A Convolution Model for Prediction of Physiological Responses to Physical Exercises

Ludwig, Melanie; Grohganz, Harald G.; Asteroth, Alexander

doi:10.1007/978-3-030-14526-2_2

Cited by 2 publications

(6 citation statements)

References 29 publications

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“…In one study, the average prediction error (test set) was 7.08 bpm in a leave-one-out cross validation of altogether 17 tests of three subjects (Ludwig et al, 2016 ). In a second study, average approximation error (training set) of 4.95 bpm and an average prediction error (test set) of 7.34 bpm in altogether 20 tests of five subjects was observed (Ludwig et al, press ).…”

Section: Modeling and Prediction Of Heart Ratementioning

confidence: 94%

“…As defined in Ludwig et al ( press ), many (non-black box) models

can be defined as functions mapping all parameters

required by the model, and a stress curve u , to an artificially computed HR curve y . In this curve both, input (i.e., stress curve) and output (i.e., HR curve), are real time series.…”

Section: Modeling and Prediction Of Heart Ratementioning

confidence: 99%

“…However, black box models tend to overfit in HR response prediction of a complete training session. This is caused by the high number of parameters, which are also often used in non-black-box phenomenological HR models (Ludwig et al, press ). Particularly interpretability of a model's parameters is favorable in HR prediction: to model not artifacts but real factors influencing the HR significantly improves the accuracy of prediction.…”

Section: Modeling and Prediction Of Heart Ratementioning

confidence: 99%

“…The model proposed by Ludwig et al ( press ) can be illustrated as Wiener model, but has a strong focus on reduction of parameters and thus is presented in section 3.1.5.…”

Section: Modeling and Prediction Of Heart Ratementioning

confidence: 99%

“…Adjusting model parameters can certainly be performed faster for less parameters, such that a less complex model with only few parameters could possibly be optimized and adjusted online on a wearable and during training. HR models by Zakynthinaki ( 2015 ) and Ludwig et al ( press ) are reduced to one parameter and might be good candidates for this purpose. Additionally, results obtained in regression analysis as in Hoffmann and Wiemeyer ( 2017b ) can help reducing necessary parameters in other models.…”

Section: Modeling and Prediction Of Heart Ratementioning

confidence: 99%

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Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise—Basics and Options for Wearable Devices

et al. 2018

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The use of wearable devices or “wearables” in the physical activity domain has been increasing in the last years. These devices are used as training tools providing the user with detailed information about individual physiological responses and feedback to the physical training process. Advantages in sensor technology, miniaturization, energy consumption and processing power increased the usability of these wearables. Furthermore, available sensor technologies must be reliable, valid, and usable. Considering the variety of the existing sensors not all of them are suitable to be integrated in wearables. The application and development of wearables has to consider the characteristics of the physical training process to improve the effectiveness and efficiency as training tools. During physical training, it is essential to elicit individual optimal strain to evoke the desired adjustments to training. One important goal is to neither overstrain nor under challenge the user. Many wearables use heart rate as indicator for this individual strain. However, due to a variety of internal and external influencing factors, heart rate kinetics are highly variable making it difficult to control the stress eliciting individually optimal strain. For optimal training control it is essential to model and predict individual responses and adapt the external stress if necessary. Basis for this modeling is the valid and reliable recording of these individual responses. Depending on the heart rate kinetics and the obtained physiological data, different models and techniques are available that can be used for strain or training control. Aim of this review is to give an overview of measurement, prediction, and control of individual heart rate responses. Therefore, available sensor technologies measuring the individual heart rate responses are analyzed and approaches to model and predict these individual responses discussed. Additionally, the feasibility for wearables is analyzed.

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Section: Modeling and Prediction Of Heart Ratementioning

confidence: 94%

“…As defined in Ludwig et al ( press ), many (non-black box) models

can be defined as functions mapping all parameters

required by the model, and a stress curve u , to an artificially computed HR curve y . In this curve both, input (i.e., stress curve) and output (i.e., HR curve), are real time series.…”

Section: Modeling and Prediction Of Heart Ratementioning

confidence: 99%

Section: Modeling and Prediction Of Heart Ratementioning

confidence: 99%

“…The model proposed by Ludwig et al ( press ) can be illustrated as Wiener model, but has a strong focus on reduction of parameters and thus is presented in section 3.1.5.…”

Section: Modeling and Prediction Of Heart Ratementioning

confidence: 99%

Section: Modeling and Prediction Of Heart Ratementioning

confidence: 99%

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Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise—Basics and Options for Wearable Devices

et al. 2018

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Artificial Intelligence in Elite Sports—A Narrative Review of Success Stories and Challenges

Hammes¹,

Hagg²,

Asteroth³

et al. 2022

Front. Sports Act. Living

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This paper explores the role of artificial intelligence (AI) in elite sports. We approach the topic from two perspectives. Firstly, we provide a literature based overview of AI success stories in areas other than sports. We identified multiple approaches in the area of Machine Perception, Machine Learning and Modeling, Planning and Optimization as well as Interaction and Intervention, holding a potential for improving training and competition. Secondly, we discover the present status of AI use in elite sports. Therefore, in addition to another literature review, we interviewed leading sports scientist, which are closely connected to the main national service institute for elite sports in their countries. The analysis of this literature review and the interviews show that the most activity is carried out in the methodical categories of signal and image processing. However, projects in the field of modeling & planning have become increasingly popular within the last years. Based on these two perspectives, we extract deficits, issues and opportunities and summarize them in six key challenges faced by the sports analytics community. These challenges include data collection, controllability of an AI by the practitioners and explainability of AI results.

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A Convolution Model for Prediction of Physiological Responses to Physical Exercises

Cited by 2 publications

References 29 publications

Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise—Basics and Options for Wearable Devices

Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise—Basics and Options for Wearable Devices

Artificial Intelligence in Elite Sports—A Narrative Review of Success Stories and Challenges

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