scholarly journals Biofeedback: e-health prediction based on evolving fuzzy neural network and wearable technologies

2021 ◽  
Author(s):  
Mario Malcangi ◽  
Giovanni Nano
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Vital Signs ◽  
Blood Oxygenation ◽  
The Body ◽  
Motion Activity ◽  
Biofeedback System ◽  
Fuzzy Neural ◽  
Reference Design ◽  
Biofeedback Control

AbstractRecent advances in wearable microelectronics and new neural networks paradigms, capable to evolve and learn online such as the Evolving Fuzzy Neural Network (EFuNN), enable the deploy of biofeedback-based applications. The missed physiologic response could be recovered by measuring uninvasively the vital signs such as the heart rate, the bio impedance, the body temperature, the motion activity, the blood pressure, the blood oxygenation and the respiration rate. Then, the prediction could be performed applying the evolving ANN paradigms. The simulation of a wearable biofeedback system has been executed applying the Evolving Fuzzy Neural Network (EFuNN) paradigm for prediction. An highly integrated wearable microelectronic device for uninvasively vital signs measurement has been deployed. Simulation results demonstrate that biofeedback control model could be an effective reference design that enables short and long-term e-health prediction. The biofeedback framework was been then defined.

Nursing Research on Patients with Hypertensive Intracerebral Hemorrhage Based on Fuzzy Neural Network Related Algorithm Under Postural Adjustment

2020 ◽  
Vol 10 (2) ◽  
pp. 422-427
Author(s):  
Dandan Qu ◽  
Caiyun Ding
Keyword(s):  
Neural Network ◽  
Supine Position ◽  
Fuzzy Neural Network ◽  
Blood Oxygenation ◽  
Postural Adjustment ◽  
Non Invasive ◽  
Significant Difference ◽  
Fuzzy Neural ◽  
The Difference ◽  
Multi Mode

Objective: To use the multi-mode fuzzy neural network monitoring measures explored in this project to clarify the trends of rSO2, ICP and CPP in patients with neurological HICH after different postures, and to screen the best nursing position for patients after HICH. Provide a basis for development. Methods: A total of 34 HICH patients admitted to the hospital from January 2017 to December 2018 were included in the study. The patient was placed in the supine position with the head position raised by 0°, 15°, 30°, 45°, 0°, and the interval between different body positions was 5 minutes. Each position was kept for 5 minutes, and the monitoring index values were read after stabilization. EGOS-600A near-infrared tissue blood oxygen parameters non-destructive monitor was used for bedside, dynamic and non-invasive real-time monitoring of rSO2; Codman intracranial pressure monitor was used for continuous dynamic monitoring of ICP; all patients were monitored with ICP and HPM-1205A ECG The monitor measures heart rate (HR), non-invasive blood pressure (BP), and pulse oximetry (SPO2). Results: When the bed angle of the patient was raised by 0°, 15°, 30° and 45°, the ICP value showed a decreasing trend with the elevation of the patient's bed angle. The ICP values were compared at various angles, and the difference was significant. Significance (P = 0.000); MAP value comparison, no significant difference (P = 0.074); CPP value showed an increasing trend with the elevation of the patient's bedside angle, the difference was significant, statistically significant (P = 0.000). There was no significant difference in HR values (P = 0.470). There was no significant difference in SPO2 values (P = 0.780). Conclusion: For patients with HICH with a GCS score of 5 to 12, multi-mode neurological monitoring, supine position elevation of 15°∼30° is a relatively suitable position; HICH patients with NIRS for rSO2 non-invasive monitoring is very Necessary, and should pay more attention to cerebral blood oxygenation changes in patients undergoing medical care after tracheotomy; use multiple modes of monitoring to ensure that patients SaO2, PaO2, MAP, ICP, CPP, and RSO2 are within the normal range. Choosing the most effective and reliable data support for the care and treatment measures that are most beneficial to improve the cerebral ischemia and hypoxia in patients has certain clinical value.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Inverse Control of Cable-driven Parallel Mechanism Using Type-2 Fuzzy Neural Network

2010 ◽  
Vol 36 (3) ◽  
pp. 459-464 ◽  
Author(s):  
Cheng-Dong LI ◽  
Jian-Qiang YI ◽  
Yi YU ◽  
Dong-Bin ZHAO
Keyword(s):  
Neural Network ◽  
Parallel Mechanism ◽  
Fuzzy Neural Network ◽  
Inverse Control ◽  
Fuzzy Neural

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

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