飞行保护头盔集成心冲击图的心率变异性监测装置研究与设计

赵彦鹏; 李法林; 李晅; 余海波; 曹征涛; 张翼

doi:10.11999/JEIT250342

飞行保护头盔集成心冲击图的心率变异性监测装置研究与设计

doi: 10.11999/JEIT250342 cstr: 32379.14.JEIT250342

1.
空军军医大学空军特色医学中心北京 100142
2.
深圳赋远科技有限公司广东深圳 518131
3.
厦门大学福建厦门 361005

详细信息

作者简介:
赵彦鹏：男，助理研究员，研究方向为飞行保护头盔、可穿戴式生理测量

李法林：男，高级工程师，研究方向为号航空救援

李晅：女，高级实验师，研究方向为生物医学工程

余海波：男，工程师，研究方向为可穿戴式生理监测

曹征涛：男，副研究员，研究方向为可穿戴生理测量技术和飞行人为因素评估

张翼：男，副教授，研究方向为生物医学工程、生理监测

通讯作者:
曹征涛　czhengtao@126.com

中图分类号: R318.6
计量
- 文章访问数: 22
- HTML全文浏览量: 14
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2025-04-30
- 修回日期: 2025-08-19
- 网络出版日期: 2025-09-01

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

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

摘要

摘要: 心率变异性通过分析心跳间期的微小波动反映自主神经系统的调节功能，是飞行员飞行监测中不可或缺的生理参数，尤其在疲劳分层、神经功能评估和应激预警方面具有独特价值。传统的基于心电图的心率变异性监测等方法应用于飞行环境下存在佩戴不便、监测难度大等问题。该研究设计了一种基于飞行保护头盔的心冲击图心率变异性监测装置，实现了对飞行员心脏机械活动的无创、连续监测，并采用小波分析方法对心冲击图信号进行多时间尺度分解，实现心率变异性分析。实验结果表明，该装置R-R间期监测指标与心电监测结果一致性较好，其中SDNN的一致性范围为95.80%，RMSSD的一致性范围为94.08%。后续将结合头盔式眼动监测、脑电监测等集成设计，实现飞行员飞行过程中的多维生理指标监测和数据融合，为飞行应激监测、飞行疲劳预警以及飞行认知负荷评估提供技术支撑。
- 心率变异性 /
- 心冲击图 /
- 飞行员保护头盔 /
- 生理监测
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

HTML全文

图 1 心冲击图的典型波形

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图 2 传感器信号调理方案图

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图 3 监测装置位置安装图

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图 4 信号采集与预处理

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图 5 传感器在不同悬挂位置的信号对比

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图 6 心率变异性对比(时域)

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图 7 信号采集与预处理

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