scholarly journals Evaluation of Vertical Ground Reaction Forces Pattern Visualization in Neurodegenerative Diseases Identification Using Deep Learning and Recurrence Plot Image Feature Extraction

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3857 ◽  
Author(s):  
Che-Wei Lin ◽  
Tzu-Chien Wen ◽  
Febryan Setiawan
Keyword(s):  
Deep Learning ◽  
Neurodegenerative Diseases ◽  
Classification Accuracy ◽  
Reaction Force ◽  
Recurrence Plot ◽  
Classification Algorithm ◽  
Image Feature ◽  
Feature Transformation ◽  
Vertical Ground Reaction Force ◽  
Vertical Ground

To diagnose neurodegenerative diseases (NDDs), physicians have been clinically evaluating symptoms. However, these symptoms are not very dependable—particularly in the early stages of the diseases. This study has therefore proposed a novel classification algorithm that uses a deep learning approach to classify NDDs based on the recurrence plot of gait vertical ground reaction force (vGRF) data. The irregular gait patterns of NDDs exhibited by vGRF data can indicate different variations of force patterns compared with healthy controls (HC). The classification algorithm in this study comprises three processes: a preprocessing, feature transformation and classification. In the preprocessing process, the 5-min vGRF data divided into 10-s successive time windows. In the feature transformation process, the time-domain vGRF data are modified into an image using a recurrence plot. The total recurrence plots are 1312 plots for HC (16 subjects), 1066 plots for ALS (13 patients), 1230 plots for PD (15 patients) and 1640 plots for HD (20 subjects). The principal component analysis (PCA) is used in this stage for feature enhancement. Lastly, the convolutional neural network (CNN), as a deep learning classifier, is employed in the classification process and evaluated using the leave-one-out cross-validation (LOOCV). Gait data from HC subjects and patients with amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD) and Parkinson’s disease (PD) obtained from the PhysioNet Gait Dynamics in Neurodegenerative disease were used to validate the proposed algorithm. The experimental results included two-class and multiclass classifications. In the two-class classification, the results included classification of the NDD and the HC groups and classification among the NDDs. The classification accuracy for (HC vs. ALS), (HC vs. HD), (HC vs. PD), (ALS vs. PD), (ALS vs. HD), (PD vs. HD) and (NDDs vs. HC) were 100%, 98.41%, 100%, 95.95%, 100%, 97.25% and 98.91%, respectively. In the multiclass classification, a four-class gait classification among HC, ALS, PD and HD was conducted and the classification accuracy of HC, ALS, PD and HD were 98.99%, 98.32%, 97.41% and 96.74%, respectively. The proposed method can achieve high accuracy compare to the existing results, but with shorter length of input signal (Input of existing literature using the same database is 5-min gait signal, but the proposed method only needs 10-s gait signal).

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 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 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.

scholarly journals A Systematic Approach to the Design and Characterization of a Smart Insole for Detecting Vertical Ground Reaction Force (vGRF) in Gait Analysis

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 957 ◽  
Author(s):  
Anas M. Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Sara Al-Hamouz ◽  
Merna Abdalla ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Reaction Force ◽  
Applied Pressure ◽  
Pressure Sensors ◽  
Clinical Diagnostics ◽  
Piezoelectric Sensors ◽  
Sensor Calibration ◽  
Vertical Ground Reaction Force ◽  
Vertical Ground ◽  
Smart Insole

