

10.1007/s10072-021-05700-6 ◽

2021 ◽

Author(s):

Christopher Kobylecki ◽

Irena Shiderova ◽

Mihaela Boca ◽

Emilia Michou

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Correlation Analysis ◽

Freezing Of Gait ◽

Gait Disturbance ◽

Common Symptom ◽

Factors Associated ◽

Axial Symptoms ◽

Gait Score ◽

The Relationship

Abstract Objective Evaluate the relationship between falls, freezing of gait, and swallowing disturbance in Parkinson’s disease (PD). Background Dysphagia is a common symptom in PD, and is often thought of as an axial feature along with falls and gait disturbance. It is of interest to examine the relationship between these symptoms in PD, given the possibility of shared pathophysiology due to non-dopaminergic and extranigral dysfunction. Methods We recruited 29 consecutive non-demented patients with idiopathic PD and at least one clinically determined impairment in swallowing, falls, or freezing of gait. Swallow dysfunction was assessed using the Swallowing Disturbance Questionnaire (SDQ). The Falls Efficacy Scale and Freezing-of-gait questionnaire were recorded. Correlation analysis and multiple regression were used to determine the relationship between swallow and gait disturbance. Results Total SDQ score correlated strongly with the falls efficacy scale (Spearman’s rho = 0.594; P = 0.001), but not with the freezing-of-gait score. Linear regression controlling for other factors associated with dysphagia identified falls efficacy score as a significant predictor of swallow dysfunction. Conclusions The severity of dysphagia in PD is closely related to severity of falls, but not gait freezing. This may be helpful to more precisely determine the anatomical substrate of levodopa-resistant axial symptoms in PD and provide clues to further management.

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Visuospatial n-back test for early detection of mild cognitive impairment in patients with Parkinson’s Disease : an fMRI study

10.1101/2020.03.03.974725 ◽

2020 ◽

Author(s):

Shoji Kawashima ◽

Yoko Shimizu ◽

Yoshino Ueki ◽

Noriyuki Matsukawa

Keyword(s):

Parkinson’S Disease ◽

Working Memory ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Early Detection ◽

Functional Neuroimaging ◽

Common Symptom ◽

Visuospatial Working Memory ◽

Increased Risk

AbstractBackgroundCognitive impairment is a common symptom in the patients with Parkinson’s disease (PD). In delineating a therapeutic plan, the early diagnosis of mild cognitive impairment in PD (PD-MCI) is important. Patients with PD-MCI have severe impairment in frontal executive function and/or visuospatial recognition. However, the clinical assessment of these functions is not routinely performed.MethodIn this study, we aimed to clarify the advantage of visuospatial version of the n-back test as a tool for the early detection of neuropsychological change in the patients with PD-MCI. The score of 0-back test reflects visuospatial recognition, and the scores of 1-back and 2-back reflect visuospatial working memory. PD-MCI was classified according to the criteria provided by the Movement Disorder Society Task Force for mild cognitive impairment in PD. We recruited 13 patients with PD-MCI, and 15 patients with cognitive normal PD. Using functional MRI (fMRI), we also aimed to clarify the specific brain regions associated with the impairment of visuospatial working memory.ResultWe demonstrated that the correct answer rate of patients with PD-MCI was lower in the 2-back test than patients with PD-CN. However, we did not find statistical difference in the 0-back test. These results indicate the preservation of visuospatial recognition and the impairment of visuospatial working memory in the patients with PD-MCI. We revealed the reduced activation within the middle frontal gyrus (MFG) and the inferior parietal lobule (IPL) during the 2-back test in the patients with PD-MCI. It may be associated with the severity of cortico-striatal dysfunction in the dopaminergic neural network which is associated with Lewy body pathology.ConclusionThe visuospatial n-back test has advantages for use in rapid and early detection of impaired visual recognition and working memory. The combination of functional neuroimaging and neuropsychological tests may provide markers for the increased risk of dementia before the development of an irreversible disease-specific pathology.

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Deep feature learning for FoG episodes prediction In patients with PD

Future Computing and Informatics Journal ◽

10.54623/fue.fcij.5.2.2 ◽

2020 ◽

Vol 5 (2) ◽

pp. 79-95

Author(s):

Hadeer Elziaat ◽

◽

Nashwa El-Bendary ◽

Ramdan Mowad ◽

◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Freezing Of Gait ◽

Feature Learning ◽

Classification Problem ◽

Common Symptom ◽

Suggested Approach ◽

Time Frequency ◽

Deep Feature ◽

Multi Class Classification

A common symptom of Parkinson's Disease is Freezing of Gait (FoG) that causes an interrupt of the forward progression of the patient’s feet while walking. Therefore, Freezing of Gait episodes is always engaged to the patient's falls. This paper proposes a model for Freezing of Gait episodes detection and prediction in patients with Parkinson's disease. Predicting Freezing of Gait in this paper considers as a multi-class classification problem with 3 classes namely, FoG, pre-FoG, and walking episodes. In this paper, the extracted feature scheme applied for the detection and the prediction of FoG is Convolutional Neural Network (CNN) spectrogram time-frequency features. The dataset is collected from three tri-axial accelerometer sensors for PD patients with FoG. The performance of the suggested approach has been distinguished by different machine learning classifiers and accelerometer axes.

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