Abstract
Recent 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.
Highlights
Prevention of cardiovascular diseases could contribute to reduce the increasing health care expenditures
The five layer fuzzy engine (Mamdani) overlaps the five layer of the Feed Forward (FF) artificial neural networks (ANNs) architecture, so the Evolving Fuzzy Neural Network (EFuNN) capability to learn by data is applied to setup the knowledge of the fuzzy logic by supervised/unsupervised learning
The hardware setup consists of a set of sensors for vital signs measurements, the analog front-end (AFE), the mixed-signal electronics analog to digital converter and digital to analog converter (ADC and DAC), the digital electronics and the microcontroller unit (Memory, Logics, MicroComputer Unit (MCU)), the communication electronics, and the electric power management, all packaged in a watch format
Summary
Prevention of cardiovascular diseases could contribute to reduce the increasing health care expenditures. A wide range of attachable devices have been developed (Peake et al 2018), embedding state-of-art sensing technologies such as optical, inertial, thermal, chemical, mechanical, most of them based on micromachinery technology [Micro Electro Mechanical Systems (MEMS)] the integration of mechanical devices in dimension between 100 nm and 100 μm (Palhalmi and Broeders 1995) Such devices attached to the body provide measurements about body vital signs so a biofeedback process could be deployed. An important part of a successful biofeedback application are the computational paradigms applied to infer about the measurements for prediction and warning purposes. The system demonstrated accurate spatio-temporal pattern recognition and early prediction capabilities of individual events Bioinspired softcomputing methods, such as the neuromorphing paradigms and the physiologic control models known as biofeedback are innovative. We apply and generalize the biofeedback approach to model and deploy a wearable system to predict and recover the degenerative diseases providing to the person the capability to self-control the daily health.
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