Mechanosensory Coupling Between Human Cardiac Activity and Plant Bioelectric Potentials: A Naturalistic Self-Study with Spectral Validation

Abstract

We report a single-subject proof-of-concept naturalistic longitudinal within-office study (n ≈ 93,000 one-Hz observations from a single participant across seven daytime sessions; n here refers to time-series samples, not biological replicates) testing whether human emotional states couple to the bioelectric potentials of a co-located Kalanchoe dai-gremontiana plant through measurable physical intermediaries. Using a custom IoT platform comprising two complementary AD8232 bioelectric plant sensors (Plant1: RC lowpass, 5-second averages; Plant2: 0.1–20 Hz bandpass at 1 Hz), a Sensirion SCD41 CO₂/temperature/humidity sensor, an SGP40 volatile organic compound (VOC) sensor, a Polar H10 heart rate monitor, and HSEmotion facial expression recognition, we recorded simultaneous human emotion (valence, arousal), physiology (heart rate HR, heart rate variability RMSSD), and environmental chemistry (CO₂, VOC index) at 1 Hz alongside plant bioelectric voltage. Lagged mediation analysis confirmed partial mediation of the valence→plant relationship through heart rate (Δa = 8 s for valence→HR, r = +0.067, p < 0.001; 30% partial mediation with Plant1). Critically, a novel spectral mediation analy-sis—using short-time Fourier transform (STFT) band powers of Plant2 as outcome vari-ables—reveals that the valence→HR coupling (Δa = 8 s) is invariant across all eight plant frequency bands, while the HR→plant coupling (path b) is frequency-specific: full sta-tistical mediation occurs only in the 44–75 second oscillation band (Δb = 34 s), with partial mediation across five other bands. This frequency specificity rules out broadband me-chanical coupling and points toward a frequency-selective biological transduction mechanism analogous to Venus flytrap mechanosensory integration. HRV-derived emotion (RMSSD-based valence, independent of the camera system) provides convergent validation. VOC index is the strongest cross-session predictor of emotional state in random forest models.

Plant phenotyping relevance

植物のバイオ電位をセンサーで取得し、STFTによる周波数解析とスペクトル媒介分析で評価する技術的手法が研究の中心であるため、植物の生理状態を対象とするフェノタイピング手法として含める。

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