Intervention of Wearables and Smartphones in Real Time Monitoring of Sleep and Behavioral Health: An Assessment Using Adaptive Neuro-Fuzzy Technique

Arora, Anshika; Chakraborty, Pinaki; Bhatia, M. P. S.

doi:10.1007/s13369-021-06078-5

Cited by 19 publications

(8 citation statements)

References 81 publications

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“…The fuzzy neural system plays an essential role in the medical line for the early detection of chronic diseases such as blood infection, Alzheimer's disease, and heart diseases. [ 17 ]. The FNS not only provides the higher proficiency and accuracy but also provides the underexcretion of data or information; the artificial neural network (ANN) given by the scientist (Fojnica) is the type of artificial intelligence that also provides facilities for the understanding of physical problems and helps in the decision making in critical cases due to high accuracy in the detection of diseases [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks

Vyas

Gupta

Bhargava

et al. 2022

Journal of Healthcare Engineering

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The development of wireless sensors and wearable devices has led health care services to the new paramount. The extensive use of sensors, nodes, and devices in health care services generate an enormous amount of health data which is generally unstructured and heterogeneous. Many generous methods and frameworks have been developed for efficient data exchange frameworks, security protocols for data security and privacy. However, very less emphasis has been devoted to structuring and interpreting health data by fuzzy logic systems. The wireless sensors and device performances are affected by the remaining battery/energy, which induces uncertainties, noise, and errors. The classification, noise removal, and accurate interoperation of health data are critical for taking accurate diagnosis and decision making. Fuzzy logic system and algorithms were found to be effective and energy efficient in handling the challenges of raw medical data uncertainties and data management. The integration of fuzzy logic is based on artificial intelligence, neural network, and optimization techniques. The present work entails the review of various works which integrate fuzzy logic systems and algorithms for enhancing the performance of healthcare-related apps and framework in terms of accuracy, precision, training, and testing data capabilities. Future research should concentrate on expanding the adaptability of the reasoning component by incorporating other features into the present cloud architecture and experimenting with various machine learning methodologies.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks

Vyas

Gupta

Bhargava

et al. 2022

Journal of Healthcare Engineering

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“…The prevalence & high risk associated with depression in individuals, early screening efforts and preventive interventions have been studied widely in literature. Recent studies have leveraged the data from wearable devices and soft computing techniques to assess various psychological disorders such as sleep quality [11,16], depression [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33], behavioral disorders [16], anxiety [17,19], and mental wellbeing [20,21]. Automated detection of depression and its severity assessment using activity monitoring and other multimodal cues is being researched extensively.…”

Section: Related Workmentioning

confidence: 99%

“…ANFIS model is based on the structure of a multilayer feed-forward network works on the principle of the learning of neural networks combined with the imprecision handling of fuzzy inference systems [16]. Input features are mapped to input membership functions which are mapped to if-then rules.…”

Section: Anfismentioning

confidence: 99%

Depress-DCNF: A deep convolutional neuro-fuzzy model for detection of depression episodes using IoMT

Kumar

Sangwan

Arora

et al. 2022

Applied Soft Computing

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“…For instance, ML techniques have been employed to analyze sleep quality and duration through wearable technology data, revealing correlations with various health outcomes [22]. These studies typically leverage large datasets to train models that can predict sleep disorders from an array of inputs such as heart rate, blood pressure, and activity levels [23].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Machine Learning Models for Predicting Sleep Disorders in a Lifestyle and Health Data Context

Airlangga

2024

jiko

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Sleep disorders significantly impact public health, but their detection is often complicated by the multifaceted nature of causative factors. This study investigates the efficacy of various machine learning (ML) models in identifying sleep disorders based on comprehensive lifestyle and health data. We employed a dataset comprising 400 individual records with features including demographic information, sleep metrics, lifestyle factors, and health parameters. The dataset distinguished between individuals with no sleep disorder, insomnia, and sleep apnea. We evaluated a broad spectrum of ML models including logistic regression, decision trees, ensemble methods like RandomForest and GradientBoosting, support vector machines, and neural networks. The models' performances were assessed using accuracy, precision, recall, and F1 score metrics. Results indicated that ensemble methods, particularly RandomForest and XGBClassifier, outperformed other models in terms of accuracy, precision, and F1 scores, achieving values as high as 0.93. These methods proved effective in managing the complexity and variability of the dataset, thereby suggesting their robustness in clinical predictive analytics. The study's findings advocate for the use of advanced ensemble techniques in developing diagnostic tools for sleep disorders, highlighting their potential to enhance predictive accuracy and reliability in real-world healthcare settings. Further research is recommended to optimize these models and explore their integration into clinical practice.

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Intervention of Wearables and Smartphones in Real Time Monitoring of Sleep and Behavioral Health: An Assessment Using Adaptive Neuro-Fuzzy Technique

Cited by 19 publications

References 81 publications

Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks

Fuzzy Logic System Implementation on the Performance Parameters of Health Data Management Frameworks

Depress-DCNF: A deep convolutional neuro-fuzzy model for detection of depression episodes using IoMT

Evaluating Machine Learning Models for Predicting Sleep Disorders in a Lifestyle and Health Data Context

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