A closed-loop adaptive system using in-ear sensors and multimodal environmental control reduced physiological recovery time from anxiety to 52.3 s vs 98.6 s for sham control (Cohen's d 1.24, p<0.001).
RCT (n=50)
Sham-controlled
Randomized
Does a closed-loop adaptive system using an in-ear wearable sensor reduce physiological recovery time from anxiety in eVTOL passengers?
An in-ear wearable sensor with closed-loop adaptive regulation significantly reduced physiological recovery time from anxiety in a simulated eVTOL flight environment.
Standardized Mean Difference: 1.24
Absolute Event Rate: 52.3% vs 98.6%
p-value: p=<0.001
Objective: Rapid vertical manoeuvres and intermittent vibration in autonomous electric vertical take-off and landing (eVTOL) aircraft can provoke pronounced psychological anxiety in passengers. To address this, we propose a closed-loop adaptive system that integrates an in-ear wearable sensor with dynamic regulation of the cabin microenvironment, enabling real-time monitoring of each passenger’s autonomic state and delivering individualised mitigation through a continuous sense–analyse–intervene–feedback loop. Methods: The system is built around a pair of custom in-ear modules that integrate dual-wavelength photoplethysmography (PPG; 525 nm green and 940 nm infrared), galvanic skin response (GSR), and a six-axis inertial measurement unit (IMU) sampled at 200 Hz. To suppress the 20–80 Hz vibration generated by the distributed electric propulsion system, a compliant silicone damping sleeve attenuates high-frequency components at the hardware level, while a Kalman filter fuses the IMU and PPG streams and an adaptive notch filter removes residual rotor harmonics. The pipeline raises the heart-rate-variability (HRV) signal-to-noise ratio (SNR) to 24.1 dB, with a Pearson correlation of 0.96 against a medical-grade chest strap. A hybrid CNN–LSTM network—two convolutional layers (32 filters each) followed by two LSTM layers (128 hidden units)—predicts impending anxiety from HRV time-domain features (RMSSD, pNN50) and frequency-domain features (LF/HF ratio), triggering intervention 8.2 s in advance on average. According to the predicted anxiety level (mild/moderate/severe), a fuzzy controller modulates transcutaneous auricular vagus nerve stimulation (1–5 mA), the binaural-beat frequency (4–8 Hz, theta band), and the cabin lighting colour temperature (2700–6500 K) in real time. The intervention parameters are continuously refined by SPSA-based stochastic optimisation of the HRV recovery rate (step size 0.01; updated every 30 s). Results: In a randomised controlled experiment conducted in a simulated flight environment (N = 50; aged 22–45 years; 1:1 sex ratio), the active group reached physiological recovery in 52.3 s on average, compared with 98.6 s for the sham-controlled group—a 47% reduction (Cohen’s d = 1.24, p < 0.001). User acceptance reached 94%. Conclusions: The proposed in-ear platform enables closed-loop adaptive regulation of anxiety in the eVTOL cabin and overcomes the limitations of conventional passive mitigation strategies. By combining vibration-tolerant physiological sensing with multimodal environmental control, the work offers a practical pathway for improving passenger experience in urban air mobility and provides a useful reference for human-factors standards governing autonomous aircraft.
Wu et al. (Tue,) conducted a rct in Psychological anxiety in eVTOL passengers (n=50). Closed-loop adaptive system (in-ear wearable sensor with multimodal environmental control and tVNS) vs. Sham control was evaluated on Physiological recovery time (seconds) (Cohen's d 1.24, p=<0.001). A closed-loop adaptive system using in-ear sensors and multimodal environmental control reduced physiological recovery time from anxiety to 52.3 s vs 98.6 s for sham control (Cohen's d 1.24, p<0.001).