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使用心率对ASD儿童的野外影响分析
Kamran Ali1, Sachin Shah1,2, Charles E Hughes1
1Synthetic Reality Lab, Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究开发了一个基于智能手表的框架,用于在现实环境中使用心率 (HR) 信号识别自闭症谱系障碍 (ASD) 的儿童的情绪. 该系统有效地对情绪状态进行分类,有助于为患有自闭症儿童提供个性化支持.
科学领域:
- 情感计算是一种情感计算.
- 发展心理学是发展心理学.
- 生物医学工程 生物医学工程
背景情况:
- 在患有自闭症谱系障碍 (ASD) 的儿童中识别情绪对于定制干预至关重要,但在不受控制的环境中具有挑战性.
- 现有的情绪识别方法往往缺乏现实世界的适用性,因为依赖实验室设置和有限的数据集.
- 需要一个框架来评估自闭症儿童在自然环境中的情感状态.
研究的目的:
- 通过心率 (HR) 数据,为在野外环境中识别患有自闭症儿童的情感状态提供一个新的框架.
- 从实时HR信号中开发和评估一种用于分类情绪 (积极,消极,中立) 的算法.
- 将原始HR信号分析的性能与使用离散波形变换 (DWT) 的特征提取进行比较.
主要方法:
- 使用智能手表应用程序在机器人编码和化身交互任务期间获取实时HR数据.
- 开发了一种半自动注释技术,利用人力资源数据的面部表情识别.
- 实施并比较了两个HR信号分析方法:原始信号分类和基于DWT的特征提取.
主要成果:
- 拟议的框架成功地使用HR信号在不受控制的环境中对ASD儿童的情绪状态进行了分类.
- 基于HR的情绪分类算法表现出与最先进的方法可比的性能.
- 无论是原始HR信号分析还是基于DWT的特征提取,都在情绪识别方面表现出有效性.
结论:
- 开发的框架允许使用人力资源信息对患有自闭症儿童进行现实世界影响分析.
- 这种方法有可能改善对ASD儿童情绪健康的持续监测和理解.
- 这项研究强调了在自然环境中对不引人注目的情绪识别的可行性,以支持患有自闭症的儿童.
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