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对患有自闭症谱系障碍的儿童进行基于深度学习算法的视觉策略干预研究 - - 提取和检测儿童的行为特征
1Department of Applied Psychology, Lingnan University, Tuen Mun, New Territories, Hong Kong, China. lyibing3366@126.com.
European review for medical and pharmacological sciences
|June 15, 2023
概括
本研究介绍了使用人工智能进行视觉策略干预,以检测患有自闭症谱系障碍 (ASD) 的儿童的异常行为. 该方法通过提高异常行为检测准确度来增强社会融合.
科学领域:
- 神经发育障碍 神经发育障碍
- 医疗保健中的人工智能
- 行为科学 行为科学
背景情况:
- 自闭症谱系障碍 (ASD) 是一种复杂的神经发育状况,病因不明确,核心症状没有向药物治疗.
- 有效的干预措施对于改善自闭症儿童的社会融合和生活质量至关重要.
研究的目的:
- 为患有自闭症谱系障碍的儿童开发和评估一种新的视觉干预策略.
- 通过更好地检测异常行为来增强社会融合.
主要方法:
- 一种视觉策略干预,结合了时空特征融合和异常行为检测.
- 利用MotionNet进行行为特征提取,并使用光流特征 (OFF) 子网络进行增强的时间特征分析.
- 实施了一种使用注意力机制和聚类池进行动态行为分析的复杂视频数据的行为检测方法.
主要成果:
- 与现有方法相比,拟议的模型在异常行为检测方面表现出卓越的性能,在RGB中达到92.57%的准确性.
- 该方法在检测儿童的异常行为方面显示出显著的优势,比较描述器的表现高达3.64%.
- 在SDUFall,Weizmann和HMDB51数据集上进行了实验验证.
结论:
- 开发的视觉干预方法有效帮助患有自闭症谱系障碍的儿童克服社会障碍.
- 这种人工智能驱动的方法为支持ASD儿童的社会融合提供了一个有希望的途径.
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