scholarly journals Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 130 ◽  
Author(s):  
Lim ◽  
Kim ◽  
Park
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Center Of Mass ◽  
Lower Limbs ◽  
Joint Kinetics ◽  
Joint Torques ◽  
Dynamic Relationship ◽  
Reaction Forces ◽  
Artificial Neural Network Ann ◽  
Parameter Values

Recent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or rehabilitation. The number of IMUs and their measurement locations are often determined heuristically, and the rationale underlying the selection of these parameter values is not discussed. Using the dynamic relationship between the center of mass (CoM), the GRFs and joint kinetics, we propose the CoM as a single measurement location with which to predict the dynamic data of the lower limbs, using an ANN. Data from seven subjects walking on a treadmill at various speeds were collected from a single IMU worn near the sacrum. The data was segmented by step and numerically processed for integration. Six segment angles of the stance and swing leg, three joint torques, and two GRFs were estimated from the kinematics of the CoM measured from a single IMU sensor, with fair accuracy. These results indicate the importance of the CoM as a dynamic determinant of multi-segment kinetics during walking. The tradeoff between data quantity and wearable convenience can be solved by utilizing a machine learning algorithm based on the dynamic characteristics of human walking.

scholarly journals Estimation of Three-Dimensional Lower Limb Kinetics Data during Walking Using Machine Learning from a Single IMU Attached to the Sacrum

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6277
Author(s):  
Myunghyun Lee ◽  
Sukyung Park
Keyword(s):  
Center Of Pressure ◽  
Center Of Mass ◽  
Three Dimensional ◽  
Lower Limbs ◽  
Three Dimensions ◽  
Measurement Unit ◽  
Ann Model ◽  
Joint Torques ◽  
Reaction Forces ◽  
Mean Square Errors

Kinetics data such as ground reaction forces (GRFs) are commonly used as indicators for rehabilitation and sports performance; however, they are difficult to measure with convenient wearable devices. Therefore, researchers have attempted to estimate accurately unmeasured kinetics data with artificial neural networks (ANNs). Because the inputs to an ANN affect its performance, they must be carefully selected. The GRF and center of pressure (CoP) have a mechanical relationship with the center of mass (CoM) in the three dimensions (3D). This biomechanical characteristic can be used to establish an appropriate input and structure of an ANN. In this study, an ANN for estimating gait kinetics with a single inertial measurement unit (IMU) was designed; the kinematics of the IMU placed on the sacrum as a proxy for the CoM kinematics were applied based on the 3D spring mechanics. The walking data from 17 participants walking at various speeds were used to train and validate the ANN. The estimated 3D GRF, CoP trajectory, and joint torques of the lower limbs were reasonably accurate, with normalized root-mean-square errors (NRMSEs) of 6.7% to 15.6%, 8.2% to 20.0%, and 11.4% to 24.1%, respectively. This result implies that the biomechanical characteristics can be used to estimate the complete three-dimensional gait data with an ANN model and a single IMU.

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 Multi-Step-Ahead Forecasting of Wave Conditions Based on a Physics-Based Machine Learning (PBML) Model for Marine Operations

2020 ◽  
Vol 8 (12) ◽  
pp. 992
Author(s):  
Mengning Wu ◽  
Christos Stefanakos ◽  
Zhen Gao
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
High Reliability ◽  
Computational Cost ◽  
Model Performance ◽  
The North ◽  
The North Sea ◽  
Wave Conditions ◽  
Wave Models ◽  
Artificial Neural Network Ann

Short-term wave forecasts are essential for the execution of marine operations. In this paper, an efficient and reliable physics-based machine learning (PBML) model is proposed to realize the multi-step-ahead forecasting of wave conditions (e.g., significant wave height Hs and peak wave period Tp). In the model, the primary variables in physics-based wave models (i.e., the wind forcing and initial wave boundary condition) are considered as inputs. Meanwhile, a machine learning algorithm (artificial neural network, ANN) is adopted to build an implicit relation between inputs and forecasted outputs of wave conditions. The computational cost of this data-driven model is obviously much lower than that of the differential-equation based physical model. A ten-year (from 2001 to 2010) dataset of every three hours at the North Sea center was used to assess the model performance in a small domain. The result reveals high reliability for one-day-ahead Hs forecasts, while that of Tp is slightly lower due to the weaker implicit relationships between the data. Overall, the PBML model can be conceived as an efficient tool for the multi-step-ahead forecasting of wave conditions, and thus has great potential for furthering assist decision-making during the execution of marine operations.

