scholarly journals Analysis of Center of Pressure Signals by Using Decision Tree and Empirical Mode Decomposition to Predict Falls among Older Adults

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fang-Yin Liao ◽  
Chun-Chang Wu ◽  
Yi-Chun Wei ◽  
Li-Wei Chou ◽  
Kang-Ming Chang
Keyword(s):  
Older Adults ◽  
Decision Tree ◽  
Empirical Mode Decomposition ◽  
Center Of Pressure ◽  
Force Plate ◽  
Approximate Entropy ◽  
Training Group ◽  
Median Frequency ◽  
Mode Decomposition ◽  
History Of

Falls put older adults at great risk and are related to the body’s sense of balance. This study investigated how to detect the possibility of high fall risk subjects among older adults. The original signal is based on center of pressure (COP) measured using a force plate. The falling group includes 29 subjects who had a history of falls in the year preceding this study or had received high scores on the Short Falls Efficacy Scale (FES). The nonfalling group includes 47 enrollees with no history of falls and who had received low scores on the Short FES. The COP in both the anterior–posterior and mediolateral direction were calculated and analyzed through empirical mode decomposition (EMD) up to six levels. The following five features were extracted and imported to a decision tree algorithm: root-mean-square deviation, median frequency, total frequency power, approximate entropy, and sample entropy. The results showed that there were a larger number of statistically different feature parameters, and a higher classification of accuracy was obtained. With the aid of empirical mode decomposition, the average classification accuracy increased 10% and achieved a level of 99.74% in the training group and 96.77% in the testing group, respectively.

scholarly journals Empirical Mode Decomposition-Derived Entropy Features Are Beneficial to Distinguish Elderly People with a Falling History on a Force Plate Signal

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 472
Author(s):  
Li-Wei Chou ◽  
Kang-Ming Chang ◽  
Yi-Chun Wei ◽  
Mei-Kuei Lu
Keyword(s):  
Empirical Mode Decomposition ◽  
Fall Risk ◽  
Center Of Pressure ◽  
Force Plate ◽  
Approximate Entropy ◽  
The Elderly ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
History Of ◽  
Standard Derivation

Fall risk prediction is an important issue for the elderly. A center of pressure signal, derived from a force plate, is useful for the estimation of body calibration. However, it is still difficult to distinguish elderly people’s fall history by using a force plate signal. In this study, older adults with and without a history of falls were recruited to stand still for 60 s on a force plate. Forces in the x, y and z directions (Fx, Fy, and Fz) and center of pressure in the anteroposterior (COPx) and mediolateral directions (COPy) were derived. There were 49 subjects in the non-fall group, with an average age of 71.67 (standard derivation: 6.56). There were also 27 subjects in the fall group, with an average age of 70.66 (standard derivation: 6.38). Five signal series—forces in x, y, z (Fx, Fy, Fz), COPX, and COPy directions—were used. These five signals were further decomposed with empirical mode decomposition (EMD) with seven intrinsic mode functions. Time domain features (mean, standard derivation and coefficient of variations) and entropy features (approximate entropy and sample entropy) of the original signals and EMD-derived signals were extracted. Results showed that features extracted from the raw COP data did not differ significantly between the fall and non-fall groups. There were 10 features extracted using EMD, with significant differences observed among fall and non-fall groups. These included four features from COPx and two features from COPy, Fx and Fz.

A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy

2016 ◽  
Vol 231 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Xueli An ◽  
Junjie Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Real World ◽  
Approximate Entropy ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Simulation Signal

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.

scholarly journals The acute effect of using of texture foot orthoses on ground reaction forces in older adults during walking

2021 ◽  
Vol 42 (6) ◽  
pp. 756-763
Author(s):  
Aydin Valizadeh orang ◽  
Arefeh Mokhtari Malekabadi ◽  
AmirAli Jafarnezhadgero
Keyword(s):  
Older Adults ◽  
Frequency Spectrum ◽  
Force Plate ◽  
Acute Effect ◽  
Comfort Level ◽  
Ground Reaction Forces ◽  
Median Frequency ◽  
Foot Orthoses ◽  
Lower Limb Muscles ◽  
Reaction Forces

