Spring-loaded inverted pendulum modeling improves neural network estimation of ground reaction forces

2020 ◽  
Vol 113 ◽  
pp. 110069
Author(s):  
Bumjoon Kim ◽  
Hyerim Lim ◽  
Sukyung Park
Keyword(s):  
Neural Network ◽  
Inverted Pendulum ◽  
Ground Reaction Forces ◽  
Reaction Forces ◽  
Network Estimation ◽  
Spring Loaded Inverted Pendulum

scholarly journals Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7709
Author(s):  
Serena Cerfoglio ◽  
Manuela Galli ◽  
Marco Tarabini ◽  
Filippo Bertozzi ◽  
Chiarella Sforza ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Knee Joint ◽  
Field Analysis ◽  
Ground Reaction Forces ◽  
Drop Jump ◽  
Joint Moments ◽  
Data Set ◽  
Joint Kinetics ◽  
Reaction Forces

Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.

Predicting Complete Ground Reaction Forces and Moments During Gait With Insole Plantar Pressure Information Using a Wavelet Neural Network

2015 ◽  
Vol 137 (9) ◽  
Author(s):  
Taeyong Sim ◽  
Hyunbin Kwon ◽  
Seung Eel Oh ◽  
Su-Bin Joo ◽  
Ahnryul Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Plantar Pressure ◽  
Prediction Models ◽  
Pressure Sensors ◽  
Principal Component ◽  
Wavelet Neural Network ◽  
Human Gait ◽  
Ground Reaction Forces ◽  
Reaction Forces

In general, three-dimensional ground reaction forces (GRFs) and ground reaction moments (GRMs) that occur during human gait are measured using a force plate, which are expensive and have spatial limitations. Therefore, we proposed a prediction model for GRFs and GRMs, which only uses plantar pressure information measured from insole pressure sensors with a wavelet neural network (WNN) and principal component analysis-mutual information (PCA-MI). For this, the prediction model estimated GRFs and GRMs with three different gait speeds (slow, normal, and fast groups) and healthy/pathological gait patterns (healthy and adolescent idiopathic scoliosis (AIS) groups). Model performance was validated using correlation coefficients (r) and the normalized root mean square error (NRMSE%) and was compared to the prediction accuracy of the previous methods using the same dataset. As a result, the performance of the GRF and GRM prediction model proposed in this study (slow group: r = 0.840–0.989 and NRMSE% = 10.693–15.894%; normal group: r = 0.847–0.988 and NRMSE% = 10.920–19.216%; fast group: r = 0.823–0.953 and NRMSE% = 12.009–20.182%; healthy group: r = 0.836–0.976 and NRMSE% = 12.920–18.088%; and AIS group: r = 0.917–0.993 and NRMSE% = 7.914–15.671%) was better than that of the prediction models suggested in previous studies for every group and component (p < 0.05 or 0.01). The results indicated that the proposed model has improved performance compared to previous prediction models.

scholarly journals Individuality decoded by running patterns: Movement characteristics that determine the uniqueness of human running

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249657
Author(s):  
Fabian Hoitz ◽  
Vinzenz von Tscharner ◽  
Jennifer Baltich ◽  
Benno M. Nigg
Keyword(s):  
Neural Network ◽  
Sagittal Plane ◽  
Gait Recognition ◽  
Transverse Plane ◽  
Human Gait ◽  
Ground Reaction Forces ◽  
Movement Trajectories ◽  
Gait Characteristics ◽  
The Neural Network ◽  
Reaction Forces

Human gait is as unique to an individual as is their fingerprint. It remains unknown, however, what gait characteristics differentiate well between individuals that could define the uniqueness of human gait. The purpose of this work was to determine the gait characteristics that were most relevant for a neural network to identify individuals based on their running patterns. An artificial neural network was trained to recognize kinetic and kinematic movement trajectories of overground running from 50 healthy novice runners (males and females). Using layer-wise relevance propagation, the contribution of each variable to the classification result of the neural network was determined. It was found that gait characteristics of the coronal and transverse plane as well as medio-lateral ground reaction forces provided more information for subject identification than gait characteristics of the sagittal plane and ground reaction forces in vertical or anterior-posterior direction. Additionally, gait characteristics during the early stance were more relevant for gait recognition than those of the mid and late stance phase. It was concluded that the uniqueness of human gait is predominantly encoded in movements of the coronal and transverse plane during early stance.

scholarly journals Stability in skipping gaits

2016 ◽  
Vol 3 (11) ◽  
pp. 160602 ◽  
Author(s):  
Emanuel Andrada ◽  
Roy Müller ◽  
Reinhard Blickhan
Keyword(s):  
Numerical Simulations ◽  
Inverted Pendulum ◽  
Ground Reaction Forces ◽  
Fast Running ◽  
Leg Stiffness ◽  
Adult Humans ◽  
Speed Range ◽  
Reaction Forces ◽  
System Energy ◽  
Male Subjects

