SBIR Phase I: A Machine-Learning Tool for Social-Emotional Learning, Development, and Intervention for Remote or Hybrid Child Development Support (COVID-19)

SBIR 第一阶段：用于社交情感学习、发展和干预的机器学习工具，用于远程或混合儿童发展支持 (COVID-19)

• 批准号: 2039090
• 负责人: Nicole A Lipkin
• 金额: $ 25.6万
• 依托单位: HEYKIDDO, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039090&HistoricalAwards=false
• 关键词: SBIR; Phase; Machine; Learning; Tool

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to detect social-emotional issues in children (ages 5-12) for early intervention. A growing trend in juvenile mental health concerns was exacerbated by the recent COVID-19 pandemic and the associated disruption in child development. This project proposes a machine learning (ML) system that learns from interactions with the child and parent independently and can detect potential social-emotional concerns. The ML system enables personalized evaluation and monitoring outside a clinical setting in a remote or hybrid context.This Small Business Innovation Research (SBIR) Phase I project advances algorithms for integration into a social-emotional skill building curriculum. The project proposes: 1) User segmentation based on a comprehensive assessment of social-emotional functioning, 2) Correlation of the relevancy and effectiveness of modules to segmented users, and 3) Collection of information to identify social emotional deficits (red flags). These activities enable a feedback loop to learn and deliver recommendations for each child-parent dyad, personalizing learning in real-time. These algorithms learn from behavioral, social, and emotional inputs from both the parent and child when they engage with the technology. The system will also detect red flags correlated with social and emotional deficits, promoting early intervention.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是检测儿童（5-12岁）的社会情感问题，以便早期干预。最近的COVID-19大流行及相关的儿童发育中断加剧了青少年心理健康问题日益严重的趋势。该项目提出了一种机器学习（ML）系统，该系统可以独立地从与孩子和父母的互动中学习，并可以检测潜在的社交情感问题。 ML系统可以在远程或混合环境中实现临床环境外的个性化评估和监测。这个小型企业创新研究（SBIR）第一阶段项目推进了整合到社交情感技能培养课程中的算法。 该项目建议：1）基于社交情绪功能的综合评估的用户细分，2）模块与细分用户的相关性和有效性的相关性，以及3）收集信息以识别社交情绪缺陷（红旗）。这些活动使反馈循环能够学习并为每个孩子-父母二元组合提供建议，实时个性化学习。这些算法从父母和孩子在使用技术时的行为、社交和情感输入中学习。该系统还将检测与社交和情感缺陷相关的危险信号，促进早期干预。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。