Modeling and classification of gait patterns between anterior cruciate ligament deficient and intact knees based on phase space reconstruction, Euclidean distance and neural networks

Wu, Wenbao; Zeng, Wei; Ma, Limin; Yuan, Chengzhi

doi:10.1186/s12938-018-0594-1

Cited by 8 publications

(8 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the second goal of the paper, the importance of addressing gait pattern classification in biomechanics, and, in particular, in defining which parameters can distinguish between post-ACL subjects from healthy controls is well-known in literature [46][47][48]. While those works relied on gait metrics, recent works published by Wu et al [49] and Richter et al [50] also considered the application of machine learning models for the discrimination between ACL deficient and healthy subjects. Nevertheless, all those studies were carried out in gait laboratories using gold-standard marker-based optoelectronic systems, thus limiting the applicability of those insights to real-world use cases.…”

Section: Discussionmentioning

confidence: 99%

“…However, in both cases [21,22], no machine learning model was developed to discriminate between the two populations of interest. Wu et al [49] built a neural network based on the features extrapolated from the 3D phase space reconstruction of the knee mechanics during the internal-external rotation, and flexion-extension, antero-poster, and proximal-distal translations while walking on a treadmill. On the other hand, Richter et al [50] considered a wide range of biomechanical features, including ground reaction forces and impulses, center of mass velocity and power in pelvis, hip, knee, and ankle, as well as joint angles of the ankle, knee, hip, pelvis, thorax, and thorax on pelvis in sagittal, frontal, and transversal planes, joint angular velocities of the ankle, knee, hip, pelvis, thorax, and thorax on pelvis in sagittal, frontal, and transversal plane, joint powers, moments, work, and impulse of ankle, knee, hip, and pelvis in sagittal, frontal, and transversal plane, time, and the rotation foot angle to pelvis.…”

Section: Discussionmentioning

confidence: 99%

“…It was also observed that double leg countermovement jump and double leg drop jump were the exercises that show the highest accuracy in the discrimination between post-ACL and healthy athletes with 82% and 87% accuracy, respectively. However, Richter et al [50] recruited the post-ACL population only nine months after ACL reconstruction, and both [49,50] relied on optoelectronic systems and force platforms. Finally, Mandalapu et al [24] adopted inertial sensors on the ankles, wrists, and sacrum on athletes while walking and jogging on a treadmill.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

Tedesco

Crowe

Ryan

et al. 2020

Sensors

View full text Add to dashboard Cite

Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5–10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

Tedesco

Crowe

Ryan

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The proposed method was applicated to classification of focal and non-focal EEG signals, and classification of gait patterns between patients with Parkinson's disease and healthy people. Wu et al 39 used Euclidean distance computation to quantify the distribution of points in reconstructed phase space. The method was used to identify gait dynamics between anterior cruciate ligament (ACL) and ACL-intact knee gait patterns.…”

Section: Introductionmentioning

confidence: 99%

Robust fault diagnosis of rolling bearing via phase space reconstruction of intrinsic mode functions and neural network under various operating conditions

Yuan

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

Rolling bearings are important components in mechanical, civil, and aerospace engineering. The practical working conditions of rolling bearings are complex; hence, fault diagnosis of rolling bearings under various operating conditions is very challenging. This paper proposes a novel approach to fault diagnosis of rotary machinery using phase space reconstruction (PSR) of intrinsic mode functions (IMFs) and neural network under various operating conditions. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to decompose vibration signal of rotary component into IMFs denoting high-to-low instantaneous frequencies adaptively. PSR constructs one-dimensional IMFs to high-dimensional IMFs, which helps reveal the underlying nonlinear geometric topology via the reconstructed inherent and hidden dynamical characteristics of the one-dimensional vibration signal. To explore intrinsic dynamical properties, interquartile range (IQR) of Euclidean distance (ED) values of high-dimensional IMFs are extracted as condition indicators and used as input of back propagation (BP) neural network to fulfill fault identification of rolling bearings. The effectiveness and superiority of the proposed approach have been validated by theoretical derivations, numerical simulations and experimental data. The results show that the proposed approach is promising in fault diagnosis of rotary machinery under various operating conditions.

show abstract

“…The method improves the prediction accuracy and robustness. In [15], Wu et al proposed a new classification method to distinguish gait patterns between anterior cruciate ligament deficient and intact knees. The proposed method is based on PSR, Euclidean distance and an RBF neural network, and has a high correct classification rate.…”

mentioning

confidence: 99%

Hybrid prediction method of ECG signals based on VMD, PSR, and RBF neural network

Fu-ying

Qin

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: It is significant for doctors and body area networks (BANs) to predict ECG signals accurately. At present, the prediction accuracy of many existing ECG prediction methods is generally low. In order to improve the prediction accuracy of ECG signals in BANs, a hybrid prediction method of ECG signals is proposed in this paper. Methods: The proposed prediction method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network. First, the embedding dimension and delay time of PSR are calculated according to the trained set of ECG data. Second, the ECG data are decomposed into several intrinsic mode functions (IMFs). Third, the phase space of each IMF is reconstructed according to the embedding dimension and the delay time. Fourth, an RBF neural network is established and each IMF is predicted by the network. Finally, the prediction results of all IMFs are added to realize the final prediction result. Results: To evaluate the prediction performance of the proposed method, simulation experiments are carried out on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the prediction index RMSE (root mean square error) of the proposed method is only 10-3 magnitude and that of some traditional prediction methods is 10-2 magnitude.Conclusions: Compared with some traditional prediction methods, the proposed method improves the prediction accuracy of ECG signals obviously.

show abstract

Modeling and classification of gait patterns between anterior cruciate ligament deficient and intact knees based on phase space reconstruction, Euclidean distance and neural networks

Cited by 8 publications

References 61 publications

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

Robust fault diagnosis of rolling bearing via phase space reconstruction of intrinsic mode functions and neural network under various operating conditions

Hybrid prediction method of ECG signals based on VMD, PSR, and RBF neural network

Contact Info

Product

Resources

About