As an alternative to walking and running, humans are able to skip. However, adult humans avoid it. This fact seems to be related to the higher energetic costs associated with skipping. Still, children, some birds, lemurs and lizards use skipping gaits during daily locomotion. We combined experimental data on humans with numerical simulations to test whether stability and robustness motivate this choice. Parameters for modelling were obtained from 10 male subjects. They locomoted using unilateral skipping along a 12 m runway. We used a bipedal spring loaded inverted pendulum to model and to describe the dynamics of skipping. The subjects displayed higher peak ground reaction forces and leg stiffness in the first landing leg (trailing leg) compared to the second landing leg (leading leg). In numerical simulations, we found that skipping is stable across an amazing speed range from skipping on the spot to fast running speeds. Higher leg stiffness in the trailing leg permits longer strides at same system energy. However, this strategy is at the same time less robust to sudden drop perturbations than skipping with a stiffer leading leg. A slightly higher stiffness in the leading leg is most robust, but might be costlier.

Ground Reaction Force Reduction of Biped Robot for Walking Along a Step with Dual Length Linear Inverted Pendulum Method

2013 ◽  
Vol 25 (1) ◽  
pp. 220-231 ◽  
Author(s):  
Fariz Ali ◽  
◽  
Naoki Motoi ◽  
Kirill Van Heerden ◽  
Atsuo Kawamura
Keyword(s):  
Inverted Pendulum ◽  
Reaction Force ◽  
Biped Robot ◽  
Ground Reaction Forces ◽  
Bipedal Robot ◽  
Impact Forces ◽  
Reaction Forces ◽  
Left And Right ◽  
Newton Raphson ◽  
Force Reduction

A bipedal robot should be robust and able to move in various directions on stairs. However, up to date many research studies have been focusing on walking in the up or down direction only. Therefore, a strategy to realize walking along a step is investigated. In conventional methods, CoM is moved up or down during walking in this situation. In this paper, a method named as Dual Length Linear Inverted Pendulum Method (DLLIPM) with Newton-Raphson is proposed for 3-D biped robot walking. The proposed method applies different length of pendulum at left and right legs in order to represent the CoM height. By using the proposed method, maximum impact forces are reduced. From the Ground Reaction Forces (GRF) data obtained in the simulations, the validity of the proposed method is confirmed.

scholarly journals Frequency-dependent force direction elucidates neural control of balance

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Kaymie Shiozawa ◽  
Jongwoo Lee ◽  
Marta Russo ◽  
Dagmar Sternad ◽  
Neville Hogan
Keyword(s):  
Neural Control ◽  
Inverted Pendulum ◽  
Control Method ◽  
Control Strategies ◽  
Quiet Standing ◽  
Assistive Device ◽  
Ground Reaction Forces ◽  
Control Effort ◽  
Frequency Dependent ◽  
Reaction Forces

Abstract Background Maintaining upright posture is an unstable task that requires sophisticated neuro-muscular control. Humans use foot–ground interaction forces, characterized by point of application, magnitude, and direction to manage body accelerations. When analyzing the directions of the ground reaction forces of standing humans in the frequency domain, previous work found a consistent pattern in different frequency bands. To test whether this frequency-dependent behavior provided a distinctive signature of neural control or was a necessary consequence of biomechanics, this study simulated quiet standing and compared the results with human subject data. Methods Aiming to develop the simplest competent and neuromechanically justifiable dynamic model that could account for the pattern observed across multiple subjects, we first explored the minimum number of degrees of freedom required for the model. Then, we applied a well-established optimal control method that was parameterized to maximize physiologically-relevant insight to stabilize the balancing model. Results If a standing human was modeled as a single inverted pendulum, no controller could reproduce the experimentally observed pattern. The simplest competent model that approximated a standing human was a double inverted pendulum with torque-actuated ankle and hip joints. A range of controller parameters could stabilize this model and reproduce the general trend observed in experimental data; this result seems to indicate a biomechanical constraint and not a consequence of control. However, details of the frequency-dependent pattern varied substantially across tested control parameter values. The set of parameters that best reproduced the human experimental results suggests that the control strategy employed by human subjects to maintain quiet standing was best described by minimal control effort with an emphasis on ankle torque. Conclusions The findings suggest that the frequency-dependent pattern of ground reaction forces observed in quiet standing conveys quantitative information about human control strategies. This study’s method might be extended to investigate human neural control strategies in different contexts of balance, such as with an assistive device or in neurologically impaired subjects.

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