用可穿戴设备和量子机器学习识别老人情绪，更隐私更准确。
Emotion Recognition in Older Adults with Quantum Machine Learning and Wearable Sensors
- 结合可穿戴传感器与量子支持向量机，提升情绪识别性能。
- 所有情绪类别的F1分数超80%，召回率最高提升36%。
- 适合阿尔茨海默病、创伤后应激障碍等沟通困难人群使用。
我们探究仅通过生理信号推断情绪状态的可行性，为传统面部识别提供一种保护隐私的替代方案。通过对比经典机器学习算法与基于量子核的混合量子机器学习（QML）方法，结果表明量子增强的支持向量机在所有情绪类别中均优于经典模型，即使在小规模数据集上训练也表现优异。所有类别的F1分数超过80%，召回率最高提升约36%。可穿戴传感器数据与量子机器学习的融合不仅提高了识别准确率和鲁棒性，还实现了无感化情绪监测。该方法对存在沟通障碍的群体（如阿尔茨海默病及相关痴呆症患者、创伤后应激障碍退伍军人）具有重要应用前景。研究为临床及辅助生活场景中的被动情感监测奠定了初步基础。
原文摘要 · Abstract (English)
We investigate the feasibility of inferring emotional states exclusively from physiological signals, thereby presenting a privacy-preserving alternative to conventional facial recognition techniques. We conduct a performance comparison of classical machine learning algorithms and hybrid quantum machine learning (QML) methods with a quantum kernel-based model. Our results indicate that the quantum-enhanced SVM surpasses classical counterparts in classification performance across all emotion categories, even when trained on limited datasets. The F1 scores over all classes are over 80% with around a maximum of 36% improvement in the recall values. The integration of wearable sensor data with quantum machine learning not only enhances accuracy and robustness but also facilitates unobtrusive emotion recognition. This methodology holds promise for populations with impaired communication abilities, such as individuals with Alzheimer's Disease and Related Dementias (ADRD) and veterans with Post-Traumatic Stress Disorder (PTSD). The findings establish an early foundation for passive emotional monitoring in clinical and assisted living conditions.
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