AI 中文总结
研究针对软质毛绒陪伴物情感触摸分类难题，提出基于MATLAB 的开源框架。通过探索468个CNN模型，确定有效方案，公开相关数据集，所提混合推理管道可用于嵌入式部署，证明情感触摸解读在计算上对软质陪伴物可行。
AI 中文摘要
柔软且带有传感器的陪伴物为社交辅助技术提供了物理安全且情感直观的界面，但其可变形性和多通道触觉传感使人类情感的稳健解读变得复杂。本研究提出了一个基于MATLAB的完整开源框架，用于开发和验证软质交互陪伴物中情感触摸识别的紧凑深度学习模型。主要贡献包括公开了一个来自25名儿童、青少年和成人参与者的1326个带标签手势序列的合规公平数据集。通过对468个CNN模型进行系统的架构和超参数探索，确定了紧凑扩张一维卷积神经网络是最有效的解决方案，一个13200参数的模型测试准确率达75%，平均留一法交叉验证准确率达85%。理论推理时间分析表明量化部署每个窗口需要3.2 MMAC，与目标微控制器上20 Hz的实时操作兼容。基于PC的实时模拟表明CNN能解决先前启发式系统未能检测到的微妙社交触摸，而高力负向交互通过基于阈值的简单逻辑能更可靠地捕获。由此提出了混合推理管道——即时启发式过滤 followed by CNN-based nuanced gesture classification——作为嵌入式部署策略。该研究表明情感上有意义、保护隐私的触摸解读在计算上对于直接嵌入软质治疗陪伴物是可行的，硬件集成将在后续研究中解决。
英文摘要
Soft plush companions provide a safe and intuitive platform for affective human-robot interaction, but their deformable structure and distributed tactile signals make reliable gesture recognition difficult. This study presents a complete workflow for developing and validating compact affective-touch classifiers for an interactive plush companion. A newly collected dataset comprised 1,326 labelled recordings before curation, including interactions from 25 children, teenagers, and adults. Each classifier received 2.5-s windows containing ten capacitive channels and one accelerometer-magnitude channel. MATLAB supported acquisition, quality control, window generation, and a 468-run exploratory study of dilated one-dimensional convolutional neural networks (1D CNNs). A closely matched Python workflow then preserved participant provenance, fitted preprocessing inside each fold, and evaluated shortlisted models by 25-fold leave-one-subject-out cross-validation (LOSO-CV). On the operational 10-class task, the compact dilated CNN achieved 81.96% mean macro-F1 and 85.42% mean accuracy. The depthwise-separable CNN achieved the strongest neural result (84.50% macro-F1), whereas a linear support-vector machine using 66 predefined time-domain features achieved the best overall result (87.98% macro-F1 and 91.05% mean accuracy). Paired fold analysis showed that the linear support-vector machine and constrained random forest outperformed the compact dilated CNN, whereas differences among the tested neural models were not statistically significant after correction. Direct measurements on a 240-MHz ESP32-S3 confirmed valid deployment of the dilated CNN, depthwise-separable CNN, temporal convolutional network, linear support-vector machine, and random forest; the linear model required a 3.2 kB serialized payload and 1.405 ms mean end-to-end classifier time.
Comments25 pages, 11 figures
