Miranda Jose A, Canabal Manuel F, Gutiérrez-Martín Laura, Lanza-Gutierrez Jose M, Portela-García Marta, López-Ongil Celia
Electronic Technology Department, Universidad Carlos III of Madrid, 28911 Leganés, Madrid, Spain.
Department of Computer Science, University of Alcalá, 28871 Alcalá de Henares, Madrid, Spain.
Sensors (Basel). 2021 Feb 25;21(5):1587. doi: 10.3390/s21051587.
Emotion recognition is benefitting from the latest research into physiological monitoring and wireless communications, among other remarkable achievements. These technologies can indeed provide solutions to protect vulnerable people in scenarios such as personal assaults, the abuse of children or the elderly, gender violence or sexual aggression. Cyberphysical systems using smart sensors, artificial intelligence and wearable and inconspicuous devices can serve as bodyguards to detect these risky situations (through fear-related emotion detection) and automatically trigger a protection protocol. As expected, these systems should be trained and customized for each user to ensure the best possible performance, which undoubtedly requires a gender perspective. This paper presents a specialized fear recognition system for women based on a reduced set of physiological signals. The architecture proposed is characterized by the usage of three physiological sensors, lightweight binary classification and the conjunction of linear (temporal and frequency) and non-linear features. Moreover, a binary fear mapping strategy between dimensional and discrete emotional information based on emotional self-report data is implemented to avoid emotional bias. The architecture is evaluated using a public multi-modal physiological dataset with two approaches (subject-dependent and subject-independent models) focusing on the female participants. As a result, the proposal outperforms the state-of-the-art in fear recognition, achieving a recognition rate of up to 96.33% for the subject-dependent model.
情感识别正受益于对生理监测和无线通信等方面的最新研究以及其他显著成果。这些技术确实能够提供解决方案，以保护处于诸如人身攻击、虐待儿童或老人、性别暴力或性侵犯等场景中的弱势群体。使用智能传感器、人工智能以及可穿戴且不引人注意的设备的信息物理系统可以充当保镖，检测这些危险情况（通过与恐惧相关的情感检测）并自动触发保护协议。不出所料，这些系统应该针对每个用户进行训练和定制，以确保尽可能最佳的性能，这无疑需要从性别角度出发。本文提出了一种基于一组简化生理信号的针对女性的专门恐惧识别系统。所提出的架构的特点是使用三个生理传感器、轻量级二元分类以及线性（时间和频率）与非线性特征的结合。此外，基于情感自我报告数据实施了维度和离散情感信息之间二元恐惧映射策略，以避免情感偏差。使用一个公共多模态生理数据集，通过两种方法（依赖于受试者和独立于受试者的模型）对架构进行评估，重点关注女性参与者。结果，该提议在恐惧识别方面优于现有技术，依赖于受试者的模型实现了高达96.33%的识别率。
