HCC-SMALL: High-Resolution RFID Tracking With Applications In Gesture Recognition And Human-Computer Interaction

HCC-SMALL：高分辨率 RFID 跟踪及其在手势识别和人机交互中的应用

• 批准号: 0812458
• 负责人: Kenneth Barner
• 金额: $ 45万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812458&HistoricalAwards=false
• 关键词: HCC; SMALL; Resolution; RFID; Tracking

Current gesture recognition efforts, such as vision and direct sensing based methods, are yet to be proven effective and practical. Similarly, human-computer interaction (HCI) methods, despite considerable research efforts, remain dominated by the traditional keyboard and mouse. Improving HCI is a critical area of need, especially for individuals with disabilities who find traditional methods cumbersome (at best) or unusable (at worst). The rapid development of radio frequency identification (RFID) technologies holds the potential to significantly advance current challenges in both gesture recognition and HCI. Advantages of RFID approaches include miniaturization of passive tags, the ability to safely implant tags, and the potential to remotely track multiple tags; these characteristics combine to support the definition, recognition, and development of interfaces based on (tagged) human movements, or gestures. The PI's goal in this project is to explore RFID solutions for the most challenging gesture recognition and HCI applications, including complex gesture recognition and effective computer interfaces for individuals with severe motor impairments. These objectives will be realized through a series of coordinated research, development, and evaluation efforts. First, RFID localization algorithms will be developed utilizing harmonic radar approaches and implemented using modified commercially available components; this approach will yield significantly higher resolution than current power loss techniques, and will be capable of tracking multiple tags in cost-effective implementations. Next, implementations for small and large workspace will be carried out. The small workspace implementation will address tongue-based HCI for individuals with severe disabilities, while the large workspace implementation will be used for hand gesture recognition and computer control. Finally, the algorithms, hardware, and implementations will be rigorously evaluated for fundamental accuracy, application performance, and user efficacy. Fundamental accuracy evaluations will characterize the performance and limits of spatial-temporal RFID tag tracking, while HCI and gesture recognition capabilities will be assessed through application performance and user efficacy evaluations. Broader Impacts: This research addresses fundamentally important interface and communications issues faced by individuals with disabilities, and will include direct programmatic involvement by individuals with disabilities. Project outcomes will afford individuals with disabilities more effective interfaces and greater communications capabilities, thereby providing greater independence and, ultimately, improved social, educational, and economic opportunities. In achieving these objectives, the project will lay the foundation for a fundamentally new approach to capturing, characterizing, and exploiting human movement for HCI and gesture recognition applications. The results will have significant impact beyond the targeted applications, for instance in animal studies, human performance studies, and even security (e.g., monitoring fine movements of objects).

目前的手势识别工作，例如基于视觉和直接感测的方法，尚未被证明有效和实用。 类似地，人机交互（HCI）方法，尽管相当大的研究努力，仍然由传统的键盘和鼠标占主导地位。 改善人机交互是一个关键的需求领域，特别是对于那些发现传统方法繁琐（最好的情况下）或无法使用（最坏的情况下）的残疾人。 射频识别（RFID）技术的快速发展有可能大大推进手势识别和人机交互的当前挑战。 RFID方法的优点包括无源标签的小型化，安全植入标签的能力，以及远程跟踪多个标签的潜力;这些特性联合收割机结合起来支持基于（标记的）人类运动或手势的接口的定义，识别和开发。 PI在这个项目中的目标是探索RFID解决方案，用于最具挑战性的手势识别和HCI应用，包括复杂的手势识别和有效的计算机接口，用于严重运动障碍的个人。 这些目标将通过一系列协调一致的研究、开发和评价工作来实现。 首先，RFID定位算法将开发利用谐波雷达的方法和使用修改后的商用组件实现，这种方法将产生显着更高的分辨率比目前的功率损耗技术，并将能够跟踪多个标签的成本效益的实现。 接下来，将进行小型和大型工作空间的实现。 小工作空间的实现将解决严重残疾人士基于舌头的人机交互，而大工作空间的实现将用于手势识别和计算机控制。 最后，算法，硬件和实现将严格评估基本精度，应用程序性能和用户功效。 基本准确性评估将表征时空RFID标签跟踪的性能和限制，而HCI和手势识别能力将通过应用性能和用户功效评估进行评估。 更广泛的影响：这项研究解决了残疾人面临的根本性重要的接口和通信问题，并将包括残疾人直接参与方案。 项目成果将为残疾人提供更有效的界面和更强的沟通能力，从而提供更大的独立性，并最终改善社会、教育和经济机会。 在实现这些目标的过程中，该项目将为一种全新的方法奠定基础，该方法可以捕获、表征和利用人体运动，用于人机交互和手势识别应用。 这些结果将在目标应用之外产生重大影响，例如在动物研究，人类性能研究，甚至安全性（例如，监测物体的细微运动）。