Pulomonary Nodule Classification From CT Scan Images Using Machine Learning Method

2021 ◽  
pp. 99-115
Author(s):  
Rekha K. V. ◽  
Anirudh Itagi ◽  
Bharath K. P. ◽  
Balaji Subramanian ◽  
Rajesh Kumar M.
Keyword(s):  
Machine Learning ◽  
Ct Scan ◽  
Learning Algorithm ◽  
Research Work ◽  
Early Period ◽  
Pulmonary Nodules ◽  
Artificial Neural Network Ann ◽  
Benign Nodules ◽  
Occurrence Matrix ◽  
Nodule Classification

The research work is to enhance the classification accuracy of the pulmonary nodules with the limited number of features extracted using Gray level co-occurrence matrix and linear binary pattern. The classification is done using the machine learning algorithm such as artificial neural network (ANN) and the random forest classifier (RF). In present, lung cancer seems to be the most deadly disease in the world which can be detected only after the computerized tomography (i.e., CT scan images of the person). Detecting the infected portion at the early period is the challenging task. Hence, the recent researchers where under the detection of pulmonary nodules to categorize it either as benign nodules which named as non-cancerous or as malignant nodules which are named as cancerous. When associated the results with the recent papers, the accuracy has been improved in classifying the lung nodules.

scholarly journals Joint Kinetics and Kinematics During Common Lower Limb Rehabilitation Exercises

2015 ◽  
Vol 50 (10) ◽  
pp. 1011-1018 ◽  
Author(s):  
Paul Comfort ◽  
Paul Anthony Jones ◽  
Laura Constance Smith ◽  
Lee Herrington
Keyword(s):  
Lower Limbs ◽  
Ground Reaction Forces ◽  
Joint Moments ◽  
Joint Kinetics ◽  
3 Dimensional ◽  
Reaction Forces ◽  
Vertical Ground ◽  
Vertical Ground Reaction Forces ◽  
Limb Rehabilitation ◽  
Rehabilitation Exercises

Context  Unilateral body-weight exercises are commonly used to strengthen the lower limbs during rehabilitation after injury, but data comparing the loading of the limbs during these tasks are limited. Objective  To compare joint kinetics and kinematics during 3 commonly used rehabilitation exercises. Design  Descriptive laboratory study. Setting  Laboratory. Patients or Other Participants  A total of 9 men (age = 22.1 ± 1.3 years, height = 1.76 ± 0.08 m, mass = 80.1 ± 12.2 kg) participated. Intervention(s)  Participants performed the single-legged squat, forward lunge, and reverse lunge with kinetic data captured via 2 force plates and 3-dimensional kinematic data collected using a motion-capture system. Main Outcome Measure(s)  Peak ground reaction forces, maximum joint angles, and peak sagittal-joint moments. Results  We observed greater eccentric and concentric peak vertical ground reaction forces during the single-legged squat than during both lunge variations (P ≤ .001). Both lunge variations demonstrated greater knee and hip angles than did the single-legged squat (P < .001), but we observed no differences between lunges (P > .05). Greater dorsiflexion occurred during the single-legged squat than during both lunge variations (P < .05), but we noted no differences between lunge variations (P = .70). Hip-joint moments were greater during the forward lunge than during the reverse lunge (P = .003) and the single-legged squat (P = .011). Knee-joint moments were greater in the single-legged squat than in the reverse lunge (P < .001) but not greater in the single-legged squat than in the forward lunge (P = .41). Ankle-joint moments were greater during the single-legged squat than during the forward lunge (P = .002) and reverse lunge (P < .001). Conclusions  Appropriate loading progressions for the hip should begin with the single-legged squat and progress to the reverse lunge and then the forward lunge. In contrast, loading progressions for the knee and ankle should begin with the reverse lunge and progress to the forward lunge and then the single-legged squat.

scholarly journals Using Machine Learning to Predict the Fuel Peak Cladding Temperature for a Large Break Loss of Coolant Accident

