Smart wearables, equipped with sensors monitoring physiological parameters, are becoming an integral part of our life. In this work, we investigate the possibility of utilizing such wearables to recognize emotions in the wild. In most reviewed papers, the authors apply a similar procedure consisting of participant recruitment, stimuli preparation and annotation, signal collection and processing, self-assessment, and machine learning model learning and validation. Besides, we identified seven emotion recognition scenarios and analyzed the transition from psychological models to machine learning tasks. Even though the majority of the research was performed in the laboratory environment, we conclude that studies in the field are feasible. They require especially: (1) new self-assessment and triggering procedures adjusted to a real-life scenario, (2) more attention to the machine learning process, including suitable deep learning architectures, revision of the data imbalance problem, and subject-specific data processing, (3) adequate validation procedures, (4) consideration of the model generalizability versus personalizability, (5) comfortable devices able to provide reliable measurements in motion. Additionally, more large-scale studies are necessary to increase result credibility. We also postulate actions toward replicability and comparability of the research.

中文翻译：

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使用可穿戴设备的生理信号进行日常生活情绪识别：系统文献综述


配备监测生理参数的传感器的智能可穿戴设备正在成为我们生活中不可或缺的一部分。在这项工作中，我们研究了利用此类可穿戴设备来识别野外情绪的可能性。在大多数审阅的论文中，作者应用了类似的程序，包括参与者招募、刺激准备和注释、信号收集和处理、自我评估以及机器学习模型学习和验证。此外，我们还确定了七种情绪识别场景，并分析了从心理模型到机器学习任务的转变。尽管大部分研究是在实验室环境中进行的，但我们得出的结论是，该领域的研究是可行的。他们特别需要：（1）适应现实生活场景的新的自我评估和触发程序，（2）更多地关注机器学习过程，包括合适的深度学习架构、数据不平衡问题的修正以及特定于主题的问题。数据处理，（3）适当的验证程序，（4）考虑模型的通用性与个性化，（5）能够在运动中提供可靠测量的舒适设备。此外，需要进行更大规模的研究以提高结果的可信度。我们还假设了针对研究的可重复性和可比性采取的行动。