Sensing surface vibrations promise unobtrusive interaction for smart home systems by enabling gesture recognition on existing everyday surfaces without disturbing living-space design. Existing approaches typically address only parts of the processing chain, such as sensing hardware or offline gesture recognition, rather than providing an end-to-end system from surface-mounted sensors to the evaluation of the prediction model. This paper presents a custom sensor system and a configurable data-to-model pipeline for gesture recognition on a standard office desk. Our hardware enables a low-noise sensing of the vibrations using piezoelectric sensors. Building on a modular signal-processing framework, we model the full chain from continuous recordings through variable pre-processing to a model-ready dataset, and process the resulting data with compact depthwise separable 1D-CNNs. We conduct a joint search over pre-processing and model hyperparameters and identify a configuration with 8,722 parameters that uses band-pass filtering, fixed-length windows, and min-max normalization. On a self-recorded dataset with 15 participants performing six gestures this configuration achieves high accuracies across different data splitting methods, including strong user-independent performance in a leave-one-subject-out cross-validation.

翻译：感知表面振动有望通过识别日常家居表面上的手势，在不影响生活空间设计的前提下，为智能家居系统带来无干扰的交互。现有方法通常仅处理感知硬件或离线手势识别等处理链中的部分环节，而非提供从表面安装传感器到预测模型评估的完整端到端系统。本文提出了一种定制传感器系统及可配置的数据到模型流水线，用于在标准办公桌上实现手势识别。我们的硬件通过压电传感器实现了低噪声振动感知。基于模块化信号处理框架，我们构建了从连续记录经可变预处理到模型就绪数据集的完整链路，并使用紧凑的深度可分离一维卷积神经网络（1D-CNNs）处理所得数据。我们对预处理和模型超参数进行了联合搜索，最终确定了一个包含8722个参数的配置，该配置采用带通滤波、固定长度窗口和最小-最大归一化。在一个包含15名参与者执行六种手势的自录数据集中，该配置在不同数据划分方法下均实现了高准确率，其中在留一受试者交叉验证中展现了强大的用户无关性能。