SBIR Phase I: Scaling Mental Healthcare in COVID-19 with Voice Biomarkers

SBIR 第一阶段：利用语音生物标记扩大 COVID-19 的心理医疗保健

• 批准号: 2031310
• 负责人: Grace Chang
• 金额: $ 25.6万
• 依托单位: Kintsugi Mindful Wellness, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031310&HistoricalAwards=false
• 关键词: SBIR; Phase; Scaling; Mental; Healthcare

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to use voice as a real-time measurement of mental health. Transforming voice intonations into biomarkers could enable disease diagnosis and progression, supporting the $13 B virtual health care sector that was growing 27% annually prior to COVID-19. Furthermore, peer support for mental health increases engagement in self-care decreases substance use and depression, particularly for vulnerable populations. The project will advance the use of machine learning for voice mental health biomarkers in a group setting. This Small Business Innovation Research (SBIR) Phase I project will define voice biomarker features for a deep reinforcement learning based system. This project will advance a voice biomarker technology that can serve as fast behavioral health diagnostic, potentially superseding the current paper-based PHQ-9 and GAD-7 tests. The priority is to scale the optimal mix of individuals and activities for group therapy based on reward functions that maximize improvements in depression and anxiety scores. The major technical challenges include: (1) capturing nonverbal cues in a video; (2) interpreting multi-speaker audio processing; (3) creating deep reinforcement learning models to serve relevant group matches and follow-up exercises; and (4) building engaging visual feedback of progress from group meetings. The anticipated technical result of this innovation will be to define voice biomarker features and reward functions for a deep reinforcement learning based system in clinically relevant settings to improve depression and anxiety treatment outcomes.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/商业潜力是使用语音作为心理健康的实时测量。 将语音语调转化为生物标志物可以实现疾病诊断和进展，支持价值130亿B美元的虚拟医疗保健行业，该行业在COVID-19之前每年增长27%。此外，同伴对心理健康的支持增加了自我保健的参与，减少了药物使用和抑郁症，特别是对弱势群体而言。该项目将推进机器学习在群体环境中用于语音心理健康生物标志物的使用。这个小企业创新研究（SBIR）第一阶段项目将为基于深度强化学习的系统定义语音生物标记特征。该项目将推进一种声音生物标志物技术，可作为快速的行为健康诊断，有可能取代目前基于纸张的PHQ-9和GAD-7测试。首要任务是根据奖励函数，最大限度地改善抑郁和焦虑评分，为团体治疗调整个人和活动的最佳组合。主要的技术挑战包括：（1）在视频中捕捉非语言线索;（2）解释多扬声器音频处理;（3）创建深度强化学习模型，以服务于相关的小组比赛和后续练习;以及（4）从小组会议中建立引人入胜的视觉反馈。这项创新的预期技术成果将是为临床相关环境中基于深度强化学习的系统定义语音生物标志物特征和奖励功能，以改善抑郁和焦虑的治疗效果。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。