Robust Respiratory Rate Monitoring Using Smartwatch Photoplethysmography

Zhao, Langcheng; Zhang, Fenglin; Zhang, Huanhuan; Liang, Yumeng; Zhou, Anfu; Ma, Huadóng

doi:10.1109/jiot.2022.3219813

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…It was determined that the most suitable supervised machine-learning technique would be the gradient-boosted model, which has obtained the best accuracy of 0.95 with an f-score of 0.93, recall of 0.92, and precision of 0.94, which was used in works such as [43,44]. This has been corroborated by the ROC-AUC curves, where the "Low" classes for males and "High" for females show a substantial elevation towards the upper left corner of the graph and have a high area under the curve of 0.98; this indicates that the model has a high sensitivity to detect motor competence classification.…”

Section: Discussionmentioning

confidence: 99%

Classification of Motor Competence in Schoolchildren Using Wearable Technology and Machine Learning with Hyperparameter Optimization

Sulla-Torres,

Calla Gamboa,

Avendaño Llanque

et al. 2024

Applied Sciences

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Determining the classification of motor competence is an essential aspect of physical activity that must be carried out during school years. The objective is to evaluate motor competence in schoolchildren using smart bands, generate percentiles of the evaluation metrics, and classify motor performance through machine learning with hyperparameter optimization. A cross-sectional descriptive study was carried out on 764 schoolchildren (451 males and 313 females) aged 6 to 17 years. Five state schools in the city of Arequipa, Peru were evaluated. Weight, height, and waist circumference were assessed, and body mass index (BMI) was calculated. The tests evaluated in the schoolchildren measured walking and running for 6 minutes. These tests were carried out using smart bands, capturing cadence, number of steps, calories consumed, speed, stride, and heart rate. As a result, the percentiles were created through the LMS method [L (asymmetry: lambda), M (median: mu), and S (coefficient of variation: sigma)]. The cut-off points considered were <P25 (below average), p25 to p75 (average), and >p75 (above average). For classification, the machine-learning algorithms random forest, decision tree, support vector machine, naive Bayes, logistic regression, k-nearest neighbor, neural network, gradient boosting, XGBboost, LightGBM, and CatBoost were used, and the hyperparameters of the models were optimized using the RandomizedSearchCV technique. In conclusion, it was possible to classify motor competence with the tests carried out on schoolchildren, significantly improving the accuracy of the machine-learning algorithms through the selected hyperparameters, with the gradient boosting classifier being the best result at 0.95 accuracy and in the ROC-AUC curves with a 0.98. The reference values proposed in this study can be used to classify the walking motor competence of schoolchildren. Finally, the mobile software product built based on the proposed model was validated using the prototype of the Software Quality Systemic Model (SQSM) based on three specific categories: functionality, reliability, and usability, obtaining 77.09%. The results obtained can be used in educational centers to achieve the suggested recommendations for physical activity in schoolchildren.

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Section: Discussionmentioning

confidence: 99%

Classification of Motor Competence in Schoolchildren Using Wearable Technology and Machine Learning with Hyperparameter Optimization

Sulla-Torres,

Calla Gamboa,

Avendaño Llanque

et al. 2024

Applied Sciences

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“…According to previous research [ 12 ], heart rate is a fundamental parameter to know the health state and the main cardiovascular risks, especially when it can be continuously monitored using portable devices in daily life. In this way, several investigations use photoplethysmography (PPG) through smart devices, such as smartwatches, for continuous monitoring of respiration; this is how they are based on a certain medical discovery called respiratory sinus arrhythmia (RSA), which describes the relationship between respiratory and heart rate.…”

Section: Previous Work and Literature Reviewmentioning

confidence: 99%

Monitoring System for Operating Variables in Incubators in the Neonatology Service of a Highly Complex Hospital through the Internet of Things (IoT)

Aya-Parra,

Rodriguez-Orjuela,

Rodriguez Torres

et al. 2023

Sensors

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Background: Around 15 million premature babies are born annually, requiring specialized care. Incubators are vital for maintaining their body temperature, which is crucial for their well-being. Ensuring optimal conditions in incubators, including constant temperature, oxygen control, and comfort, is essential for improving the care and survival rates of these infants. Methods: To address this, an IoT-based monitoring system was developed in a hospital setting. The system comprised hardware components such as sensors and a microcontroller, along with software components including a database and a web application. The microcontroller collected data from the sensors, which was then transmitted to a broker via WiFi using the MQTT protocol. The broker validated and stored the data in the database, while the web application provided real-time access, alerts, and event recording. Results: Two certified devices were created, employing high quality components. The system was successfully implemented and tested in both the biomedical engineering laboratory and the neonatology service of the hospital. The results of the pilot test supported the concept of IoT-based technology, demonstrating satisfactory responses in temperature, humidity, and sound variables within the incubators. Conclusions: The monitoring system facilitated efficient record traceability, allowing access to data over various timeframes. It also captured event records (alerts) related to variable problems, providing information on duration, date, hour, and minutes. Overall, the system offered valuable insights and enhanced monitoring capabilities for neonatal care.

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Respiratory rate estimation from photoplethysmogram baseline wandering by harmonic analysis and sequential fusion

Zhang,

Wei,

Dong

et al. 2025

Biomedical Signal Processing and Control

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Robust Respiratory Rate Monitoring Using Smartwatch Photoplethysmography

Cited by 6 publications

References 44 publications

Classification of Motor Competence in Schoolchildren Using Wearable Technology and Machine Learning with Hyperparameter Optimization

Classification of Motor Competence in Schoolchildren Using Wearable Technology and Machine Learning with Hyperparameter Optimization

Monitoring System for Operating Variables in Incubators in the Neonatology Service of a Highly Complex Hospital through the Internet of Things (IoT)

Respiratory rate estimation from photoplethysmogram baseline wandering by harmonic analysis and sequential fusion

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