A Neural Network Model for Estimating the Heart Rate Response to Constant Intensity Exercises

Zakynthinaki, Maria S.; Kapetanakis, Theodoros N.; Lampou, Anna; Ioannidou, Melina P.; Vardiambasis, Ioannis O.

doi:10.3390/signals2040049

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…ANNs have also been reported to serve as decision-making tools, in various fields, due to their ability to model complex and nonlinear relationships. Such usage of ANNs extends in numerous applications, including engineering [ 35 ], statistics and stock market [ 36 ], medicine [ 37 ], and exercise physiology [ 38 ], among others. Predictions based on ANNs are more accurate than regression models, thanks to the hidden layers that filter out redundant information.…”

Section: Discussionmentioning

confidence: 99%

Biomechanical comparison of two surgical methods for Hallux Valgus deformity: Exploring the use of artificial neural networks as a decision-making tool for orthopedists

Kaczmarczyk,

Zakynthinaki,

Barton

et al. 2024

PLoS ONE

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Hallux Valgus foot deformity affects gait performance. Common treatment options include distal oblique metatarsal osteotomy and chevron osteotomy. Nonetheless, the current process of selecting the appropriate osteotomy method poses potential biases and risks, due to its reliance on subjective human judgment and interpretation. The inherent variability among clinicians, the potential influence of individual clinical experiences, or inherent measurement limitations may contribute to inconsistent evaluations. To address this, incorporating objective tools like neural networks, renowned for effective classification and decision-making support, holds promise in identifying optimal surgical approaches. The objective of this cross-sectional study was twofold. Firstly, it aimed to investigate the feasibility of classifying patients based on the type of surgery. Secondly, it sought to explore the development of a decision-making tool to assist orthopedists in selecting the optimal surgical approach. To achieve this, gait parameters of twenty-three women with moderate to severe Hallux Valgus were analyzed. These patients underwent either distal oblique metatarsal osteotomy or chevron osteotomy. The parameters exhibiting differences in preoperative and postoperative values were identified through various statistical tests such as normalization, Shapiro-Wilk, non-parametric Wilcoxon, Student t, and paired difference tests. Two artificial neural networks were constructed for patient classification based on the type of surgery and to simulate an optimal surgery type considering postoperative walking speed. The results of the analysis demonstrated a strong correlation between surgery type and postoperative gait parameters, with the first neural network achieving a remarkable 100% accuracy in classification. Additionally, cases were identified where there was a mismatch with the surgeon’s decision. Our findings highlight the potential of artificial neural networks as a complementary tool for surgeons in making informed decisions. Addressing the study’s limitations, future research may investigate a wider range of orthopedic procedures, examine additional gait parameters and use more diverse and extensive datasets to enhance statistical robustness.

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Section: Discussionmentioning

confidence: 99%

Biomechanical comparison of two surgical methods for Hallux Valgus deformity: Exploring the use of artificial neural networks as a decision-making tool for orthopedists

Kaczmarczyk,

Zakynthinaki,

Barton

et al. 2024

PLoS ONE

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show abstract

“…A soft computing technique was applied to build a model that is capable of estimating the heart rate (HR) response to exercise of constant intensity and recovery [65]. Multilayer perceptron artificial neural networks (NN) were employed using raw heart rate time series data.…”

Section: Machine Learning For Healthcarementioning

confidence: 99%

“…It can observed from the literature that there is a lack of advanced machine learning methods to enhance prediction accuracy through smart clothing [58]. Some AI tools are focused on classifying only a single product, which is not suitable for other product/data sets [64,65]. Some methods are suitable for detecting particular body types and unable to detect other body types [66].…”

Section: Deep Learning For Healthcarementioning

confidence: 99%

Smart Clothing Framework for Health Monitoring Applications

Ahsan

Teay²,

Sayem

et al. 2022

Signals

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Wearable technologies are making a significant impact on people’s way of living thanks to the advancements in mobile communication, internet of things (IoT), big data and artificial intelligence. Conventional wearable technologies present many challenges for the continuous monitoring of human health conditions due to their lack of flexibility and bulkiness in size. Recent development in e-textiles and the smart integration of miniature electronic devices into textiles have led to the emergence of smart clothing systems for remote health monitoring. A novel comprehensive framework of smart clothing systems for health monitoring is proposed in this paper. This framework provides design specifications, suitable sensors and textile materials for smart clothing (e.g., leggings) development. In addition, the proposed framework identifies techniques for empowering the seamless integration of sensors into textiles and suggests a development strategy for health diagnosis and prognosis through data collection, data processing and decision making. The conceptual technical specification of smart clothing is also formulated and presented. The detailed development of this framework is presented in this paper with selected examples. The key challenges in popularizing smart clothing and opportunities of future development in diverse application areas such as healthcare, sports and athletics and fashion are discussed.

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Physical performance estimation in practice: A systematic review of advancements in performance prediction and modeling in cycling

Stessens,

Gielen,

Meeusen

et al. 2024

International Journal of Sports Science & Coaching

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Physical performance in cycling is commonly evaluated with laboratory-based performance markers. However, these markers are not monitored on a regular basis, mainly due to the high costs of testing equipment, invasive sampling and time-intensive protocols. The use of mathematical modeling offers a promising alternative allowing for consistent performance monitoring, identification of influential variables affecting performance, and facilitation of planning, monitoring, and predictive analysis. Wearable technology, such as physiological and biomechanical sensors, can be integrated with mathematical models to enhance the practicality of performance monitoring and enable real-time feedback and personalized training recommendations. In this systematic review, we attempted to provide an overview of the developments in predicting and modeling of performance in cycling and their respective practical applications. The PRISMA framework yielded 52 studies that met the inclusion criteria. The models were discussed according to their modeling goal: characterizing kinetics, alternatives to the gold-standard, training control, observing training effects, predicting competitive performance and optimizing performance. Field-based models and technological advancements were highlighted as solutions to the limitations of gold-standard testing. Due to the lower accuracies of modeling techniques, the gold-standard laboratory-based methods of testing will not be replaced by mathematical models. However, models do form a more practical alternative for regular monitoring and a powerful tool for training and competition optimization. A modeling technique needs to be individualized to the goal and the person and be as simple as possible to allow regular monitoring. Ideally, the technique would work in the field, uses submaximal exercise intensities and integrates technological advancements such as wearable technology and machine learning to increase the practicality even more.

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A Neural Network Model for Estimating the Heart Rate Response to Constant Intensity Exercises

Cited by 3 publications

References 20 publications

Biomechanical comparison of two surgical methods for Hallux Valgus deformity: Exploring the use of artificial neural networks as a decision-making tool for orthopedists

Biomechanical comparison of two surgical methods for Hallux Valgus deformity: Exploring the use of artificial neural networks as a decision-making tool for orthopedists

Smart Clothing Framework for Health Monitoring Applications

Physical performance estimation in practice: A systematic review of advancements in performance prediction and modeling in cycling

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