Engineering topics

Beach, Glenn

Publications and source records attributed to Beach, Glenn.

3 records

NASA NTRS2011

Enabling Disabled Persons to Gain Access to Digital Media

A report describes the first phase in an effort to enhance the NaviGaze software to enable profoundly disabled persons to operate computers. (Running on a Windows-based computer equipped with a video camera aimed at the user s head, the original NaviGaze software processes the user's head movements and eye blinks into cursor movements and mouse clicks to enable hands-free control of the computer.) To accommodate large variations in movement capabilities among disabled individuals, one of the enhancements was the addition of a graphical user interface for selection of parameters that affect the way the software interacts with the computer and tracks the user s movements. Tracking algorithms were improved to reduce sensitivity to rotations and reduce the likelihood of tracking the wrong features. Visual feedback to the user was improved to provide an indication of the state of the computer system. It was found that users can quickly learn to use the enhanced software, performing single clicks, double clicks, and drags within minutes of first use. Available programs that could increase the usability of NaviGaze were identified. One of these enables entry of text by using NaviGaze as a mouse to select keys on a virtual keyboard.

Beach, Glenn↗

NASA NTRS2006

Gesture-Controlled Interfaces for Self-Service Machines

Gesture-controlled interfaces are software- driven systems that facilitate device control by translating visual hand and body signals into commands. Such interfaces could be especially attractive for controlling self-service machines (SSMs) for example, public information kiosks, ticket dispensers, gasoline pumps, and automated teller machines (see figure). A gesture-controlled interface would include a vision subsystem comprising one or more charge-coupled-device video cameras (at least two would be needed to acquire three-dimensional images of gestures). The output of the vision system would be processed by a pure software gesture-recognition subsystem. Then a translator subsystem would convert a sequence of recognized gestures into commands for the SSM to be controlled; these could include, for example, a command to display requested information, change control settings, or actuate a ticket- or cash-dispensing mechanism. Depending on the design and operational requirements of the SSM to be controlled, the gesture-controlled interface could be designed to respond to specific static gestures, dynamic gestures, or both. Static and dynamic gestures can include stationary or moving hand signals, arm poses or motions, and/or whole-body postures or motions. Static gestures would be recognized on the basis of their shapes; dynamic gestures would be recognized on the basis of both their shapes and their motions. Because dynamic gestures include temporal as well as spatial content, this gesture- controlled interface can extract more information from dynamic than it can from static gestures.

Cohen, Charles J.↗

NASA NTRS2004

Gesture-controlled interfaces for self-service machines and other applications

A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.

Cohen, Charles J.↗