Distinguishing between Parkinson’s disease patients and healthy individuals using a comprehensive set of time, frequency and time-frequency features extracted from vertical ground reaction force data

2020 ◽  
Vol 62 ◽  
pp. 102132
Author(s):  
Sajjad Farashi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
Healthy Individuals ◽  
Vertical Ground Reaction Force ◽  
Time Frequency ◽  
Vertical Ground ◽  
Force Data ◽  
Frequency Features

scholarly journals Vertical ground reaction force marker for Parkinson’s disease

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0175951 ◽  
Author(s):  
Md Nafiul Alam ◽  
Amanmeet Garg ◽  
Tamanna Tabassum Khan Munia ◽  
Reza Fazel-Rezai ◽  
Kouhyar Tavakolian
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
Vertical Ground Reaction Force ◽  
Vertical Ground

scholarly journals Implementation of a Deep Learning Algorithm Based on Vertical Ground Reaction Force Time–Frequency Features for the Detection and Severity Classification of Parkinson’s Disease

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5207
Author(s):  
Febryan Setiawan ◽  
Che-Wei Lin
Keyword(s):  
Deep Learning ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
Detection Algorithm ◽  
Feature Transformation ◽  
Vertical Ground Reaction Force ◽  
Severity Level ◽  
Time Frequency ◽  
Severity Classification ◽  
Vertical Ground

Conventional approaches to diagnosing Parkinson’s disease (PD) and rating its severity level are based on medical specialists’ clinical assessment of symptoms, which are subjective and can be inaccurate. These techniques are not very reliable, particularly in the early stages of the disease. A novel detection and severity classification algorithm using deep learning approaches was developed in this research to classify the PD severity level based on vertical ground reaction force (vGRF) signals. Different variations in force patterns generated by the irregularity in vGRF signals due to the gait abnormalities of PD patients can indicate their severity. The main purpose of this research is to aid physicians in detecting early stages of PD, planning efficient treatment, and monitoring disease progression. The detection algorithm comprises preprocessing, feature transformation, and classification processes. In preprocessing, the vGRF signal is divided into 10, 15, and 30 s successive time windows. In the feature transformation process, the time domain vGRF signal in windows with varying time lengths is modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, principal component analysis (PCA) is used for feature enhancement. Finally, different types of convolutional neural networks (CNNs) are employed as deep learning classifiers for classification. The algorithm performance was evaluated using k-fold cross-validation (kfoldCV). The best average accuracy of the proposed detection algorithm in classifying the PD severity stage classification was 96.52% using ResNet-50 with vGRF data from the PhysioNet database. The proposed detection algorithm can effectively differentiate gait patterns based on time–frequency spectrograms of vGRF signals associated with different PD severity levels.

scholarly journals Gait Analyses of Parkinson’s Disease Patients Using Multiscale Entropy

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2604
Author(s):  
Yuan-Lun Hsieh ◽  
Maysam F. Abbod
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Dual Task ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
Pressure Sensors ◽  
Step Length ◽  
Multiscale Entropy ◽  
Vertical Ground Reaction Force ◽  
Sensor Signals

Parkinson’s disease (PD) is a type of neurodegenerative diseases. PD influences gait in many aspects: reduced gait speed and step length, increased axial rigidity, and impaired rhythmicity. Gait-related data used in this study are from PhysioNet. Twenty-one PD patients and five healthy controls (CO) were sorted into four groups: PD without task (PDw), PD with dual task (PDd), control without task (COw), and control with dual task (COd). Since dual task actions are attention demanding, either gait or cognitive function may be affected. To quantify the used walking data, eight pressure sensors installed in each insole are used to measure the vertical ground reaction force. Thus, quantitative measurement analysis is performed utilizing multiscale entropy (MSE) and complexity index (CI) to analyze and differentiate between the ground reaction force of the four different groups. Results show that the CI of patients with PD is higher than that of CO and 11 of the sensor signals are statistically significant (p < 0.05). The COd group has larger CI values at the beginning (p = 0.021) but they get lower at the end of the test (p = 0.000) compared to that in the COw group. The end-of-test CI for the PDw group is lower in one of the feet sensor signals, and in the right total ground reaction force compared to the PDd group counterparts. In conclusion, when people start to adjust their gait due to pathology or stress, CI may increase first and reach a peak, but it decreases afterward when stress or pathology is further increased.

Effects of trial repetition, limb side, intraday and inter-week variation on vertical and craniocaudal ground reaction forces in clinically normal Labrador Retrievers

2011 ◽  
Vol 24 (06) ◽  
pp. 435-444 ◽  
Author(s):  
B. Nordquist ◽  
J. Fischer ◽  
S. Y. Kim ◽  
S. M. Stover ◽  
T. Garcia-Nolen ◽  
...  
Keyword(s):  
Data Collection ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
Ground Reaction Forces ◽  
Vertical Ground Reaction Force ◽  
Reaction Forces ◽  
Pelvic Limb ◽  
Vertical Ground ◽  
Labrador Retrievers ◽  
Force Data

