Muscle fatigue detection and treatment system driven by internet of things

Ma, Bin; Li, Chunxiao; Wu, Zhaolong; Huang, Yulong; Zijp-Tan, Ada Chaeli van der; Tan, Shaobo; Li, Dongqi; Fong, Ada; Basetty, Chandan; Borchert, Glen M.; Benton, Ryan; Wu, Bin; Huang, Jingshan

doi:10.1186/s12911-019-0982-x

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…In recent years, physiological signals, including EEG [ 6 ], ECG [ 20 , 21 ] and EMG [ 22 ], have been widely used in disease detection. With the development of machine learning (ML) technology, many researchers have made outstanding contributions to automatic MDD detection using ML.…”

Section: Related Workmentioning

confidence: 99%

A novel EEG-based major depressive disorder detection framework with two-stage feature selection

Shen

Fan

et al. 2022

BMC Med Inform Decis Mak

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Background Major depressive disorder (MDD) is a common mental illness, characterized by persistent depression, sadness, despair, etc., troubling people’s daily life and work seriously. Methods In this work, we present a novel automatic MDD detection framework based on EEG signals. First of all, we derive highly MDD-correlated features, calculating the ratio of extracted features from EEG signals at frequency bands between $$\beta$$ β and $$\alpha$$ α . Then, a two-stage feature selection method named PAR is presented with the sequential combination of Pearson correlation coefficient (PCC) and recursive feature elimination (RFE), where the advantages lie in minimizing the feature searching space. Finally, we employ widely used machine learning methods of support vector machine (SVM), logistic regression (LR), and linear regression (LNR) for MDD detection with the merit of feature interpretability. Results Experiment results show that our proposed MDD detection framework achieves competitive results. The accuracy and $$F_{1}$$ F 1 score are up to 0.9895 and 0.9846, respectively. Meanwhile, the regression determination coefficient $$R^2$$ R 2 for MDD severity assessment is up to 0.9479. Compared with existing MDD detection methods with the best accuracy of 0.9840 and $$F_1$$ F 1 score of 0.97, our proposed framework achieves the state-of-the-art MDD detection performance. Conclusions Development of this MDD detection framework can be potentially deployed into a medical system to aid physicians to screen out MDD patients.

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Section: Related Workmentioning

confidence: 99%

A novel EEG-based major depressive disorder detection framework with two-stage feature selection

Shen

Fan

et al. 2022

BMC Med Inform Decis Mak

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“…Internet of Things (IoT) strategies are widely utilized in an array of areas including sensor related applications [15][16][17] and detection systems [18][19][20]. Furthermore, combining IoT, cloud computing, and machine learning has previously proven effective for precise, realtime sleep apnea detection and diagnosis [21].…”

Section: Introductionmentioning

confidence: 99%

Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis

et al. 2020

BMC Med Inform Decis Mak

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Background The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis. Methods This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time. Results Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. Discussion Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising. Conclusions Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

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