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 Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson’s Disease

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2416 ◽  
Author(s):  
Sara Soltaninejad ◽  
Irene Cheng ◽  
Anup Basu
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Freezing Of Gait ◽  
The Elderly ◽  
Assessment System ◽  
Microsoft Kinect ◽  
Data Sets ◽  
Fog Prediction ◽  
Foot Joint ◽  
Rehabilitation Clinic

Parkinson’s disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. FOG is a short absence or reduction of ability to walk for PD patients which can cause fall, reduction in patients’ quality of life, and even death. Existing FOG assessments by doctors are based on a patient’s diaries and experts’ manual video analysis which give subjective, inaccurate, and unreliable results. In the present research, an automatic FOG assessment system is designed for PD patients to provide objective information to neurologists about the FOG condition and the symptom’s characteristics. The proposed FOG assessment system uses an RGB-D sensor based on Microsoft Kinect V2 for capturing data for 5 healthy subjects who are trained to imitate the FOG phenomenon. The proposed FOG assessment system is called “Kin-FOG”. The analysis of foot joint trajectory of the motion captured by Kinect is used to find the FOG episodes. The evaluation of Kin-FOG is performed by two types of experiments, including: (1) simple walking (SW); and (2) walking with turning (WWT). Since the standing mode has features similar to a FOG episode, our Kin-FOG system proposes a method to distinguish between the FOG and standing episodes. Therefore, two general groups of experiments are conducted with standing state (WST) and without standing state (WOST). The gradient displacement of the angle between the foot and the ground is used as the feature for discriminating between FOG and standing modes. These experiments are conducted with different numbers of FOGs for getting reliable and general results. The Kin-FOG system reports the number of FOGs, their lengths, and the time slots when they occur. Experimental results demonstrate Kin-FOG has around 90% accuracy rate for FOG prediction in both experiments for different tasks (SW, WWT). The proposed Kin-FOG system can be used as a remote application at a patient’s home or a rehabilitation clinic for sending a neurologist the required FOG information. The reliability and generality of the proposed system will be evaluated for bigger data sets of actual PD subjects.

scholarly journals C-Gait for Detecting Freezing of Gait in the Early to Middle Stages of Parkinson’s Disease: A Model Prediction Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Zi-Yan Chen ◽  
Hong-Jiao Yan ◽  
Lin Qi ◽  
Qiao-Xia Zhen ◽  
Cui Liu ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Model Prediction ◽  
Freezing Of Gait ◽  
Walking Ability ◽  
Middle Stage ◽  
Gait Assessment ◽  
Walking Tests

Graphical AbstractPatients with early- to middle-stage PD (Hoehn and Yahr stages 1–3) were enrolled for C-Gait assessment and traditional walking ability assessments. The correlation of C-Gait assessment and traditional walking tests were studied. Two models were established based on C-Gait assessment and traditional walking tests to explore the value of C-Gait assessment in predicting freezing of gait.

scholarly journals Quantitative Analysis of Motor Status in Parkinson’s Disease Using Wearable Devices: From Methodological Considerations to Problems in Clinical Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Masahiko Suzuki ◽  
Hiroshi Mitoma ◽  
Mitsuru Yoneyama
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Sleep Disturbances ◽  
Freezing Of Gait ◽  
Wearable Devices ◽  
Mean Values ◽  
Methodological Problems ◽  
Methodological Considerations ◽  
Motor Abnormalities ◽  
Gait Disturbances

Long-term and objective monitoring is necessary for full assessment of the condition of patients with Parkinson’s disease (PD). Recent advances in biotechnology have seen the development of various types of wearable (body-worn) sensor systems. By using accelerometers and gyroscopes, these devices can quantify motor abnormalities, including decreased activity and gait disturbances, as well as nonmotor signs, such as sleep disturbances and autonomic dysfunctions in PD. This review discusses methodological problems inherent in wearable devices. Until now, analysis of the mean values of motion-induced signals on a particular day has been widely applied in the clinical management of PD patients. On the other hand, the reliability of these devices to detect various events, such as freezing of gait and dyskinesia, has been less than satisfactory. Quantification of disease-specific changes rather than nonspecific changes is necessary.

scholarly journals Inertial Sensor Based Detection of Freezing of Gait for On-Demand Cueing in Parkinson’s Disease

2020 ◽  
Vol 53 (2) ◽  
pp. 16004-16009
Author(s):  
A. Dvorani ◽  
M.C.E. Jochner ◽  
T. Seel ◽  
C. Salchow-Hömmen ◽  
J. Meyer-Ohle ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensor ◽  
Freezing Of Gait ◽  
On Demand

scholarly journals Detection of Freezing of Gait using Convolutional Neural Networks and Data from Lower Limb Motion Sensors

2021 ◽  
Author(s):  
Bohan Shi ◽  
Arthur Tay ◽  
Dawn M.L. Tan ◽  
Nicole S.Y. Chia ◽  
W.L. Au ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Geometric Mean ◽  
Freezing Of Gait ◽  
Sensor Data ◽  
Motion Sensors ◽  
Clinical Assessments ◽  
The Past ◽  
Proposed Model ◽  
Measurement Units