Background:Walking is one the common daily activities. With the beginning of middle age, weakness in the lower limb muscles can reduce the ability to walk. The use of foot orthoses reduce the load on the limbs and supports the joints during walking. The purpose of the present study was to investigate the acute effect of foot orthoses on the frequency spectrum of ground reaction forces during walking in the older adults. Methods: In this semi-experimental and laboratory study, 21 elderly (15 females and 6 males) with a mean height of 164.19±4.26 centimeters and weight of 80.04±3.50 kg, and age of 66.00±3.50 years were volunteered to participate in the study. The walking trials were done during three conditions including walking without foot orthoses, walking with small and large textured orthoses. The Bertec force plate (made in USA) with dimensions of 40 * 60 cm was used to record ground reaction forces. Results: The results of this study did not show any significant differences between walking without foot orthoses, walking with small and large textured foot orthoses for frequency of 99.5%, median frequency, frequency band and number of essential harmonics (P>0.05). However, the comfort level during wearing of large texture insole condition significantly increased compared to other conditions (P<0.05). Conclusion: The textured foot orthoses do not affect the frequency spectrum of ground reaction forces; however, it improves the comfort of the individual while walking.

Ensemble empirical mode decomposition-entropy and feature selection for pantograph fault diagnosis

2020 ◽  
Vol 26 (23-24) ◽  
pp. 2230-2242
Author(s):  
Ying Shi ◽  
Cai Yi ◽  
Jianhui Lin ◽  
Zhe Zhuang ◽  
Senhua Lai
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Effective Measure ◽  
Vibration Data ◽  
Ranking Criteria ◽  
Mode Decomposition

In this article, a fault diagnosis approach for a pantograph is developed with collected vibration data from a test rig. Ensemble empirical mode decomposition is used to decompose the signals to get intrinsic mode function, and four kinds of entropies (permu1tation entropy, approximate entropy, sample entropy, and fuzzy entropy) reflecting the working state are extracted as the inputs of the support vector machine based on particle swarm optimization algorithm support vector machine. The effect of data length, embedded dimension, and other parameters on calculation of the entropy value has also been studied. Multiple feature ranking criteria are used to select the useful features and improve the fault diagnosis accuracy of certain measurement points. Experimental results on pantograph vibration analysis have then confirmed that the proposed method provides an effective measure for pantograph diagnosis.

scholarly journals Single-Legged Hop and Single-Legged Squat Balance Performance in Recreational Athletes With a History of Concussion

2020 ◽  
Vol 55 (5) ◽  
pp. 488-493 ◽  
Author(s):  
Robert C. Lynall ◽  
Kody R. Campbell ◽  
Timothy C. Mauntel ◽  
J. Troy Blackburn ◽  
Jason P. Mihalik
Keyword(s):  
Injury Risk ◽  
Center Of Pressure ◽  
Balance Control ◽  
Dynamic Balance ◽  
Reaction Force ◽  
Force Plate ◽  
Analysis Of Covariance ◽  
Control Group ◽  
Time To Stabilization ◽  
History Of

Context Researchers have suggested that balance deficiencies may linger during functional activities after concussion recovery. Objective To determine whether participants with a history of concussion demonstrated dynamic balance deficits as compared with control participants during single-legged hops and single-legged squats. Design Cross-sectional study. Setting Laboratory. Patients or Other Participants A total of 15 previously concussed participants (6 men, 9 women; age = 19.7 ± 0.9 years, height = 169.2 ± 9.4 cm, mass = 66.0 ± 12.8 kg, median time since concussion = 126 days [range = 28–432 days]) were matched with 15 control participants (6 men, 9 women; age = 19.7 ± 1.6 years, height = 172.3 ± 10.8 cm, mass = 71.0 ± 10.4 kg). Intervention(s) During single-legged hops, participants jumped off a 30-cm box placed at 50% of their height behind a force plate, landed on a single limb, and attempted to achieve a stable position as quickly as possible. Participants performed single-legged squats while standing on a force plate. Main Outcome Measure(s) Time to stabilization (TTS; time for the normalized ground reaction force to stabilize after landing) was calculated during the single-legged hop, and center-of-pressure path and speed were calculated during single-legged squats. Groups were compared using analysis of covariance, controlling for average days since concussion. Results The concussion group demonstrated a longer TTS than the control group during the single-legged hop on the nondominant leg (mean difference = 0.35 seconds [95% confidence interval = 0.04, 0.64]; F2,27 = 5.69, P = .02). No TTS differences were observed for the dominant leg (F2,27 = 0.64, P = .43). No group differences were present for the single-legged squat on either leg (P ≥ .11). Conclusions Dynamic balance-control deficits after concussion may contribute to an increased musculoskeletal injury risk. Given our findings, we suggest that neuromuscular deficits currently not assessed after concussion may linger. Time to stabilization is a clinically applicable measure that has been used to distinguish patients with various pathologic conditions, such as chronic ankle instability and anterior cruciate ligament reconstruction, from healthy control participants. Whereas the single-legged squat may not sufficiently challenge balance control, future study of the more dynamic single-legged hop is needed to determine its potential diagnostic and prognostic value after concussion.