2021 ◽  
Vol 9 ◽  
Author(s):  
Wazif Sallehhudin ◽  
Aya Diab
Keyword(s):  
Machine Learning ◽  
Uncertainty Quantification ◽  
Mean Squared Error ◽  
Learning Algorithm ◽  
Severe Accidents ◽  
Loss Of Coolant Accident ◽  
Lumped Parameter ◽  
Loss Of Coolant ◽  
Artificial Neural Network Ann ◽  
Uncertainty Parameters

In this paper the use of machine learning (ML) is explored as an efficient tool for uncertainty quantification. A machine learning algorithm is developed to predict the peak cladding temperature (PCT) under the conditions of a large break loss of coolant accident given the various underlying uncertainties. The best estimate approach is used to simulate the thermal-hydraulic system of APR1400 large break loss of coolant accident (LBLOCA) scenario using the multidimensional reactor safety analysis code (MARS-KS) lumped parameter system code developed by Korea Atomic Energy Research Institute (KAERI). To generate the database necessary to train the ML model, a set of uncertainty parameters derived from the phenomena identification and ranking table (PIRT) is propagated through the thermal hydraulic model using the Dakota-MARS uncertainty quantification framework. The developed ML model uses the database created by the uncertainty quantification framework along with Keras library and Talos optimization to construct the artificial neural network (ANN). After learning and validation, the ML model can predict the peak cladding temperature (PCT) reasonably well with a mean squared error (MSE) of ∼0.002 and R2 of ∼0.9 with 9 to 11 key uncertain parameters. As a bounding accident scenario analysis of the LBLOCA case paves the way to using machine learning as a decision making tool for design extension conditions as well as severe accidents.

scholarly journals Dependent-Gaussian-Process-Based Learning of Joint Torques Using Wearable Smart Shoes for Exoskeleton

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3685
Author(s):  
Jiantao Yang ◽  
Yuehong Yin
Keyword(s):  
Gaussian Process ◽  
Learning Algorithm ◽  
Low Cost ◽  
Joint Kinematics ◽  
Kernel Functions ◽  
Lower Limbs ◽  
Joint Torque ◽  
Human Gait ◽  
Joint Torques ◽  
Composite Kernel

Estimating the joint torques of lower limbs in human gait is a highly challenging task and of great significance in developing high-level controllers for lower-limb exoskeletons. This paper presents a dependent Gaussian process (DGP)-based learning algorithm for joint-torque estimations with measurements from wearable smart shoes. The DGP was established to perform data fusion, and serves as the mathematical foundation to explore the correlations between joint kinematics and joint torques that are embedded deeply in the data. As joint kinematics are used in the training phase rather than the prediction process, the DGP model can realize accurate predictions in outdoor activities by using only the smart shoe, which is low-cost, nonintrusive for human gait, and comfortable to wearers. The design methodology of dynamic specific kernel functions is presented in accordance to prior knowledge of the measured signals. The designed composite kernel functions can be used to model multiple features at different scales, and cope with the temporal evolution of human gait. The statistical nature of the proposed DGP model and the composite kernel functions offer superior flexibility for time-varying gait-pattern learning, and enable accurate joint-torque estimations. Experiments were conducted with five subjects, whose results showed that it is possible to estimate joint torques under different trained and untrained speed levels. Comparisons were made between the proposed DGP and Gaussian process (GP) models. Obvious improvements were achieved when all DGP r2 values were higher than those of GP.

scholarly journals Comparison of Countermovement and Preferred-Style Jump Biomechanics in Male Basketball Players

2021 ◽  
Vol 11 (13) ◽  
pp. 6092
Author(s):  
Kristof Kipp ◽  
John Krzyszkowski ◽  
Todd Smith ◽  
Christopher Geiser ◽  
Hoon Kim
Keyword(s):  
Center Of Mass ◽  
Force Plate ◽  
Movement Pattern ◽  
Cross Sectional Study ◽  
Basketball Players ◽  
Cross Sectional ◽  
Joint Kinetics ◽  
Reaction Forces ◽  
Vertical Ground ◽  
Vertical Ground Reaction Forces

