Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Mapping Intimacies ◽

10.31236/osf.io/w734a ◽

2021 ◽

Author(s):

Petteri Teikari ◽

Aleksandra Pietrusz

Keyword(s):

Strength Training ◽

Training Data ◽

Data Driven ◽

Measurement Technology ◽

Dynamic Adjustment ◽

Strength And Conditioning ◽

Concept System ◽

Human In The Loop ◽

Intelligence Approach

Abstract In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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In-Season Strength Training in Elite Junior Swimmers: The Role of the Low-Volume, High-Velocity Training on Swimming Performance

Journal of Human Kinetics ◽

10.2478/hukin-2020-0015 ◽

2020 ◽

Vol 74 (1) ◽

pp. 71-84

Author(s):

Mário C. Marques ◽

Juan Manuel Yáñez-García ◽

Daniel A. Marinho ◽

Juan José González-Badillo ◽

David Rodríguez-Rosell

Keyword(s):

Strength Training ◽

Swimming Performance ◽

Dynamic Strength ◽

Lower Limbs ◽

Strength And Conditioning ◽

Dry Land ◽

Pooled Data ◽

Low Volume ◽

Maximal Dynamic Strength

Abstract The aim of this study was to analyze the effect of long-term combined strength training (ST) and plyometrics on strength, power and swimming performances in elite junior swimmers during a competitive season. Ten elite junior swimmers (5 women and 5 men) completed the study (age: 16.6 ± 0.7 years; mass: 62.2 ± 5.4 kg; stature: 1.70 ± 0.07 m). The participants trained twice a week during 20 weeks. The ST program consisted of upper- and lower limbs exercises with low loads and low volume, lifting the load at maximal intended velocity. The effect of the training protocol was assessed using the 1RM in the full squat (SQ) and bench press (BP), jump height (CMJ), the maximal number of repetitions completed in the pull-up (PU) exercise and time during 50-m freestyle. Training program resulted in significant improvements in CMJ (12.1%, ES: 0.57), maximal dynamic strength in the SQ (16.4%, ES: 0.46) and BP (12.1%, ES: 0.34) exercises, the maximum number of repetitions completed during the PU test (90.7%, ES: 0.57) and swimming performance (-3.9%, ES: 0.45). There were no significant differences between both genders. The relative changes in swimming performance showed significant relationship with the relative changes in 1RM of SQ for pooled data (r=-0.66, p<0.05) and the relative changes in the PU exercise in female swimmers (r=-0.99, p<0.05). Therefore, coaches and strength and conditioning professionals should consider including in-season dry-land ST programs within the training routine in order to obtain further improvements in swimming performance.

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Domain randomization-enhanced deep learning models for bird detection

Scientific Reports ◽

10.1038/s41598-020-80101-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xin Mao ◽

Jun Kang Chow ◽

Pin Siang Tan ◽

Kuan-fu Liu ◽

Jimmy Wu ◽

...

Keyword(s):

Deep Learning ◽

Continuous Monitoring ◽

Bird Species ◽

Training Data ◽

Learning Models ◽

Fine Grained ◽

Bird Detection ◽

Relationship Of ◽

The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

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Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽

10.3390/w13081109 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1109

Author(s):

Nobuaki Kimura ◽

Kei Ishida ◽

Daichi Baba

Keyword(s):

Climate Change ◽

Surface Water ◽

Water Temperature ◽

Transfer Learning ◽

Air Temperature ◽

Climate Models ◽

Temporal Variations ◽

Training Data ◽

Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

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A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Water ◽

10.3390/w13010107 ◽

2021 ◽

Vol 13 (1) ◽

pp. 107

Author(s):

Elahe Jamalinia ◽

Faraz S. Tehrani ◽

Susan C. Steele-Dunne ◽

Philip J. Vardon

Keyword(s):

Numerical Simulation ◽

Water Flux ◽

Temporal Stability ◽

Synthetic Data ◽

Climatic Conditions ◽

Training Data ◽

Data Driven ◽

Data Set ◽

Surface Cracking ◽

Real Time Analysis

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

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Data-driven deep density estimation

Neural Computing and Applications ◽

10.1007/s00521-021-06281-3 ◽

2021 ◽

Author(s):

Patrik Puchert ◽

Pedro Hermosilla ◽

Tobias Ritschel ◽

Timo Ropinski

Keyword(s):

Data Analysis ◽

Density Estimation ◽

Population Data ◽

Training Data ◽

Data Driven ◽

Discrete Observations ◽

Efficient Manner ◽

Continuous Models ◽

3D Scans ◽

Spatial Locations

AbstractDensity estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric form, nor as priors, or in the form of many samples. This is enabled by training an unstructured convolutional neural network on an infinite stream of synthetic PDFs, as unbound amounts of synthetic training data generalize better across a deck of natural PDFs than any natural finite training data will do. Thus, we hope that our publicly available DDE method will be beneficial in many areas of data analysis, where continuous models are to be estimated from discrete observations.

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A Copula-Based Sampling Method for Data-Driven Prognostics and Health Management

Volume 3A: 39th Design Automation Conference ◽

10.1115/detc2013-13592 ◽

2013 ◽

Author(s):

Zhimin Xi ◽

Rong Jing ◽

Pingfeng Wang ◽

Chao Hu

Keyword(s):

Sampling Method ◽

Failure Time ◽

Health Management ◽

Remaining Useful Life ◽

Training Data ◽

Data Driven ◽

Prognostics And Health Management ◽

Statistical Relationship ◽

Cooling Fans ◽

On Line

This paper develops a Copula-based sampling method for data-driven prognostics and health management (PHM). The principal idea is to first build statistical relationship between failure time and the time realizations at specified degradation levels on the basis of off-line training data sets, then identify possible failure times for on-line testing units based on the constructed statistical model and available on-line testing data. Specifically, three technical components are proposed to implement the methodology. First of all, a generic health index system is proposed to represent the health degradation of engineering systems. Next, a Copula-based modeling is proposed to build statistical relationship between failure time and the time realizations at specified degradation levels. Finally, a sampling approach is proposed to estimate the failure time and remaining useful life (RUL) of on-line testing units. Two case studies, including a bearing system in electric cooling fans and a 2008 IEEE PHM challenge problem, are employed to demonstrate the effectiveness of the proposed methodology.

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Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4037782 ◽

2017 ◽

Vol 140 (3) ◽

Cited By ~ 1

Author(s):

Nurali Virani ◽

Devesh K. Jha ◽

Zhenyuan Yuan ◽

Ishana Shekhawat ◽

Asok Ray

Keyword(s):

Particle Filtering ◽

Dynamic Models ◽

Training Data ◽

Data Driven ◽

Conditional Density ◽

Bayesian Filtering ◽

State Dependent ◽

State Evolution ◽

And Control ◽

Nonparametric Kernel

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

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