scholarly journals Using Ground Reaction Forces To Predict Fatigue In Treadmill Running: A Machine Learning Approach

2021 ◽  
Author(s):  
Sérgio Baldo Junior ◽  
Thiago Faria dos Santos ◽  
Renato Tinós ◽  
Paulo Roberto Pereira Santiago
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Good Accuracy ◽  
Low Cost ◽  
Treadmill Running ◽  
Ground Reaction Forces ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
Reaction Forces ◽  
Interesting Alternative

Abstract The analysis of running patterns, especially those associated with fatigue, can help specialists in designing more efficient workouts and preventing injuries in high-performance sports. However, classifying running patterns is not trivial for humans. An interesting alternative is to use Machine Learning methods, such as Artificial Neural Networks (ANNs), to classify running patterns. In this work, ground reaction forces are measured by sensors coupled to the base of a low-cost open-source treadmill. ANNs are used to classify the force signals and to indicate the occurrence of fatigue. Different features, extracted from the force signals, are proposed and investigated. A Genetic Algorithm (GA) is used to select the best features. The experimental results indicate that the ANN is able to classify the running patterns with good accuracy. In addition, some features selected by the GA provide important information regarding the identification of fatigue in treadmill running.

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.

Using AI at the Edge and Incremental Machine Learning to Process Onboard Instrument Data

2021 ◽  
Author(s):  
Nicholas Parkyn
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Real Time ◽  
Data Storage ◽  
High Performance ◽  
Heterogeneous Computing ◽  
Low Cost ◽  
Machine Intelligence ◽  
Edge Computing ◽  
Continual Learning

Emerging heterogeneous computing, computing at the edge, machine learning and AI at the edge technology drives approaches and techniques for processing and analysing onboard instrument data in near real-time. The author has used edge computing and neural networks combined with high performance heterogeneous computing platforms to accelerate AI workloads. Heterogeneous computing hardware used is readily available, low cost, delivers impressive AI performance and can run multiple neural networks in parallel. Collecting, processing and machine learning from onboard instruments data in near real-time is not a trivial problem due to data volumes, complexities of data filtering, data storage and continual learning. Little research has been done on continual machine learning which aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn from a non-stationary and never-ending stream of data. The author has applied the concept of continual learning to building a system that continually learns from actual boat performance and refines predictions previously done using static VPP data. The neural networks used are initially trained using the output from traditional VPP software and continue to learn from actual data collected under real sailing conditions. The author will present the system design, AI, and edge computing techniques used and the approaches he has researched for incremental training to realise continual learning.

A reverse design model for high-performance and low-cost magnesium alloys by machine learning

2022 ◽  
Vol 201 ◽  
pp. 110881
Author(s):  
Xiaoxi Mi ◽  
Lianjuan Tian ◽  
Aitao Tang ◽  
Jing Kang ◽  
Peng Peng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnesium Alloys ◽  
High Performance ◽  
Low Cost ◽  
Design Model

scholarly journals To switch or not to switch – a machine learning approach for ferroelectricity

2020 ◽  
Vol 2 (5) ◽  
pp. 2063-2072 ◽  
Author(s):  
Sabine M. Neumayer ◽  
Stephen Jesse ◽  
Gabriel Velarde ◽  
Andrei L. Kholkin ◽  
Ivan Kravchenko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Measured Signal ◽  
Learning Approach ◽  
Two Dimensional ◽  
Dimensional Representation ◽  
Machine Learning Methods ◽  
Machine Learning Approach ◽  
Signal Dependence ◽  
Two Parameter

The introduced two-dimensional representation of two-parameter signal dependence allows for clear interpretation and classification of the measured signal upon using machine learning methods.

scholarly journals Effects of Velocity and Weight Support on Ground Reaction Forces and Metabolic Power during Running

2008 ◽  
Vol 24 (3) ◽  
pp. 288-297 ◽  
Author(s):  
Alena M. Grabowski ◽  
Rodger Kram
Keyword(s):  
Body Weight ◽  
Normal Weight ◽  
Treadmill Running ◽  
Ground Reaction Forces ◽  
Metabolic Power ◽  
Data Set ◽  
Weight Support ◽  
Scientific Goal ◽  
Reaction Forces ◽  
Support Body Weight

The biomechanical and metabolic demands of human running are distinctly affected by velocity and body weight. As runners increase velocity, ground reaction forces (GRF) increase, which may increase the risk of an overuse injury, and more metabolic power is required to produce greater rates of muscular force generation. Running with weight support attenuates GRFs, but demands less metabolic power than normal weight running. We used a recently developed device (G-trainer) that uses positive air pressure around the lower body to support body weight during treadmill running. Our scientific goal was to quantify the separate and combined effects of running velocity and weight support on GRFs and metabolic power. After obtaining this basic data set, we identified velocity and weight support combinations that resulted in different peak GRFs, yet demanded the same metabolic power. Ideal combinations of velocity and weight could potentially reduce biomechanical risks by attenuating peak GRFs while maintaining aerobic and neuromuscular benefits. Indeed, we found many combinations that decreased peak vertical GRFs yet demanded the same metabolic power as running slower at normal weight. This approach of manipulating velocity and weight during running may prove effective as a training and/or rehabilitation strategy.

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