Engineers have been working towards replicating the complexity of the human hand for use in applications ranging from manufacturing to human-computer interaction to wearable consumer technology. This project will build a platform for hand-gesture research, compiling and consolidating hand gesture data and enabling research communities to collaborate for more efficient development.

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.