Research and Design of a Ballistocardiogram-Based Heart Rate Variability (HRV) Monitoring Device Integrated into Pilot Helmets

ZHAO Yanpeng; LI Falin; LI Xuan; YU Haibo; CAO Zhengtao; ZHANG Yi

doi:10.11999/JEIT250342

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ZHAO Yanpeng, LI Falin, LI Xuan, YU Haibo, CAO Zhengtao, ZHANG Yi. Research and Design of a Ballistocardiogram-Based Heart Rate Variability (HRV) Monitoring Device Integrated into Pilot Helmets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250342

Citation:

ZHAO Yanpeng, LI Falin, LI Xuan, YU Haibo, CAO Zhengtao, ZHANG Yi. Research and Design of a Ballistocardiogram-Based Heart Rate Variability (HRV) Monitoring Device Integrated into Pilot Helmets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250342

Citation:

ZHAO Yanpeng, LI Falin, LI Xuan, YU Haibo, CAO Zhengtao, ZHANG Yi. Research and Design of a Ballistocardiogram-Based Heart Rate Variability (HRV) Monitoring Device Integrated into Pilot Helmets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250342

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Research and Design of a Ballistocardiogram-Based Heart Rate Variability (HRV) Monitoring Device Integrated into Pilot Helmets

doi: 10.11999/JEIT250342 cstr: 32379.14.JEIT250342

1.
Air Force Medical Center, Air Force Medical University, PLA, Beijing 100142, China
2.
Shenzhen Fuyuan Technology Co., Ltd., Shenzhen, Guangdong 518131, China
3.
Xiamen University, Xiamen, Fujian 361005, China

Received Date: 2025-04-30
Rev Recd Date: 2025-08-19

Available Online: 2025-09-01

Abstract

Abstract

Objective Conventional Heart Rate Variability (HRV) monitoring in aviation is limited by bulky wearable devices that require direct skin contact, are prone to electromagnetic interference during flight, and suffer from electrode displacement during high-G maneuvers. These constraints hinder continuous physiological monitoring, which is critical for flight safety. This study presents a non-contact monitoring approach integrated into pilot helmets, utilizing BallistoCardioGram (BCG) technology to detect cardiac mechanical activity via helmet-mounted inertial sensors. The objective is to establish a novel physiological monitoring paradigm that eliminates the need for skin–electrode interfaces while achieving measurement accuracy suitable for aviation operational standards. Methods Hardware ConfigurationA patented BCG sensing module is embedded within the occipital stabilization system of flight protective helmets. Miniaturized, high-sensitivity inertial sensors interface with proprietary signal conditioning circuits that execute a three-stage physiological signal refinement process. First, primary analog amplification scales microvolt-level inputs to measurable voltage ranges. Second, a fourth-order Butterworth bandpass filter (0.5–20 Hz) isolates cardiac mechanical signatures. Third, analog-to-digital conversion quantizes the signals at a 250 Hz sampling rate. Physical integration complies with military equipment standards for helmet structural integrity and ergonomic performance, ensuring full compatibility with existing flight gear without compromising protection or pilot comfort during extended missions.Computational Framework A multi-layer signal processing architecture is implemented to extract physiological features. Raw BCG signals undergo five-level discrete wavelet transformation using Daubechies-4 basis functions, effectively separating cardiac components from respiratory modulation and motion-induced artifacts. J-wave identification is achieved through dual-threshold detection: morphological amplitudes exceeding three times the local baseline standard deviation and temporal positioning within 200 ms sliding analysis windows. Extracted J–J intervals are treated as functional analogs of ElectroCardioGram (ECG)-derived R–R intervals. Time-domain HRV metrics are computed as follows: (1) Standard Deviation of NN intervals (SDNN), representing overall autonomic modulation; (2) Root Mean Square of Successive Differences (RMSSD), indicating parasympathetic activity; (3) Percentage of adjacent intervals differing by more than 50 ms (pNN50). Frequency-domain analysis applies Fourier transformation to quantify Low-Frequency (LF: 0.04–0.15 Hz) and High-Frequency (HF: 0.15–0.4 Hz) spectral powers. The LF/HF ratio is used to assess sympathetic–parasympathetic balance. The entire processing pipeline is optimized for real-time execution under in-flight operational conditions. Results and Discussions System validation is conducted under simulated flight conditions to evaluate physiological monitoring performance. Signal acquisition is found to be reliable across static, turbulent, and high-G scenarios, with consistent capture of BCG waveforms. Quantitative comparisons with synchronized ECG recordings show strong agreement between measurement modalities: (1) SDNN: 95.80%; (2) RMSSD: 94.08%; (3) LF/HF ratio: 92.86%. These results demonstrate that the system achieves physiological measurement equivalence to established clinical standards. Artifact suppression is effectively performed by the wavelet-based signal processing framework, which maintains waveform integrity under conditions of aircraft vibration and rapid gravitational transition—conditions where conventional ECG monitoring often fails. Among tested sensor placements, the occipital position exhibits the highest signal-to-noise ratio. Operational stability is maintained during continuous 6-hour monitoring sessions, with no observed signal degradation. This long-duration robustness indicates suitability for extended flight operations.Validation results indicate that the BCG-based approach addresses three primary limitations associated with ECG systems in aviation. The removal of electrode–skin contact mitigates the risk of contact dermatitis during prolonged wear. Non-contact sensing eliminates susceptibility to electromagnetic interference generated by radar and communication systems. Furthermore, mechanical coupling ensures signal continuity during abrupt gravitational changes, which typically displace ECG electrodes and cause signal dropout. The wavelet decomposition method is particularly effective in attenuating rotorcraft harmonic vibrations and turbulence-induced high-frequency noise. Autonomic nervous system modulation is reliably captured through pulse transit time variability, which aligns with neurocardiac regulation indices derived from ECG. Two operational considerations are identified. First, respiratory coupling under hyperventilation may introduce artifacts that require additional filtering. Second, extreme cervical flexion exceeding 45 degrees may degrade signal quality, indicating the potential benefit of redundant sensor configurations under such conditions. Conclusions This study establishes a functional, helmet-integrated BCG monitoring system capable of delivering medical-grade HRV metrics without compromising flight safety protocols. The technology represents a shift from contact-based to non-contact physiological monitoring in aviation settings. Future system development will incorporate: (1) Infrared eye-tracking modules to assess blink interval variability for objective fatigue evaluation; (2) Dry-contact electroencephalography sensors to quantify prefrontal cortex activity and assess cognitive workload; (3) Multimodal data fusion algorithms to generate unified indices of physiological strain. The integrated framework aims to enable real-time pilot state awareness during critical operations such as aerial combat maneuvers, hypoxia exposure, and emergency responses. Further technology maturation will prioritize operational validation across diverse aircraft platforms and environmental conditions. System implementation remains fully compliant with military equipment specifications and is positioned for future translation to commercial aviation and human factors research. Broader applications include astronaut physiological monitoring during spaceflight missions and enhanced safety systems in high-performance motorsports.
- Heart Rate Variability (HRV),
- BallistoCardioGram (BCG),
- Pilot helmet,
- Physiological monitoring

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References(52)

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