用可穿戴设备实时识别情绪，保护隐私且速度快。
Synheart Emotion: Privacy-Preserving On-Device Emotion Recognition from Biosignals
- 用经典集成方法在设备端分析生理信号识情绪。
- 最佳模型在手部信号上达F1=0.623，延迟仅0.05毫秒。
- 适合注重隐私和低延迟的智能穿戴场景。
人机交互日益需要识别用户隐含的情绪状态。尽管情感计算取得进展，多数系统仍依赖云端推理，存在隐私风险且延迟高，难以满足实时应用需求。本文系统评估了基于腕部光电容积脉搏波（PPG）信号的设备端情绪识别模型，涵盖经典集成方法、深度神经网络和Transformer，在WESAD压力检测数据集上进行对比。结果表明，经典集成方法在小生理数据集上显著优于深度学习，其中ExtraTrees在融合特征上达到F1=0.826，腕部仅特征上达F1=0.623，而Transformer仅得F1=0.509–0.577。通过ONNX优化部署腕部仅特征的ExtraTrees模型，实现4.08 MB存储占用、0.05毫秒推理延迟，较原实现提速152倍。此外，ONNX优化带来30.5%平均存储减少和40.1倍推理加速，验证了隐私保护型设备端情绪识别在真实可穿戴设备中的可行性。
原文摘要 · Abstract (English)
Human-computer interaction increasingly demands systems that recognize not only explicit user inputs but also implicit emotional states. While substantial progress has been made in affective computing, most emotion recognition systems rely on cloud-based inference, introducing privacy vulnerabilities and latency constraints unsuitable for real-time applications. This work presents a comprehensive evaluation of machine learning architectures for on-device emotion recognition from wrist-based photoplethysmography (PPG), systematically comparing different models spanning classical ensemble methods, deep neural networks, and transformers on the WESAD stress detection dataset. Results demonstrate that classical ensemble methods substantially outperform deep learning on small physiological datasets, with ExtraTrees achieving F1 = 0.826 on combined features and F1 = 0.623 on wrist-only features, compared to transformers achieving only F1 = 0.509-0.577. We deploy the wrist-only ExtraTrees model optimized via ONNX conversion, achieving a 4.08 MB footprint, 0.05 ms inference latency, and 152x speedup over the original implementation. Furthermore, ONNX optimization yields a 30.5% average storage reduction and 40.1x inference speedup, highlighting the feasibility of privacy-preserving on-device emotion recognition for real-world wearables.
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