Carnegie Mellon University

Qeexo, Co.

Other

Disney Enterprises, Inc.

University of Rochester

Disney Interprises, Inc.

Christopher Harrison

Julia Schwarz

Gierad Laput

Robert Xiao

Scott E Hudson

Yang Zhang

Robert Bo Xiao

Bo Robert Xiao

Rajen Bhatt

Yanfei Chen

Sang Won Lee

Karan Ashok Ahuja

Mayank Goel

Ivan Poupyrev

Paul Mansky

Douglas H Adamson

Alanson P Sample

Paul Michael Chaikin

Jeffrey P Bigham

Richard A Register

Chouchang Yang

Karl D D Willis

Walter S Lasecki

Xiang Chen

HasanMMasoud Smeir

Jason Wiese

Yuvraj Agarwal

Sudershan Boovaraghavan

Chen Chen

Abhijit Hota

Hasan M Masoud Smeir

Miri Park

Embodiments are provided to recognize features and activities from an audio signal. In one embodiment, a model is generated from sound effect data, which is augmented and projected into an audio domain to form a training dataset efficiently. Sound effect data is data that has been artificially created or from enhanced sounds or sound processes to provide a more accurate baseline of sound data than traditional training data. The sound effect data is augmented to create multiple variants to broaden the sound effect data. The augmented sound effects are projected into various audio domains, such as indoor, outdoor, urban, based on mixing background sounds consistent with these audio domains. The model is installed on any computing device, such as a laptop, smartphone, or other device. Features and activities from an audio signal are then recognized by the computing device based on the model without the need for in-situ training.

System and method for acoustic activity recognition

Systems and techniques for facilitating hand activity sensing are presented. In one example, a system obtains, from a wrist-worn computational device, hand activity data associated with a sustained series of hand motor actions in performance of a human task. The system also employs a machine learning technique to determine classification data indicative of a classification for the human task.

Method and system for hand activity sensing

The disclosure facilitates fingerprint recognition, user authentication, and prevention of loss of control of personal information and identity theft. The disclosure also facilitates identifying spoofed fingerprint authentication attempts, and/or securing user touch sensitive devices against spoofed fingerprint authentication attempts.

Method and system to prevent identity theft for fingerprint recognition enabled touch screen devices

Internet of Things ('IoT') appliances are gaining consumer traction, from smart thermostats to smart speakers. These devices generally have limited user interfaces, most often small buttons and touchscreens, or rely on voice control. Further, these devices know little about their surroundings&#x2014;unaware of objects, people and activities around them. Consequently, interactions with these 'smart' devices can be cumbersome and limited. The present invention presents an approach that enriches IoT experiences with rich touch and object sensing, offering a complementary input channel and increased contextual awareness. The present invention incorporates a range sensing technology into the computing devices, providing an expansive ad hoc plane of sensing just above the surface with which a device is associated. Additionally, the present invention can recognize and track a wide array of objects, including finger touches and hand gestures. The present invention can also track people and estimate which way they are facing.

System, method and devices for touch, user and object sensing for IoT experiences

The disclosure describes a wearable, low-cost and low-power Electrical Impedance Tomography system for gesture recognition. The system measures cross-sectional bio-impedance using electrodes on wearers' skin. Using all-pairs measurements, the interior impedance distribution is recovered, which is then fed to a hand gesture classifier. This system also solves the problem of poor accuracy of gesture recognition often observed with other gesture recognition approaches.

System for wearable, low-cost electrical impedance tomography for non-invasive gesture recognition

A touch sensing system is configured to determine a state of a paper material indicative of a touch input on the paper material. The actions include receiving one or more values of features representing physical properties of a paper material. The system generates, by a pair of electrodes in a conductive material that is electrically connected with the paper material, an electric field in the conductive material. The paper material is configured to shunt current from the conductive material when the paper material is touched. The system measures the electric field in the conductive material in the conductive material. The system generates an approximation of the electric field in the conductive material. The system determines with a classifier a state of the paper material indicative of a touch input on the paper material.

