Support vector machines can classify runner’s ability using wearable sensor data from a variety of anatomical locations

We developed and tested an algorithm to automatically classify twenty runners as novice or experienced based on their technique. Linear accelerations and angular velocities collected from six common wearable sensor locations were used to train support vector machine classifiers. The model using input data from all six sensors achieved a classification accuracy of 98.5% (10 km/h running). The classification performance of models based on single sensor data showed a 56.3-94.5% accuracy range, with sensors from the upper body giving the best results. Comparisons of kinematic variables between the two populations confirmed significant differences in upper body biomechanics throughout the stride, thus showing applied potential when aiming to compare novice runner’s technique with movement patterns more akin to those with greater experience

Support vector machines can classify runner’s ability using wearable sensor data from a variety of anatomical locations

We developed and tested an algorithm to automatically classify twenty runners as novice or experienced based on their technique. Linear accelerations and angular velocities collected from six common wearable sensor locations were used to train support vector machine classifiers. The model using input data from all six sensors achieved a classification accuracy of 98.5% (10 km/h running). The classification performance of models based on single sensor data showed a 56.3-94.5% accuracy range, with sensors from the upper body giving the best results. Comparisons of kinematic variables between the two populations confirmed significant differences in upper body biomechanics throughout the stride, thus showing applied potential when aiming to compare novice runner’s technique with movement patterns more akin to those with greater experience

The effects of focus of attention on the learning of the clean weightlifting technique in novices

The purpose of this study was to assess the effects of different focus of attention (FOA) feedback on the learning of the clean weightlifting technique. Sixteen participants naive to the task were divided into two groups (external and internal FOA) and attended three sessions of learning. Bar and lifter kinematics were measured prior to and after learning, and retention and transfer tests were performed seven days after protocol completion. Internal FOA feedback promoted greater reduction of the distance between the bar and the lifter’s body, whereas both types of feedback were found to be equally effective at modifying hip, knee and ankle flexion/extension patterns in the pulling phases of the lift. There seems to be insufficient evidence to advise coaches to choose one type of FOA feedback exclusively, as each approach could target different needs/stages of learning

Development and validation of FootNet, a new kinematic and deep learning-based algorithm to detect foot-strike and toe-off in treadmill running

Foot-strike and toe-off detection is often critical in the assessment of running biomechanics. The onset and offset of the vertical ground reaction force is regarded as the “gold standard” method for step event detection, but several kinematics-based algorithms have been proposed to detect foot-strike and toe-off in the absence of force plates. However, the accuracy and generalisability of kinematics-based methods are often limited. Therefore, we developed FootNet, an algorithm using kinematic input and deep learning, to improve the detection of foot-strike and toe-off events during treadmill running in a variety of speed, foot-strike angle and incline conditions

Development and validation of FootNet, a new kinematic and deep learning-based algorithm to detect foot-strike and toe-off in treadmill running

Foot-strike and toe-off detection is often critical in the assessment of running biomechanics. The onset and offset of the vertical ground reaction force is regarded as the “gold standard” method for step event detection, but several kinematics-based algorithms have been proposed to detect foot-strike and toe-off in the absence of force plates. However, the accuracy and generalisability of kinematics-based methods are often limited. Therefore, we developed FootNet, an algorithm using kinematic input and deep learning, to improve the detection of foot-strike and toe-off events during treadmill running in a variety of speed, foot-strike angle and incline conditions

The effects of focus of attention on the learning of the clean weightlifting technique in novices

The purpose of this study was to assess the effects of different focus of attention (FOA) feedback on the learning of the clean weightlifting technique. Sixteen participants naive to the task were divided into two groups (external and internal FOA) and attended three sessions of learning. Bar and lifter kinematics were measured prior to and after learning, and retention and transfer tests were performed seven days after protocol completion. Internal FOA feedback promoted greater reduction of the distance between the bar and the lifter’s body, whereas both types of feedback were found to be equally effective at modifying hip, knee and ankle flexion/extension patterns in the pulling phases of the lift. There seems to be insufficient evidence to advise coaches to choose one type of FOA feedback exclusively, as each approach could target different needs/stages of learning.<br/

Dataset for "Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running"

This dataset includes the input features and target labels needed to train and test FootNet. The input features include the distal tibia anteroposterior velocity, ankle plantar/dorsi flexion angle and foot centre of mass anteroposterior and vertical velocities. Additionally, ground reaction force data and trial names are also included.This dataset includes data coming from five different datasets collected in three independent laboratories (see associated publication for more details). It includes treadmill running kinematics and kinetics processed to obtain the previously mentioned variables and chopped in running gait cycles.The original datasets were fully reprocessed as described in the Methods section of the associated publication.The project directory StepDetectionStudy is organised as follows: - Data > OriginalDatasets: Folder containing the entire datasets (*_dataset.npy files). - Data > DataFolds.npy: File containing the training data grouped in 5 folds. - Data > TestingSet.npy: File containing the testing set. Data are organised as Python dictionaries containing the kinematic input features ['X'], label vectors ['Y'], metadata about the trials ['meta'] and vertical GRF ['GRFv']. Each of those dictionary keys contains a list with nested lists with the structure participant > trial > stride. For instance, `dataset['X'][0][0][0]` accesses the kinematic input features characterising the first stride recorded in the first trial of the first participant in dataset. - CrossValidation > Models: Folder containing the five models developed during cross validation. - CrossValidation > Results: Folder containing the summary performance metrics for each model on its corresponding validation set and Bland-Altman plots comparing foot strike, toe off and contact times as predicted by FootNet vs gold standard method. - FinalTest > FootNet_best_candidate: Folder containing the best set of parameters resulting from cross validation. Summary performance metrics on testing set and Bland-Altman plots comparing foot strike, toe off and contact times as predicted by FootNet vs gold standard method. - FinalTest > y_and_yhat.mat: File containing testing predictions, target labels and metadata from testing stride cycles for posterior analyses in Matlab presented in the paper. - FinalModel: Folder containing the final updated model resulting from FinalTest as a SavedModel directory (Tensorflow model format) and as .h5. - Notebooks > TrainTest_Split.ipynb: Google Colab (Jupyter) notebook demonstrating how the dataset splitting was performed, including training and testing (70/30) and further folding of training dataset in 5 folds. - Notebooks > CrossValidation.ipnyb. Google Colab (Jupyter) notebook that performs 5-fold cross-validation and selects the best set of weights as best candidate for the final test. - Notebooks > FinalTest.ipnyb: Google Colab (Jupyter) notebook that updates the best candidate model resulting from cross-validation with the 5 folds as training set and performs the final test on the testing set

Development of Epitaxial Oxide Ceramics Nanomaterials Based on Chemical Strategies on Semiconductor Platforms

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Dataset for "Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running"

This dataset includes the input features and target labels needed to train and test FootNet. The input features include the distal tibia anteroposterior velocity, ankle plantar/dorsi flexion angle and foot centre of mass anteroposterior and vertical velocities. Additionally, ground reaction force data and trial names are also included

Dataset for "Development and validation of FootNet; a new kinematic algorithm to improve foot-strike and toe-off detection in treadmill running"

This dataset includes the input features and target labels needed to train and test FootNet. The input features include the distal tibia anteroposterior velocity, ankle plantar/dorsi flexion angle and foot centre of mass anteroposterior and vertical velocities. Additionally, ground reaction force data and trial names are also included