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.

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 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.

scholarly journals Multimodal Few-Shot Learning for Gait Recognition

2020 ◽  
Vol 10 (21) ◽  
pp. 7619
Author(s):  
Jucheol Moon ◽  
Nhat Anh Le ◽  
Nelson Hebert Minaya ◽  
Sang-Il Choi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Recognition Problem ◽  
Human Gait ◽  
Training Dataset ◽  
Support Vector ◽  
Open Set ◽  
Latent Space

A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to process gait information for identification purposes. Despite recent advances in gait recognition, an open set gait recognition problem presents challenges to current approaches. To address the open set gait recognition problem, a system should be able to deal with unseen subjects who have not included in the training dataset. In this paper, we propose a system that learns a mapping from a multimodal time series collected using insole to a latent (embedding vector) space to address the open set gait recognition problem. The distance between two embedding vectors in the latent space corresponds to the similarity between two multimodal time series. Using the characteristics of the human gait pattern, multimodal time series are sliced into unit steps. The system maps unit steps to embedding vectors using an ensemble consisting of a convolutional neural network and a recurrent neural network. To recognize each individual, the system learns a decision function using a one-class support vector machine from a few embedding vectors of the person in the latent space, then the system determines whether an unknown unit step is recognized as belonging to a known individual. Our experiments demonstrate that the proposed framework recognizes individuals with high accuracy regardless they have been registered or not. If we could have an environment in which all people would be wearing the insole, the framework would be used for user verification widely.

Sign in / Sign up

Export Citation Format

Share Document