Characterization of human gait by means of body center of mass oscillations derived from ground reaction forces

1995 ◽  
Vol 42 (3) ◽  
pp. 293-303 ◽  
Author(s):  
A. Crowe ◽  
P. Schiereck ◽  
R.W. de Boer ◽  
W. Keessen
Keyword(s):  
Center Of Mass ◽  
Human Gait ◽  
Ground Reaction Forces ◽  
Reaction Forces ◽  
Body Center

scholarly journals Ground reaction forces intersect above the center of mass even when walking down visible and camouflaged curbs

2019 ◽  
Vol 222 (14) ◽  
pp. jeb204305 ◽  
Author(s):  
Johanna Vielemeyer ◽  
Eric Grießbach ◽  
Roy Müller
Keyword(s):  
Center Of Mass ◽  
Ground Reaction Forces ◽  
Reaction Forces

scholarly journals The effect of walking speed on the foot inter-segment kinematics, ground reaction forces and lower limb joint moments

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5517 ◽  
Author(s):  
Dong Sun ◽  
Gusztáv Fekete ◽  
Qichang Mei ◽  
Yaodong Gu
Keyword(s):  
Lower Limb ◽  
Walking Speed ◽  
Sagittal Plane ◽  
External Rotation ◽  
Center Of Mass ◽  
Ground Reaction Forces ◽  
Joint Moments ◽  
Angle Variation ◽  
Foot Kinematics ◽  
Reaction Forces

Background Normative foot kinematic and kinetic data with different walking speeds will benefit rehabilitation programs and improving gait performance. The purpose of this study was to analyze foot kinematics and kinetics differences between slow walking (SW), normal walking (NW) and fast walking (FW) of healthy subjects. Methods A total of 10 healthy male subjects participated in this study; they were asked to carry out walks at a self-selected speed. After measuring and averaging the results of NW, the subjects were asked to perform a 25% slower and 25% faster walk, respectively. Temporal-spatial parameters, kinematics of the tibia (TB), hindfoot (HF), forefoot (FF) and hallux (HX), and ground reaction forces (GRFs) were recorded while the subjects walked at averaged speeds of 1.01 m/s (SW), 1.34 m/s (NW), and 1.68 m/s (FW). Results Hindfoot relative to tibia (HF/TB) and forefoot relative to hindfoot (FF/HF) dorsiflexion (DF) increased in FW, while hallux relative to forefoot (HX/FF) DF decreased. Increased peak eversion (EV) and peak external rotation (ER) in HF/TB were observed in FW with decreased peak supination (SP) in FF/HF. GRFs were increased significantly with walking speed. The peak values of the knee and ankle moments in the sagittal and frontal planes significantly increased during FW compared with SW and NW. Discussion Limited HF/TB and FF/HF motion of SW was likely compensated for increased HX/FF DF. Although small angle variation in HF/TB EV and FF/HF SP during FW may have profound effects for foot kinetics. Higher HF/TB ER contributed to the FF push-off the ground while the center of mass (COM) progresses forward in FW, therefore accompanied by higher FF/HF abduction in FW. Increased peak vertical GRF in FW may affected by decreased stance duration time, the biomechanical mechanism maybe the change in vertical COM height and increase leg stiffness. Walking speed changes accompanied with modulated sagittal plane ankle moments to alter the braking GRF during loading response. The findings of foot kinematics, GRFs, and lower limb joint moments among healthy males may set a reference to distinguish abnormal and pathological gait patterns.

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 Effect of Horizontal Ground Reaction Forces during the Golf Swing: Implications for the Development of Technical Solutions of Golf Swing Analysis

Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 45
Author(s):  
Maxime Bourgain ◽  
Christophe Sauret ◽  
Grégoire Prum ◽  
Laura Valdes-Tamayo ◽  
Olivier Rouillon ◽  
...  
Keyword(s):  
Center Of Mass ◽  
Field Performance ◽  
Golf Swing ◽  
Ground Reaction Forces ◽  
Technological Developments ◽  
Reaction Forces ◽  
Analysis Laboratory ◽  
Technical Solutions ◽  
Vertical Ground Reaction Forces ◽  
Full Body

The swing is a key movement for golf. Its in-field performance could be estimated by embedded technologies, but often only vertical ground reaction forces (VGRF) are estimated. However, as the swing plane is inclined, horizontal ground reaction forces (HGRF) are expected to contribute to the increase of the club angular velocity. Thus, this study aimed at investigating the role of the HGRF during the golf swing. Twenty-eight golf players were recruited and performed 10 swings with their own driver club, in a motion analysis laboratory, equipped with a full body marker set. Ground reaction forces (GRF) were measured with force-plates. A multibody kinematic optimization was performed with a full body model to estimate the instantaneous location of the golfer’s center of mass (CoM). Moments created by the GRF at the CoM were investigated. Results showed that horizontal forces should not be neglected regarding to VGRF because of their lever arm. Analyzing golf swing with only VGRF appeared not enough and further technological developments are still needed to ecologically measure other components.

Optimization-based subject-specific planar human vertical jumping prediction: Effect of elbow flexion and weighted vest

2021 ◽  
pp. 095441192110440
Author(s):  
Juan Baus ◽  
John R Harry ◽  
James Yang
Keyword(s):  
Center Of Mass ◽  
Elbow Flexion ◽  
The Body ◽  
Loading Conditions ◽  
Arm Swing ◽  
Design Variables ◽  
Vertical Jumping ◽  
Reaction Forces ◽  
Body Center ◽  
Mass Position

Jumping strategies differ considerably depending on athletes’ physical activity demands. In general, the jumping motion is desired to have excellent performance and low injury risk. Both of these outcomes can be achieved by modifying athletes’ jumping and landing mechanics. This paper presents a consecutive study on the optimization-based subject-specific planar human vertical jumping to test different loading conditions (weighted vest) during jumping with or without elbow flexion during the arm-swing based on the validated prediction model in the first part of this study. The sagittal plane skeletal model simulates the weighting, unweighting, breaking, propulsion phases and considers four loading conditions: 0%, 5%, 10%, and 15% body weight. Results show that the maximum ground reaction forces, the body center of mass position, and velocities at the take-off instant are different for different loading conditions and with/without elbow flexion. The optimization formulation is solved using MATLAB® with 35 design variables with 197 nonlinear constraints for a five-segment body model and 42 design variables with 227 nonlinear constraints for a six-segment body model. Both models are computationally efficient, and they can predict ground reaction forces, the body center of mass position, and velocity. This work is novel in the sense that presents a simulation model capable of considering different external loading conditions and the effect of elbow flexion during arm swing.

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.

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