Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions

面向柔软毛绒陪伴机器人的轻量级一维卷积神经网络（1D CNN）情感触觉分类设计与验证

Abstract: Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies, yet their deformability and multichannel tactile sensing complicate the robust interpretation of human affect. This study presents a complete open-source MATLAB-based framework for the development and validation of compact deep learning models for affective touch recognition in soft interactive companions.

摘要： 柔软且配备传感器的陪伴机器人为社会辅助技术提供了一种物理安全且情感直观的交互界面，然而其可变形性与多通道触觉传感特性使得准确解读人类情感变得复杂。本研究提出了一个完整的、基于 MATLAB 的开源框架，用于开发和验证适用于柔软交互式陪伴机器人的紧凑型深度学习情感触觉识别模型。

As a primary contribution, a diverse FAIR-compliant dataset of 1326 labelled gesture sequences collected from 25 participants spanning children, teenagers, and adults is made publicly available, providing a reusable resource for future research in affective touch recognition. Through systematic architecture and hyperparameter exploration across 468 CNN models, the study identifies compact dilated one-dimensional convolutional neural networks (1D CNNs) as the most effective solution, with a 13.2k-parameter model achieving 75% test accuracy and 85% mean leave-one-subject-out cross-validation accuracy.

作为主要贡献，本研究公开了一个包含 1326 条标注手势序列的多样化数据集，该数据集符合 FAIR 原则，涵盖了 25 名儿童、青少年和成年参与者，为未来情感触觉识别研究提供了可复用的资源。通过对 468 个 CNN 模型进行系统的架构和超参数探索，研究确定紧凑型空洞一维卷积神经网络（1D CNN）为最有效的解决方案；其中一个仅含 13.2k 参数的模型在测试集上达到了 75% 的准确率，在留一法交叉验证中达到了 85% 的平均准确率。

Theoretical inference-time analysis shows that quantized deployment requires 3.2 MMAC per window, compatible with 20 Hz real-time operation on the target microcontroller. PC-based real-time simulation with the physical toy streaming sensor data demonstrates that the CNN resolves subtle social touches that the previous heuristic system failed to detect, whereas high-force negative interactions are captured more reliably by trivial threshold-based logic.

理论推理时间分析表明，量化部署后每个窗口仅需 3.2 MMAC（百万次乘加运算），能够满足目标微控制器上 20 Hz 的实时运行需求。基于物理玩具流式传感器数据的 PC 端实时仿真表明，该 CNN 模型能够识别出此前启发式系统无法检测到的细微社交触碰，而对于高强度的负面交互，简单的阈值逻辑则能更可靠地进行捕捉。

The resulting hybrid inference pipeline - instantaneous heuristic filtering followed by CNN-based nuanced gesture classification - is proposed as the embedded deployment strategy. The study demonstrates that emotionally meaningful, privacy-preserving touch interpretation is computationally feasible for direct embedding within soft therapeutic companions, with hardware integration addressed in a forthcoming study.

研究提出了一种混合推理流水线作为嵌入式部署策略，即先进行即时的启发式过滤，随后进行基于 CNN 的细微手势分类。该研究证明，在柔软的治疗型陪伴机器人中直接嵌入具有情感意义且保护隐私的触觉解读功能在计算上是可行的，相关的硬件集成工作将在后续研究中探讨。