The purpose of this study was to investigate and compare the biomechanics of countermovement (CMJ) and preferred-style (PrefJ) jumps. Eight male basketball players (age: 19 ± 1 year; height: 1.84 ± 0.14 m; mass: 92.8 ± 11.4 kg) participated in a cross-sectional study for which they performed max effort CMJ and PrefJ while motion capture and force plate data were recorded. The CMJ were performed according to common procedures. For the PrefJ, the eight players chose to use a short approach run and a step-in jump, with a clear lead and trail leg foot contact pattern. Vertical ground reaction forces (GRF), center-of-mass (COM) parameters, as well as hip, knee, and ankle flexion angles, extension velocities, net joint moments, powers, and work were all calculated and used for analysis. Bi-lateral data from the CMJ were averaged, whereas lead and trail leg data from the PrefJ were kept separated. The PrefJ was characterized by greater jump height and GRF and shorter contact times. Joint-level differences indicated that the PrefJ was characterized by larger joint kinetics. Importantly, very few biomechanical variables of the CMJ and PrefJ were correlated, which suggests that each jump type is characterized by unique movement strategies. Since PrefJ may better represent athlete- and sport-specific movement pattern, these findings could have implications for assessing and monitoring neuromuscular performance of basketball players.

scholarly journals Machine Learning Cases in Clinical and Biomedical Domains

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Mohd Zulfaezal Che Azemin ◽  
Tijani Ahmed Ashimi ◽  
Md Muziman Syah
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
University Of California ◽  
General Audience ◽  
Supervised And Unsupervised Learning ◽  
Hypothetical Data ◽  
Artificial Neural Network Ann ◽  
Applications Of Machine Learning ◽  
Compare And Contrast ◽  
Learning Concept

The aim of this paper is twofold: Firstly, to provide introductory knowledge to the reader who has little or no knowledge of machine learning with examples of applications in clinical and biomedical domains, and secondly, to compare and contrast the concept of Artificial Neural Network (ANN) and the Qur’anic concept of intellect (aql) in the Qur’an. Learning algorithm can generally be categorised into supervised and unsupervised learning. To better understand the machine learning concept, hypothetical data of glaucoma cases are presented. ANN is then selected as an example of supervised learning and the underlying principles in ANN are presented with general audience in mind with an attempt to relate the mechanism employed in the algorithm with Qur’anic verses containing the verbs derived from aql. The applications of machine learning in clinical and biomedical domains are briefly demonstrated based on the author’s own research and most recent examples available from University of California, Irvine Machine Learning Repository. Selected verses which indicate motivation to use the intellect in positive manners and rebuke to those who do not activate the intellect are presented. The evidence found from the verses suggests that ANN shares similar learning process to achieve belief (iman) by analysing the similitudes (amsal) introduced to the algorithm.

scholarly journals Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury

2018 ◽  
Vol 45 (5) ◽  
pp. E2 ◽  
Author(s):  
Andrew T. Hale ◽  
David P. Stonko ◽  
Amber Brown ◽  
Jaims Lim ◽  
David J. Voce ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Learning Algorithm ◽  
Classification Systems ◽  
Artificial Neural Network Ann ◽  
Clinical Records ◽  
Ct Classification ◽  
Based Medicine ◽  
Learning Analysis

OBJECTIVEModern surgical planning and prognostication requires the most accurate outcomes data to practice evidence-based medicine. For clinicians treating children following traumatic brain injury (TBI) these data are severely lacking. The first aim of this study was to assess published CT classification systems in the authors’ pediatric cohort. A pediatric-specific machine-learning algorithm called an artificial neural network (ANN) was then created that robustly outperformed traditional CT classification systems in predicting TBI outcomes in children.METHODSThe clinical records of children under the age of 18 who suffered a TBI and underwent head CT within 24 hours after TBI (n = 565) were retrospectively reviewed.RESULTS“Favorable” outcome (alive with Glasgow Outcome Scale [GOS] score ≥ 4 at 6 months postinjury, n = 533) and “unfavorable” outcome (death at 6 months or GOS score ≤ 3 at 6 months postinjury, n = 32) were used as the primary outcomes. The area under the receiver operating characteristic (ROC) curve (AUC) was used to delineate the strength of each CT grading system in predicting survival (Helsinki, 0.814; Rotterdam, 0.838; and Marshall, 0.781). The AUC for CT score in predicting GOS score ≤ 3, a measure of overall functionality, was similarly predictive (Helsinki, 0.717; Rotterdam, 0.748; and Marshall, 0.663). An ANN was then constructed that was able to predict 6-month outcomes with profound accuracy (AUC = 0.9462 ± 0.0422).CONCLUSIONSThis study showed that machine-learning can be leveraged to more accurately predict TBI outcomes in children.

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