Artificial Neural Network Based on Ensemble Empirical Mode Decomposition Features for Human Gait Diagnosis and Classification

2020 ◽  
Author(s):  
Rami Alkhatib ◽  
Mohamad O. Diab ◽  
Christophe Corbier ◽  
Mohamed El Badaoui
Keyword(s):  
Neural Network ◽  
Ground Reaction Force ◽  
Reaction Force ◽  
The Elderly ◽  
Ensemble Empirical Mode Decomposition ◽  
Human Gait ◽  
Walking Ability ◽  
Intrinsic Mode Functions ◽  
Vertical Ground Reaction Force ◽  
Mode Decomposition

Abstract Human gait analysis has been widely used to assess the stage of disease affecting the walking ability. The gait signals, namely vertical ground reaction force signals, become more unsteady and non-linear with the progress of the disease. This paper makes use of ground reaction force signals measured from both normal and subjects diagnosed with Parkinson. New features are then extracted from different intrinsic mode functions as a result of the ensemble mode decomposition. The extracted features are divided randomly into a training set of 60%, a validation set of 15% and testing set of 25%. The neural network is then employed which yield an interesting overall classification accuracy of 95.7 %. This paper will pave the way for better rehabilitative programs, understanding of gait biomechanics and fall prevention among the elderly.

Effect of ankle-foot orthosis on level walking in healthy subjects

2019 ◽  
Vol 233 (12) ◽  
pp. 1262-1268
Author(s):  
Mizuki Kato ◽  
Arinori Kamono ◽  
Naomichi Ogihara
Keyword(s):  
Ground Reaction Force ◽  
Healthy Subjects ◽  
Sagittal Plane ◽  
Reaction Force ◽  
Walking Ability ◽  
Vertical Ground Reaction Force ◽  
Walking Gait ◽  
Ankle Foot Orthosis ◽  
Heel Contact ◽  
Foot Orthosis

An ankle-foot orthosis is often prescribed in the rehabilitation of patients with neurological motor disorders such as hemiparesis. However, walking with a unilateral ankle-foot orthosis may not be effectively achieved just by trying to reproduce normal intact walking with a symmetrical gait pattern. Understanding skills to facilitate walking gait with a unilateral ankle-foot orthosis has implications for better rehabilitative interventions to help restore walking ability in patients with stroke. We, therefore, analyzed the kinematics and ground reaction forces of walking with and without an ankle-foot orthosis in healthy subjects to infer the possible skills to facilitate walking gait with a unilateral ankle-foot orthosis. Adult male participants were asked to walk with and without an ankle-foot orthosis across two force platforms set in a wooden walkway, and body kinematics and ground reaction force profiles in the sagittal plane were simultaneously recorded. We found that the forward tilting angle of the trunk at the time of toe-off of the leg with the ankle-foot orthosis was significantly larger than that of the leg without the ankle-foot orthosis, to adaptively compensate for the loss of ankle joint mobility due to the unilateral ankle-foot orthosis. Furthermore, the peak vertical ground reaction force at heel-contact was significantly larger in the leg without the ankle-foot orthosis than in the leg with the ankle-foot orthosis owing to the fact that the stance phase duration of the leg with the ankle-foot orthosis was relatively shorter. Such information may potentially be applied to facilitate walking training in stroke patients wearing a unilateral ankle-foot orthosis.

scholarly journals Gait Recognition for Human-Exoskeleton System in Locomotion Based on Ensemble Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Qiu ◽  
Huxian Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Performance ◽  
Human Subjects ◽  
Gait Recognition ◽  
Ensemble Empirical Mode Decomposition ◽  
Machine Learning Algorithms ◽  
Human Gait ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Phase Recognition

As exoskeleton robots are more frequently applied to impaired people to regain mobility, detection and recognition of human gait motions is important to prepare suitable control modes for exoskeletons. This paper proposes to explore the potential of the ensemble empirical mode decomposition (EEMD) method to help analyze and recognize gait motions for human subjects who wear the exoskeleton to walk. The intrinsic mode functions (IMFs) extracted from the original gait signals by EEMD are utilized to act as inputs for classification algorithms. Evident correlations are found between some IMFs and original gait kinematic sequences. Experimental results on gait phase recognition performance on 14 able-bodied subjects are shown. The performance of the composing signals extracted from the original signals as IMF 1 ∼ IMF 8 is investigated, which indicates that IMF 8 might be helpful when wearing exoskeleton and IMF 5 might be helpful when walking without exoskeleton on gait recognition. And the similarity of joint synergy between wearing and without wearing exoskeleton is analyzed, and the result shows that the joint synergy might change between with and without wearing exoskeleton. The quantitative results show that based on some IMFs of the same orders, these machine learning algorithms can achieve promising performances.

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 Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 931
Author(s):  
Kecheng Peng ◽  
Xiaoqun Cao ◽  
Bainian Liu ◽  
Yanan Guo ◽  
Wenlong Tian
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
South Asian ◽  
Recurrent Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
South Asian High ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Deep Learning Model

The intensity variation of the South Asian high (SAH) plays an important role in the formation and extinction of many kinds of mesoscale systems, including tropical cyclones, southwest vortices in the Asian summer monsoon (ASM) region, and the precipitation in the whole Asia Europe region, and the SAH has a vortex symmetrical structure; its dynamic field also has the symmetry form. Not enough previous studies focus on the variation of SAH daily intensity. The purpose of this study is to establish a day-to-day prediction model of the SAH intensity, which can accurately predict not only the interannual variation but also the day-to-day variation of the SAH. Focusing on the summer period when the SAH is the strongest, this paper selects the geopotential height data between 1948 and 2020 from NCEP to construct the SAH intensity datasets. Compared with the classical deep learning methods of various kinds of efficient time series prediction model, we ultimately combine the Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which has the ability to deal with the nonlinear and unstable single system, with the Permutation Entropy (PE) method, which can extract the SAH intensity feature of IMF decomposed by CEEMDAN, and the Convolution-based Gated Recurrent Neural Network (ConvGRU) model is used to train, test, and predict the intensity of the SAH. The prediction results show that the combination of CEEMDAN and ConvGRU can have a higher accuracy and more stable prediction ability than the traditional deep learning model. After removing the redundant features in the time series, the prediction accuracy of the SAH intensity is higher than that of the classical model, which proves that the method has good applicability for the prediction of nonlinear systems in the atmosphere.

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