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CCRI: Planning: Establishing A Hand-Gesture Research Platform for Behavior Biometrics and Cognitive Robotics (HGRP)

CCRI：规划：建立行为生物识别和认知机器人（HGRP）手势研究平台

基本信息

批准号：
1925709
负责人：
Dijiang Huang
金额：
$ 10万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2021-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925709&HistoricalAwards=false
关键词：
CCRI Planning Establishing Hand Gesture

项目摘要

The hand is one of the most complex and beautiful pieces of natural engineering in the human body, and it represents a triumph of complex engineering, exquisitely evolved to perform a range of tasks. Hand-gestures have been used for many areas and how to model hand-gestures has broad and profound impact on addressing the nation's priorities and societal needs, e.g., manipulators and co-robot in manufacturing, natural human-computer interaction for virtual reality, wearable platforms in consumer electronics, hand-gesture biometrics in cybersecurity, etc. In this project, one of the research goals is to build a hand-gesture research platform, Hand-Gesture Research Platform (HGRP). HGRP targets at enabling researchers to easily access various hand-gesture data to validate their hand-gesture recognition models, benchmark the performance of newly developed algorithms, and compare with research outcomes from others. Moreover, HGRP is used to gather research communities' feedback based on existing cutting-edge hand-gesture research to prioritize the need on establishing a hand-gesture focused computing research infrastructure.The HGRP framework is based on a cloud computing platform and is used to enable research capabilities in the following areas: (a) hand-gesture biometrics; (b) cognitive Robotics; and (c) programmable interfaces for gesture-based data processing and visualization. HGRP is composed by the following salient features:*Data collection based on two major types of sensors: (a) motion detection sensors, e.g., wearable sensors such as watch, wrist band, on figure sensors, data motion gloves, infrared motion detection sensors, etc., and (b) video sensors such as leap motion sensors, video recorders, etc. The detected hand-gesture data is sent to data storage for processing and storing. *Data are collected and stored on an objective storage service, and frequently used data is stored in memory storage. *A GPU-based private cloud is established to allow researchers to implement well- known hand-gesture data processing models and establish benchmarking models. HGRP also allows researchers to submit computation tasks for evaluations and comparative studies. HGRP provides a web-based data collection, processing, sharing, and storing APIs that allow researchers remotely to access the hand-gesture repository for data retrieval, processing, sharing, storing through web services APIs.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.

手是人体中最复杂和最美丽的自然工程之一，它代表了复杂工程的胜利，精心进化以执行一系列任务。手势已被用于许多领域，如何建模手势对解决国家的优先事项和社会需求具有广泛而深刻的影响，例如，制造业中的机械手和协作机器人、虚拟现实中的自然人机交互、消费电子产品中的可穿戴平台、网络安全中的手势生物识别技术等。本项目的研究目标之一是建立手势研究平台，手势研究平台（HGRP）。HGRP的目标是使研究人员能够轻松访问各种手势数据，以验证他们的手势识别模型，对新开发的算法的性能进行基准测试，并与其他人的研究成果进行比较。HGRP框架基于云计算平台，用于实现以下领域的研究能力：（a）手势生物识别;（B）认知机器人;（b）手势识别技术。以及（c）用于基于手势的数据处理和可视化的可编程接口。HGRP由以下显著特征组成：* 基于两种主要类型的传感器的数据收集：（a）运动检测传感器，例如，可穿戴传感器，例如手表、腕带、数字传感器、数据运动手套、红外运动检测传感器等，以及（B）视频传感器，例如跳跃运动传感器、视频记录器等。检测到的手势数据被发送到数据存储器以进行处理和存储。* 数据收集并存储在目标存储服务上，经常使用的数据存储在内存中。* 建立基于GPU的私有云，允许研究人员实现众所周知的手势数据处理模型并建立基准模型。HGRP还允许研究人员提交计算任务进行评估和比较研究。HGRP提供基于Web的数据收集、处理、共享和存储API，允许研究人员远程访问手势存储库，通过Web服务API进行数据检索、处理、共享和存储。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。