Using floor vibrations to accurately predict occupants' footstep locations is essential for smart building operation and privacy-preserving indoor sensing. However, existing approaches are dominated by either physics-based models that rely on simplified wave propagation assumptions and careful calibration, or data-driven methods that require large labeled datasets and often lack robustness to subject and environmental variability. This work introduces a new approach by treating an instrumented building floor as a physical reservoir computer, whose intrinsic structural dynamics can perform nonlinear spatio-temporal computation and information extraction directly. Specifically, foot strike-induced floor vibrations recorded by a distributed accelerometer network are processed using a lightweight physical reservoir computing (PRC) pipeline consisting of short waveform extraction, root-mean-square (RMS) normalization, principal component analysis (PCA), and a weighted linear readout. Results of this study, involving 2 participants and 12 accelerometers, showed that RMS normalization and PCA projection successfully extracted occupant-invariant features from floor-vibration waveform data, enabling a single linear readout to predict foot-strike location across repeated traversals and participants. Sub-meter accuracy is achieved along the hallway direction with moderate sensing coverage, while cross-participant tests achieved meter-scale accuracy without subject-specific recalibration or retraining. These findings demonstrate that building-scale structures can function as capable physical reservoir computers for intelligent monitoring.

翻译：利用地板振动精确预测居住者的脚步位置对于智能建筑运营和隐私保护的室内感知至关重要。然而，现有方法主要分为两类：一类是基于物理的模型，依赖于简化的波传播假设和精细校准；另一类是数据驱动方法，需要大量标注数据集且通常对主体和环境变化缺乏鲁棒性。本研究提出了一种新方法，将仪器化的建筑地板视为物理储层计算机，其固有的结构动力学能够直接执行非线性时空计算和信息提取。具体而言，通过分布式加速度计网络记录的脚步冲击引发的地板振动，采用轻量级物理储层计算（PRC）流程进行处理，该流程包括短波形提取、均方根（RMS）归一化、主成分分析（PCA）以及加权线性读出。本研究涉及2名参与者和12个加速度计，结果表明，RMS归一化和PCA投影成功地从地板振动波形数据中提取了与居住者无关的特征，使得单个线性读出能够跨重复遍历和参与者预测脚步冲击位置。在中等传感覆盖下，沿走廊方向实现了亚米级精度，而跨参与者测试在无需针对特定主体重新校准或重新训练的情况下达到了米级精度。这些发现表明，建筑尺度结构能够作为高效的物理储层计算机用于智能监测。