Biomechanics of Trunk During Walking Carrying Load on One Hand Using Nonlinear Methods

2014 ◽  
Author(s):  
Vikas Yadav ◽  
Maruti Ram Gudavalli ◽  
P. K. Raju ◽  
Dan Marghitu
Keyword(s):  
Correlation Dimension ◽  
Reaction Force ◽  
Force Plate ◽  
Approximate Entropy ◽  
Peak Force ◽  
Erector Spinae ◽  
Trunk Muscles ◽  
Emg Activity ◽  
Median Frequency ◽  
Trunk Motion

Activities of daily living include carrying objects using one hand. Carrying a load using one hand can alter the loading on the musculoskeletal system as well as the walking pattern. The objective of this pilot study was to quantify the ground reaction forces, electromyographic (EMG) activity of trunk muscles, and trunk motion during walking. Nine human volunteers with no symptoms of pain were recruited from the student and employee population of an academic institution. Data were recorded from 8 volunteer subjects. Participants were asked to walk at self-selected speed back and forth at their comfortable speed carrying loads varying from 0 to 25 pounds on right hand on a wooden walking platform for 30 steps/cycles. Motion data were recorded from T1, L1, L3, and S1 vertebrae at a sampling frequency of 120 Hz. Range of Motion (ROM), Correlation Dimension (CoD), and Approximate Entropy (ApEn) was computed using custom written MatLab programs. EMG data were recorded from six muscle groups bilaterally (right and left): Erector Spinae, Multifidus, Latissimus Dorsi, Internal Obliques, External Obliques and Rectus Abdominis at a frequency of 1200 Hz. Root mean square EMG values, Mean and Median Frequency of the EMG data were calculated to observe the effect of increasing load on muscle fatigue using custom developed MatLab program. Ground reaction force (GRF) data were collected using a force plate and the associated 1st peak force (Fz1), 2nd Peak force (Fz3) and minimum force (Fz2) between the two peak forces were calculated during gait cycle. The ROM values had a range from 2.6–3.2 deg. for Lumbar lateral bending (LB), 6.7–8.7 deg. for Thoracic LB. Approximate Entropy (ApEn) values ranged from 0.20–0.40 for Lumbar LB motion and 0.30–0.50 for Thoracic LB motion. Correlation Dimension (CoD) values ranged from 1.20–1.40 for lumbar LB and 1.20–1.30 for Thoracic LB. Normalized GRF increased during walking with increased load. Significance difference (P<0.05) were found for vGRF with increase in load. Motion and EMG data did not reveal any significant differences.

scholarly journals Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Shuan-Feng Zhao ◽  
Wei Guo ◽  
Chuan-wei Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Driver Behavior ◽  
Approximate Entropy ◽  
Driving Behavior ◽  
Steering Wheel ◽  
State Data ◽  
The Road ◽  
Mode Decomposition ◽  
Function Selection ◽  
Vehicle Information

In the driver fatigue monitoring technology, the essence is to capture and analyze the driver behavior information, such as eyes, face, heart, and EEG activity during driving. However, ECG and EEG monitoring are limited by the installation electrodes and are not commercially available. The most common fatigue detection method is the analysis of driver behavior, that is, to determine whether the driver is tired by recording and analyzing the behavior characteristics of steering wheel and brake. The driver usually adjusts his or her actions based on the observed road conditions. Obviously the road path information is directly contained in the vehicle driving state; if you want to judge the driver’s driving behavior by vehicle driving status information, the first task is to remove the road information from the vehicle driving state data. Therefore, this paper proposes an effective intrinsic mode function selection method for the approximate entropy of empirical mode decomposition considering the characteristics of the frequency distribution of road and vehicle information and the unsteady and nonlinear characteristics of the driver closed-loop driving system in vehicle driving state data. The objective is to extract the effective component of the driving behavior information and to weaken the road information component. Finally the effectiveness of the proposed method is verified by simulating driving experiments.

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