Virtual reality based Neural Sensors with Respirator for Cardiorespiratory Synchrony Training System

Cardiorespiratory synchronization training (CRST) uses breathing to increase balance in the autonomic nervous system and reduce negative emotions. Virtual Reality (VR) makes the patients immersive in the virtual environment for CRST training system. All the participants were trained to reduce their breathing rates through slow and deep breathing to achieve their target resonance frequency. Our proposed to design the new system based on virtual reality technology, and combined with a variety of neural sensors, such as Looxid EEG sensor, BMD101 ECG sensor, and Arduino SFM3400 Respirator, not only embedded on CRST training system, but also machine learning method to stimulate a real relaxing environment. Develop the innovative features of clinical psychological intervention, user convenience and provide the therapist with real-time display of heart rate features(SDNN, LF, LnLf, HF, LF/HF, Mhr), detect emotion and flow data on the monitor. During the process, the computer calculates LnLf value and change VR scenes at any time, we provide an instant interactive system. Our showed the two modules of CRST training system including both of passive CRST module and direct CRST module, that heart rate variability (HRV) variables and EEG have significant difference between the first one minute and last one minute in CRST VR training system.