Touch-sensing system including a touch-sensitive paper

A system and method for detecting when a user contacts one arm to an opposing arm. The system comprises an emitter worn by the user on one arm and a receiver located on an opposing arm, on the user's head, or on a different part of the user's body. Contact, or touch, is indicated by comparing the value of an electrical signal flowing along a path establish, in part, through the user's skin to a threshold. When touch is initiated, multiple electrical paths are created, including one that generates airborne signal emanating from the user's body.

System and method for robust touch detection for on-skin augmented reality/virtual reality interfaces

Disclosed herein is a method and system a system that enables users to simply tap their smartphone or other electronic device to an object to discover and rapidly utilize contextual functionality. As described herein, the system and method provide for recognition of physical contact with uninstrumented objects, and summons object-specific interfaces.

System for enabling rich contextual applications for interface-poor smart devices

Embodiments disclosed herein describe a method of fabricating soft, flexible fibers using a 3D printer having an extrusion head. Embodiments of the method further include termination techniques to allow a series of fibers to be fabricated on the same object. Aspects of the certain embodiments offer a range of design parameters for controlling the properties of single strands and also of bundles of fibers. The method extends the capabilities of 3D printing without requiring any new hardware.

Method of fabricating soft fibers using fused deposition modeling

A system for classifying touch events includes a touch screen configured to display an interactive element, one or more acoustic sensors coupled to the touch screen, a touch event detector configured to monitor the one or more acoustic sensors and to save acoustic signals sensed by the one or more acoustic sensors, wherein the touch event detector is further configured to detect touch events in which the interactive element is touched by a first or a second finger part of a user, and wherein the touch events result in generating the acoustic signals, and an acoustic classifier configured to classify the acoustic signals.

Method and apparatus for classifying finger touch events

A method for sensing touch inputs to a digital equipment is provided, comprising the steps of sensing a sound/vibration signal generated by a touch, digitally processing the sensed sound/vibration signal, and determining the type of touch means that has generated the touch and the intensity of the touch based on the properties of the processed sound/vibration signal, wherein the properties include at least one of the following properties of the sound/vibration signal in time domain: maximum amplitude, average amplitude, average frequency, mean, standard deviation, standard deviation normalized by overall amplitude, variance, skewness, kurtosis, sum, absolute sum, root mean square (RMS), crest factor, dispersion, entropy, power sum, center of mass, coefficients of variation, cross correlation, zero-crossings, seasonality, DC bias, or the above properties computed for the first, second, third or higher order of derivatives of the sound/vibration signal; and the following properties of the sound/vibration signal in frequency domain: spectral centroid, spectral density, spherical harmonics, total average spectral energy, band energy ratios for every octave, log spectral band ratios, linear prediction-based cepstral coefficients (LPCCs), perceptual linear prediction (PLP) cepstral coefficients, mel-frequency cepstral coefficients, frequency topology, or the above properties computed for the first, second, third or higher order of derivatives of a frequency domain representation of the sound/vibration signal. There is also provided a device for sensing touch inputs.

Methods and systems for processing touch inputs based on touch type and touch intensity

A method and apparatus for determining pitch and yaw of an elongated interface object as it interacts with a touchscreen surface. A touch image is received, and this touch image has at least a first area that corresponds to an area of the touchscreen that has an elongated interface object positioned at least proximate to it. The elongated interface object has a pitch and a yaw with respect to the touchscreen surface. A first transformation is performed to obtain a first transformation image of the touch image, and a second transformation is performed to obtain a second transformation image of the touch image. The first transformation differs from the second transformation. The yaw is determined for the elongated interface object based on both the first and second transformation images. The pitch is determined based on at least one of the first and second transformation images.

Determining pitch and yaw for touchscreen interactions

A method of classifying touch screen events uses known non-random patterns of touch events over short periods of time to increase the accuracy of analyzing such events. The method takes advantage of the fact that after one touch event, certain actions are more likely to follow than others. Thus if a touch event is classified as a knock, and then within 500 ms a new event in a similar location occurs, but the classification confidence is low (e.g., 60% nail, 40% knuckle), the classifier may add weight to the knuckle classification since this touch sequence is far more likely. Knowledge about the probabilities of follow-on touch events can be used to bias subsequent classification, adding weight to particular events.

Growing community of inventors

Pittsburgh, PA, United States of America

Christopher Harrison