SBIR Phase I: New Wearable for Body Focused Repetitive Behavior Detection

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将源于为那些遭受身体重复性行为的人提供准确的实时感知解决方案。超过4%的美国人有抠皮肤、拔头发和咬指甲的问题，由于治疗费用的障碍和缺乏有效的工具来促进行为改变，他们中的大多数人求助于化妆、手套、假发甚至纹身来掩盖问题。虽然行为疗法，特别是习惯逆转训练，已经显示出效果，但这种方法传统上受到不可靠的日志记录、缺乏治疗途径以及患者由于缺乏意识而难以实时执行的困扰。虽然实时感知设备确实存在，但在检测精度方面仍有改进的余地。该项目将研究可穿戴设备中新型传感器系统的可行性，该系统可以显着提高对bfrb相关行为的检测精度。该小企业创新研究第一阶段项目将开发和验证一种新型可穿戴传感系统，用于检测与bfrb相关的细微运动，适用于大规模生产。我们相信所提出的可穿戴系统可以提高主流行为治疗方法的疗效。为了实现这些目标，早期研究将集中在三个主要目标上。首先，该团队将在严格控制的测试装置中研究所提议传感器的最佳电气和空间配置。其次，团队将把最佳配置集成到适合测试的设备中，并验证个人传感器输出的完整性。最后，该团队将在理想的低噪声条件下开发一种概念验证BFRB识别算法。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from providing an accurate real-time awareness solution for those who suffer from body-focused repetitive behaviors. Over 4% of Americans suffer from skin picking, hair pulling, and nail biting, the majority of whom resort to covering up the problem with makeup, gloves, wigs, and even tattoos due to treatment cost barriers and lack of effective tools to facilitate behavior change. While behavior therapy, and in particular habit reversal training, has shown efficacy, this method is traditionally burdened by unreliable journaling, a lack of access to treatment, and difficulty for patients to perform in real-time because of a lack of awareness. While real-time awareness devices do exist, there is room for improvement in detection accuracy. This project will examine feasibility of a novel sensor system within a wearable device that can significantly improve detection accuracy of BFRB-related behaviors.This Small Business Innovation Research Phase I project will develop and validate a novel wearable sensing system used to detect subtle movements associated with BFRBs that is suitable for large-scale manufacturing. We believe the proposed wearable system can improve the efficacy of leading behavior therapy methods. To accomplish these goals, early studies will focus on three main objectives. First, the team will investigate the best electrical and spatial configuration of the proposed sensors in a tightly controlled test setup. Second, the team will integrate the optimal configuration into a device suitable for testing and validate the integrity of the sensor output on individuals. Finally, the team will develop a proof-of-concept BFRB recognition algorithm under ideal, low-noise conditions.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.