封闭环境中人类情绪识别的行为人工智能

Gonzalo-Alberto Alvarez-Garcia;Claudia Zúñiga-Cañón;Antonio-Javier Garcia-Sanchez;Joan Garcia-Haro;Milton Sarria-Paja;Rafael Asorey-Cacheda
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引用次数: 0

摘要

了解人类在封闭环境中的情绪和行为,对于创造更具同理心和人性化的空间至关重要。温度、噪音和光线等环境因素在影响行为方面起着至关重要的作用,但个人的情绪状态也同样重要,而且往往不为人们所注意。行为人工智能(AIoB)提供了一种新颖的方法,它将环境测量与人类情绪相结合,创建出能够影响行为的空间适应过程。在本文中,我们介绍了一种利用摄像机开发的新型人类情绪传感器,该传感器在片上系统(SoC)开发板上实现。我们的方法使用卷积神经网络(CNN)来识别封闭空间中的情绪,并生成可影响人工智能生物系统中情绪状态和行为的参数。这项研究成功地将先进的 CNN 技术集成到了片上系统(SoC)平台中,实现了视频数据的实时处理。利用高能效 SoC 的多功能性将其应用扩展到了旨在改善心理健康的智能环境中。通过采用能够检测不同个体情绪状态的算法,这项研究提高了其有效性。此外,它还能根据相关设备的技术规格确定最佳的 CNN 操作。因此,开发过程包括三个步骤:(i) 收集足够的数据以建立健全的模型;(ii) 训练模型并使用测试值评估其性能;(iii) 在开发板上应用模型。我们的研究证明了在封闭区域使用 AIoB 识别和响应人类情绪的可行性。通过将情感线索与环境测量相结合,我们的系统可以创造出更个性化、更能满足个人需求的空间。我们的方法对设计公共空间以提高幸福感和情感满意度具有重要意义。
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Artificial Intelligence of Behavior for Human Emotion Recognition in Closed Environments
Understanding human emotions and behavior in closed environments is essential for creating more empathetic and humane spaces. Environmental factors, such as temperature, noise, and light, play a crucial role in influencing behavior, but individuals' emotional states are equally important and often go unnoticed. Artificial Intelligence of Behavior (AIoB) offers a novel approach that integrates environmental measurements with human emotions to create spatially adaptive processes that can influence behavior. In this article, we present a new human emotion sensor developed using video cameras and implemented on a System on Chip (SoC) development board. Our approach uses Convolutional Neural Networks (CNNs) to recognize the presence of emotions in enclosed spaces and generate parameters that can influence emotional states and behavior within an AIoB system. The research successfully integrates advanced CNN technology into a System on Chip (SoC) platform, allowing for real-time processing of video data. The versatility of utilizing an energy-efficient SoC extends its application to smart environments aimed at improving mental health. By employing algorithms capable of detecting emotional states across various individuals, the study enhances its effectiveness. Additionally, it identifies the best CNN operations tailored to the technical specifications of the devices involved. Thus, The development involves a three-step process: (i) collecting enough data to build a robust model, (ii) training the model and evaluating its performance using test values, and (iii) applying the model on the development board. Our study demonstrates the feasibility of using AIoB to recognize and respond to human emotions in closed areas. By integrating emotional cues with environmental measurements, our system can create more personalized and empathetic spaces that cater to the needs of individuals. Our approach could have significant implications for designing public spaces to promote well-being and emotional satisfaction.
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