SBIR Phase I: Personalized Wearable Device that Learns, Adapts to Users, and Provides Enhanced Metrics and Assessments

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力在于解决了消费者在智能健康使用案例中广泛采用可穿戴技术设备的长期障碍。主要的障碍是，当涉及到将人体佩戴的传感器数据转换为活动/手势数据时，当前的设备非常不准确。第二个障碍是，没有可靠的手势数据，就没有机制向用户提供有意义的反馈。为了解决这些障碍，我们提出了一种创新的方法来从人体佩戴的传感器数据中识别手势/活动。在一个计算轻量级但功能强大的系统中，该软件可以快速学习和适应用户的动作特征。商业化计划是将软件授权给设备制造商，让他们将其嵌入到他们的运动传感产品中。以前无法分析的产品和用例现在可以启用跟踪，从而为最终用户带来价值，并为制造商扩大经济机会。拟议中的软件将使个人能够管理他们的健身和康复养生方案；运动教练和医生将拥有前所未有的工具来监控门诊治疗；该领域将有大量行为数据可供挖掘，以获得研究意义和额外的社会结果。拟议中的项目将带来可以与可穿戴技术设备集成的软件，这些设备要么嵌入硅片，要么作为独立的移动应用程序。该软件将能够1)准确地识别多个活动/手势，尽管存在个体差异和其他噪声因素，2)提供这些活动的可靠和有效的属性(度量)，特别是运动范围、路径效率和功率，以及3)为用户反馈和研究目的提供有意义的数据。尽管现在有过多的相关设备和技术可用，但目前的技术在识别的手势数量、识别的准确性、对设备上学习的支持以及提供有效和可靠的手势度量的能力方面缺乏。拟议的技术将解决目前在功能、准确性和健壮性方面的限制和市场需求。总体的技术问题是，该软件能否被开发成足以补偿传感器漂移和运动数据中的其他类型的不确定性，达到日益复杂的物理行为和输出(度量)要求所带来的复杂程度。预期的技术成果包括新算法、基本性能界限、软件原型以及测试和验证研究。如果第一阶段演示成功，将导致这些功能和其他功能的全面软件开发。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project lies in addressing a long-standing roadblock to widespread consumer adoption of wearable tech devices for Smart Health use-cases. The primary barrier is that the current devices are woefully inaccurate when it comes to converting body-worn sensor data into activity/gesture data. A second barrier is that without reliable gesture data, there is no mechanism for providing meaningful feedback to users. To address these barriers, we propose an innovative approach to gesture/activity recognition from body-worn sensor data. The software can rapidly learn and adapt to user's motion idiosyncrasies, in a computationally lightweight but powerful system. The commercialization plan is to license the software to equipment manufacturers for them to embed it in their motion sensing products. Products and use-cases that have been impossible to analyze previously can now have tracking enabled, thus driving value for the end-user and expanding economic opportunities for the manufacturers. The proposed software will empower individuals to manage their fitness and rehabilitation regimens; sports trainers and physicians will have unprecedented tools for monitoring outpatient treatment; and the field will have troves of behavior data to mine for research implications and additional societal outcomes. The proposed project will lead to software that can be integrated with wearable tech devices, either embedded into silicon or as a stand-alone mobile app. The software will be capable of 1) accurately recognizing multiple activities/gestures despite individual variances and other noise factors, 2) providing reliable and valid attributes (metrics) of those activities, specifically range of motion, path efficiency, and power, and 3) providing meaningful data for user feedback and research purposes. Despite the plethora of related devices and technologies now available, current technology is lacking as to the number of gestures identified, accuracy of identification, support for on-device learning, and the ability to provide valid and reliable gesture metrics. The proposed technology will address current limitations in and market demand for functionality, accuracy, and robustness. The overall technical question is whether the software can be developed to adequately compensate for sensor drift and for other types of uncertainties in motion data, at the level of complexity posed by increasingly sophisticated physical behaviors and output (metric) requirements. The anticipated technical results include novel algorithms, fundamental performance bounds, software prototypes, and testing and validation studies. Phase I demonstrations, if successful, will lead to full-scale software development of these and additional functions.