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).

当前的手势识别工作，例如愿景和基于直接传感的方法，尚待证明有效和实用。 同样，尽管进行了大量研究工作，但人类计算机的互动（HCI）方法仍由传统键盘和鼠标主导。 改善HCI是需要的关键领域，尤其是对于那些发现传统方法繁琐（充其量）或无法使用（最坏）的残疾人。 射频识别（RFID）技术的快速发展具有显着提高手势识别和HCI当前挑战的潜力。 RFID方法的优点包括被动标签的小型化，安全植入标签的能力以及远程跟踪多个标签的潜力；这些特征结合了基于（标记的）人类运动或手势的界面的定义，识别和开发。 PI在该项目中的目标是探索最具挑战性的手势识别和HCI应用的RFID解决方案，包括复杂的手势识别和有效的重度运动障碍者的计算机接口。 这些目标将通过一系列协调的研究，开发和评估工作来实现。 首先，将使用谐波方法来开发RFID本地化算法，并使用经过修改的市售组件实施；这种方法将产生比当前功率损失技术更高的分辨率，并且能够在具有成本效益的实现中跟踪多个标签。 接下来，将对小型和大型工作区进行实施。 小型工作区实施将针对严重残疾的个体基于舌头的HCI，而大型工作空间实施将用于手势识别和计算机控制。 最后，将严格评估算法，硬件和实现的基本准确性，应用程序性能和用户功效。 基本准确性评估将表征空间式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程序性能和用户效率评估来评估。 更广泛的影响：这项研究涉及残疾人个人面临的根本重要的界面和通信问题，并将包括残疾人人的直接程序参与。 项目成果将为残疾人提供更有效的界面和更大的沟通能力，从而提供更大的独立性，并最终改善社会，教育和经济机会。 在实现这些目标时，该项目将为捕获，表征和利用人类运动的HCI和手势识别应用的新方法奠定基础。 该结果将超出目标应用，例如在动物研究，人类绩效研究甚至安全性（例如监视对象的细胞运动）之外产生重大影响。