Gait analysis is a systematic study of human locomotion, which can be utilized in various applications, such as rehabilitation, clinical diagnostics and sports activities. The various limitations such as cost, non-portability, long setup time, post-processing time etc., of the current gait analysis techniques have made them unfeasible for individual use. This led to an increase in research interest in developing smart insoles where wearable sensors can be employed to detect vertical ground reaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortable for gait analysis, and can monitor plantar pressure frequently through embedded sensors that convert the applied pressure to an electrical signal that can be displayed and analyzed further. Several research teams are still working to improve the insoles’ features such as size, sensitivity of insoles sensors, durability, and the intelligence of insoles to monitor and control subjects’ gait by detecting various complications providing recommendation to enhance walking performance. Even though systematic sensor calibration approaches have been followed by different teams to calibrate insoles’ sensor, expensive calibration devices were used for calibration such as universal testing machines or infrared motion capture cameras equipped in motion analysis labs. This paper provides a systematic design and characterization procedure for three different pressure sensors: force-sensitive resistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that can be used for detecting vGRF using a smart insole. A simple calibration method based on a load cell is presented as an alternative to the expensive calibration techniques. In addition, to evaluate the performance of the different sensors as a component for the smart insole, the acquired vGRF from different insoles were used to compare them. The results showed that the FSR is the most effective sensor among the three sensors for smart insole applications, whereas the piezoelectric sensors can be utilized in detecting the start and end of the gait cycle. This study will be useful for any research group in replicating the design of a customized smart insole for gait analysis.

Carrying loads with springy poles

1991 ◽  
Vol 71 (3) ◽  
pp. 1119-1122 ◽  
Author(s):  
R. Kram
Keyword(s):  
Oxygen Consumption ◽  
Ground Reaction Force ◽  
Oxygen Consumption Rate ◽  
Consumption Rate ◽  
Reaction Force ◽  
Load Carriage ◽  
Vertical Ground Reaction Force ◽  
Suspension Systems ◽  
Vertical Ground ◽  
Male Subjects

People throughout Asia use springy bamboo poles to carry the loads of everyday life. These poles are a very compliant suspension system that allows the load to move along a nearly horizontal path while the person bounces up and down with each step. Could this be an economical way to carry loads inasmuch as no gravitational work has to be done to lift the load repeatedly? To find out, an experiment was conducted in which four male subjects ran at 3.0 m/s on a motorized treadmill with no load and while carrying a load equal to 19% body wt with compliant poles. Oxygen consumption rate, vertical ground reaction force, and the force exerted by the load on the shoulders were measured. Oxygen consumption rate increased by 22%. The same increase has previously been observed when loads are carried with a backpack. Thus compliant poles are not a particularly economical method of load carriage. However, pole suspension systems offer important advantages: they minimize peak shoulder forces and loading rates. In addition, the peak vertical ground reaction force is only slightly increased above unloaded levels when loads are carried with poles.

Comparison of Biomechanical Factors between the Kicking and Stance Limbs

2004 ◽  
Vol 13 (2) ◽  
pp. 135-150 ◽  
Author(s):  
Scott Ross ◽  
Kevin Guskiewicz ◽  
William Prentice ◽  
Robert Schneider ◽  
Bing Yu
Keyword(s):  
Knee Flexion ◽  
Reaction Force ◽  
Knee Extension ◽  
Knee Kinematic ◽  
Vertical Ground Reaction Force ◽  
Time To Peak ◽  
Balance Task ◽  
Vertical Ground ◽  
Biomechanical Factors ◽  
Anterior Posterior

Objective:T o determine differences between contralateral limbs’ strength, proprio-ception, and kinetic and knee-kinematic variables during single-limb landing.Setting:Laboratory.Subjects:30.Measurements:Hip, knee, and foot isokinetic peak torques; anterior/posterior (AP) and medial/lateral (ML) sway displacements during a balance task; and stabilization times, vertical ground-reaction force (VGRF), time to peak VGRF, and knee-flexion range of motion (ROM) from initial foot contact to peak VGRF during single-limb landing.Results:The kicking limb had significantly greater values for knee-extension (P= .008) and -flexion (P= .047) peak torques, AP sway displacement (P= .010), knee-flexion ROM from initial foot contact to peak VGRF (P< .001), and time to peak VGRF (P= .004). No other dependent measures were significantly different between limbs (P> .05).Conclusion:The kicking limb had superior thigh strength, better proprioception, and greater knee-flexion ROM than the stance limb.

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