<div>Freezing of Gait is the most disabling gait disturbance in Parkinson’s disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson’s disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.</div>

scholarly journals Detection of Freezing of Gait using Convolutional Neural Networks and Data from Lower Limb Motion Sensors

2021 ◽  
Author(s):  
Bohan Shi ◽  
Arthur Tay ◽  
Dawn M.L. Tan ◽  
Nicole S.Y. Chia ◽  
W.L. Au ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Geometric Mean ◽  
Freezing Of Gait ◽  
Sensor Data ◽  
Motion Sensors ◽  
Clinical Assessments ◽  
The Past ◽  
Proposed Model ◽  
Measurement Units

<div>Freezing of Gait is the most disabling gait disturbance in Parkinson’s disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson’s disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.</div>

scholarly journals Parkinson’s disease patients benefit from bicycling - a systematic review and meta-analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Marianne Tiihonen ◽  
Britta U. Westner ◽  
Markus Butz ◽  
Sarang S. Dalal
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Meta Analysis ◽  
Freezing Of Gait ◽  
Quality Analysis ◽  
Cognitive Measures ◽  
Inverse Variance ◽  
Motor Outcomes ◽  
Gait Disturbances

AbstractMany Parkinson’s disease (PD) patients are able to ride a bicycle despite being severely compromised by gait disturbances up to freezing of gait. This review [PROSPERO CRD 42019137386] aimed to find out, which PD-related symptoms improve from bicycling, and which type of bicycling exercise would be most beneficial. Following a systematic database literature search, peer-reviewed studies with randomized control trials (RCT) and with non-randomized trials (NRCT) investigating the interventional effects of bicycling on PD patients were included. A quality analysis addressing reporting, design and possible bias of the studies, as well as a publication bias test was done. Out of 202 references, 22 eligible studies with 505 patients were analysed. An inverse variance-based analysis revealed that primary measures, defined as motor outcomes, benefitted from bicycling significantly more than cognitive measures. Additionally, secondary measures of balance, walking speed and capacity, and the PDQ-39 ratings improved with bicycling. The interventions varied in durations, intensities and target cadences. Conclusively, bicycling is particularly beneficial for the motor performance of PD patients, improving crucial features of gait. Furthermore, our findings suggest that bicycling improves the overall quality-of-life of PD patients.

scholarly journals Real-Time Detection of Freezing Motions in Parkinson's Patients for Adaptive Gait Phase Synchronous Cueing

2021 ◽  
Vol 12 ◽  
Author(s):  
Ardit Dvorani ◽  
Vivian Waldheim ◽  
Magdalena C. E. Jochner ◽  
Christina Salchow-Hömmen ◽  
Jonas Meyer-Ohle ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Prediction Models ◽  
Inertial Sensor ◽  
Freezing Of Gait ◽  
Area Under The Curve ◽  
Support Vector ◽  
Average Performance ◽  
Gait Phase ◽  
Fog Detection

Parkinson's disease is the second most common neurodegenerative disease worldwide reducing cognitive and motoric abilities of affected persons. Freezing of Gait (FoG) is one of the severe symptoms that is observed in the late stages of the disease and considerably impairs the mobility of the person and raises the risk of falls. Due to the pathology and heterogeneity of the Parkinsonian gait cycle, especially in the case of freezing episodes, the detection of the gait phases with wearables is challenging in Parkinson's disease. This is addressed by introducing a state-automaton-based algorithm for the detection of the foot's motion phases using a shoe-placed inertial sensor. Machine-learning-based methods are investigated to classify the actual motion phase as normal or FoG-affected and to predict the outcome for the next motion phase. For this purpose, spatio-temporal gait and signal parameters are determined from the segmented movement phases. In this context, inertial sensor fusion is applied to the foot's 3D acceleration and rate of turn. Support Vector Machine (SVM) and AdaBoost classifiers have been trained on the data of 16 Parkinson's patients who had shown FoG episodes during a clinical freezing-provoking assessment course. Two clinical experts rated the video-recorded trials and marked episodes with festination, shank trembling, shuffling, or akinesia. Motion phases inside such episodes were labeled as FoG-affected. The classifiers were evaluated using leave-one-patient-out cross-validation. No statistically significant differences could be observed between the different classifiers for FoG detection (p&gt;0.05). An SVM model with 10 features of the actual and two preceding motion phases achieved the highest average performance with 88.5 ± 5.8% sensitivity, 83.3 ± 17.1% specificity, and 92.8 ± 5.9% Area Under the Curve (AUC). The performance of predicting the behavior of the next motion phase was significantly lower compared to the detection classifiers. No statistically significant differences were found between all prediction models. An SVM-predictor with features from the two preceding motion phases had with 81.6 ± 7.7% sensitivity, 70.3 ± 18.4% specificity, and 82.8 ± 7.1% AUC the best average performance. The developed methods enable motion-phase-based FoG detection and prediction and can be utilized for closed-loop systems that provide on-demand gait-phase-synchronous cueing to mitigate FoG symptoms and to prevent complete motoric blockades.

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