SummaryObjectives: To document the contributions of trial repetition, limb side, and intraday and inter-week measurements on variation in vertical and craniocaudal ground reaction force data.Methods: Following habituation, force and time data were collected for all four limbs of seven Labrador Retrievers during sets of five valid trot trials. Each set was performed twice daily (morning and afternoon), every seven days for three consecutive weeks. A repeated measures analysis of variance was used to determine the effects of limb, trial, intraday, and inter-week factors on ground reaction force data for the thoracic and pelvic limbs.Results: Of the four factors evaluated, variation due to trial repetition had the largest magnitude of effect on ground reaction forces. Trial within a set of data had an effect on all craniocaudal, but not vertical, ground reaction force variables studied, for the thoracic limbs. The first of five trials was often different from later trials. Some thoracic limb and pelvic limb variables were different between weeks. A limb side difference was only apparent for pelvic limb vertical ground reaction force data. Only pelvic limb craniocaudal braking variables were different between sets within a day.Discussion and clinical significance: When controlling for speed, handler, gait, weight and dog breed, variation in ground reaction forces mainly arise from trial repetition and inter-week data collection. When using vertical peak force and impulse to evaluate treatment, trial repetition and inter-week data collection should have minimal effect of the data.

scholarly journals Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features

2021 ◽  
Vol 11 (7) ◽  
pp. 902
Author(s):  
Febryan Setiawan ◽  
Che-Wei Lin
Keyword(s):  
Deep Learning ◽  
Neurodegenerative Diseases ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
Detection Algorithm ◽  
Transformation Process ◽  
Feature Transformation ◽  
Vertical Ground Reaction Force ◽  
Time Frequency ◽  
Vertical Ground

A novel identification algorithm using a deep learning approach was developed in this study to classify neurodegenerative diseases (NDDs) based on the vertical ground reaction force (vGRF) signal. The irregularity of NDD vGRF signals caused by gait abnormalities can indicate different force pattern variations compared to a healthy control (HC). The main purpose of this research is to help physicians in the early detection of NDDs, efficient treatment planning, and monitoring of disease progression. The detection algorithm comprises a preprocessing process, a feature transformation process, and a classification process. In the preprocessing process, the five-minute vertical ground reaction force signal was divided into 10, 30, and 60 s successive time windows. In the feature transformation process, the time–domain vGRF signal was modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, feature enhancement with principal component analysis (PCA) was utilized. Finally, a convolutional neural network, as a deep learning classifier, was employed in the classification process of the proposed detection algorithm and evaluated using leave-one-out cross-validation (LOOCV) and k-fold cross-validation (k-fold CV, k = 5). The proposed detection algorithm can effectively differentiate gait patterns based on a time–frequency spectrogram of a vGRF signal between HC subjects and patients with neurodegenerative diseases.

scholarly journals Running ground reaction forces across footwear conditions are predicted from the motion of two body mass components

2019 ◽  
Vol 126 (5) ◽  
pp. 1315-1325 ◽  
Author(s):  
Andrew B. Udofa ◽  
Kenneth P. Clark ◽  
Laurence J. Ryan ◽  
Peter G. Weyand
Keyword(s):  
Ground Reaction Force ◽  
Lower Limb ◽  
Reaction Force ◽  
Ground Reaction Forces ◽  
Vertical Ground Reaction Force ◽  
Time Patterns ◽  
Foot Strike ◽  
Reaction Forces ◽  
Vertical Ground ◽  
Force Time

Although running shoes alter foot-ground reaction forces, particularly during impact, how they do so is incompletely understood. Here, we hypothesized that footwear effects on running ground reaction force-time patterns can be accurately predicted from the motion of two components of the body’s mass (mb): the contacting lower-limb (m1 = 0.08mb) and the remainder (m2 = 0.92mb). Simultaneous motion and vertical ground reaction force-time data were acquired at 1,000 Hz from eight uninstructed subjects running on a force-instrumented treadmill at 4.0 and 7.0 m/s under four footwear conditions: barefoot, minimal sole, thin sole, and thick sole. Vertical ground reaction force-time patterns were generated from the two-mass model using body mass and footfall-specific measures of contact time, aerial time, and lower-limb impact deceleration. Model force-time patterns generated using the empirical inputs acquired for each footfall matched the measured patterns closely across the four footwear conditions at both protocol speeds ( r2 = 0.96 ± 0.004; root mean squared error  = 0.17 ± 0.01 body-weight units; n = 275 total footfalls). Foot landing angles (θF) were inversely related to footwear thickness; more positive or plantar-flexed landing angles coincided with longer-impact durations and force-time patterns lacking distinct rising-edge force peaks. Our results support three conclusions: 1) running ground reaction force-time patterns across footwear conditions can be accurately predicted using our two-mass, two-impulse model, 2) impact forces, regardless of foot strike mechanics, can be accurately quantified from lower-limb motion and a fixed anatomical mass (0.08mb), and 3) runners maintain similar loading rates (ΔFvertical/Δtime) across footwear conditions by altering foot strike angle to regulate the duration of impact. NEW & NOTEWORTHY Here, we validate a two-mass, two-impulse model of running vertical ground reaction forces across four footwear thickness conditions (barefoot, minimal, thin, thick). Our model allows the impact portion of the impulse to be extracted from measured total ground reaction force-time patterns using motion data from the ankle. The gait adjustments observed across footwear conditions revealed that runners maintained similar loading rates across footwear conditions by altering foot strike angles to regulate the duration of impact.

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