An intelligent electronic appliance preferably includes a user interface, data input and/or output port, and an intelligent processor. A preferred embodiment comprises a set top box for interacting with broadband media streams, with an adaptive user interface, content-based media processing and/or media metadata processing, and telecommunications integration. An adaptive user interface models the user, by observation, feedback, and/or explicit input, and presents a user interface and/or executes functions based on the user model. A content-based media processing system analyzes media content, for example audio and video, to understand the content, for example to generate content-descriptive metadata. A media metadata processing system operates on locally or remotely generated metadata to process the media in accordance with the metadata, which may be, for example, an electronic program guide, MPEG 7 data, and/or automatically generated format. A set top box preferably includes digital trick play effects, and incorporated digital rights management features.

Intelligent electronic appliance system and method

An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation which may be efficiently processed to determine correspondence. Applications of the interface and system include a VCR, medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Ergonomic man-machine interface incorporating adaptive pattern recognition based control system

An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation that may be efficiently processed to determine correspondence. Applications of the interface and system include a video cassette recorder (VCR), medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Adaptive pattern recognition based control system and method

In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, “Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user–machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human–computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.

Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges.

An intelligent media device, comprising a packet data communications interface; a media communication interface for receiving audio and/or video data; a digital memory for persistently storing received audio and/or video data; and an intelligent server for generating a virtual interface for controlling the media communication interface and the digital memory through said packet data communications interface. The intelligent server may be adaptive. A variety of devices may be interfaced through the packet data communications interface, including telephony, imaging, videoconferencing, security, alarm, environmental control, vehicular, illumination system, domestic appliance; fluid and handling systems, as well as consumer electronic devices. A digital rights manager for enforcing a set of externally supplied restrictions associated with the received audio and/or video data may be incorporated, with a cryptographic processor for selectively cryptoprocessing audio and/or video data in dependence on said rights manager being provided to limit access to the audio and/or video data content.

Media recording device with packet data interface

The NavChair Assistive Wheelchair Navigation System is being developed to reduce the cognitive and physical requirements of operating a power wheelchair for people with wide ranging impairments that limit their access to powered mobility. The NavChair is based on a commercial wheelchair system with the addition of a DOS-based computer system, ultrasonic sensors, and an interface module interposed between the joystick and power module of the wheelchair. The obstacle avoidance routines used by the NavChair in conjunction with the ultrasonic sensors are modifications of methods originally used in mobile robotics research. The NavChair currently employs three operating modes: general obstacle avoidance, door passage, and automatic wall following. Results from performance testing of these three operating modes demonstrate their functionality. In additional to advancing the technology of smart wheelchairs, the NavChair has application to the development and testing of "shared control" systems where a human and machine share control of a system and the machine can automatically adapt to human behaviors.

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The NavChair Assistive Wheelchair Navigation System

The NavChair assistive navigation system is being developed to meet the needs of multiply handicapped people who are unable to operate available wheelchair systems. The NavChair Project was conceived as an application of mobile robot obstacle avoidance to a power wheelchair. During the course of this project, the vector field histogram (VFH) method has been adapted for use in human-machine systems and the shortcomings of the wheelchair platform have been overcome. This paper briefly reviews the VFH method, describes interesting aspects of its application to a power wheelchair, and presents an experimental evaluation of system performance. Finally, unresolved problems of obstacle avoidance in human-operated vehicles are discussed. >

An assistive navigation system for wheelchairs based upon mobile robot obstacle avoidance

Method for adaptive control of human-machine systems employing disturbance response

Methods and systems of locating and identifying categories of biological objects are discussed herein. In one aspect, a system configured to locate and identify categories of biological objects at a geographical location includes obtaining a visual or an aural characteristic of one or more biological objects sighted, comparing the obtained visual or aural characteristic with visual or aural characteristics of known biological objects stored in a database and identifying the one or more known biological objects that closely match the one or more biological objects sighted. In another aspect, a system that allows a user to efficiently record sightings of biological objects is disclosed. The system is configured to display a list of biological objects arranged based on likelihood of occurrence of biological objects in a geographical location for the user to select from.

Method and system for sharing object information

An outgrowth of this project is an investigation into methods of adaptive shared control. In particular. rnelhods of auionomous mode selection based upon observations of user behavior are being studied. This paper presents a modeling approach to monitoring human control behavior in real time; preliminary experimental results evaluate its ability to distinguish human control behaviors in a simulated system. Abstract The NavChair assistive navigation system [ I , 21 is being

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David A. Bell

Papers

The NavChair Assistive Wheelchair Navigation System

An assistive navigation system for wheelchairs based upon mobile robot obstacle avoidance

Method for adaptive control of human-machine systems employing disturbance response

Method and system for sharing object information

An identification technique for adaptive shared control in human-machine systems