Arizona State University

General Electric Company

Other

Mayo Foundation for Medical Education and Research

Sandeep Narendra Gupta

Ayan Banerjee

David Martin Mills

Aaron Mark Dentinger

Kedar Anil Patwardhan

Yongjian Yu

Thomas Kwok-Fah Foo

Vinaya Chakati

Prajwal Paudyal

Madhurima Pore

Gopal Biligeri Avinash

Lowell Scott Smith

Dattesh Dayanand Shanbhag

Rakesh Mullick

Graham Arnold Wright

Kai Erik Thomenius

Luca Marinelli

David Thomas Gering

Ameet Shridhar Deshpande

Tsur Bernstein

Daniel Alfredo Herzka

Suresh Kumar Choubey

Dominic Michael Graziani

Glenn Scott Slavin

Kumar Rajamani

Yogish C Kudva

Imane Lamrani

Mohammad Javad Sohankar Esfahani

Seyed Koosha Sadeghi Oskooyee

John W Zingsheim

Garth M Beache

Suruchi Gupta

Jinesh Doshi

Marshall Sussman

Jayant Kshirsagar

Timothy J McCoy

Zahra Abbasi

Mohsin S Dawoodbhai

April Dorcas

Ashish M Shah

Mani Sundararajan

The present disclosure discloses a data science driven system and associated methodology framework for mining relevant knowledge from input/output traces of a closed-loop control system for glycemic control to aid in actionable feedback for Type 1 diabetes management. The framework takes Continuous Glucose Monitor (CGM) records and insulin pump records from a closed loop control (CLC) system and interprets the CGM records and insulin pump data to provide medically useful information, wherein the closed-loop control system is operable for operation in an 'auto mode' (AP) and a “manual mode” (SAP).

Systems and methods for a data driven framework for actionable feedback in insulin management for type 1 diabetic subjects on artificial pancreas

Various embodiments of systems and methods for mobile crowd sensing applications are disclosed herein.

Systems and methods for crowdsourcing real-time mobile crowd sensing applications

A system and associated methods/processes includes a sensor operable to capture sensor data indicative of a gesture; and a processor in communication with a memory and the sensor. The processor is configured to execute instructions stored in the memory, which, when executed, cause the processor to access the sensor data and decompose the gesture into a canonical gesture form defining a string of gesture components arranged in a spatio-temporal order.

Systems and methods for language driven gesture understanding

Methods and systems for utilizing co-simulation of a physical model and a self-adaptive predictive controller using hybrid automata are described. For example, there is disclosed a self-adaptive predictive control system to generate an initial patient model with initial patient model settings (k, k, . . . , k) and execute a program that takes as input, the initial patient model settings as configuration parameters that specify at least a plurality of initial states. Such a system further receives new patient inputs and generates hybrid automata as a variation of the initial states by applying the new patient inputs to the initial patient model. The system further derives reachable states from the hybrid automata and outputs all reachable states computed. The system further detects changes and responsively generates a new patient predictive model with new parameter settings (k', k&#x2032;, . . . , k&#x2032;), iteratively repeating until a termination criterion is satisfied. Other related embodiments are disclosed.

Systems, methods, and apparatuses for utilizing co-simulation of a physical model and a self-adaptive predictive controller using hybrid automata

Systems and methods are provided for controlling predictive medical monitoring. A non-linear-predictive-guide model estimates a patient parameter that is used as a guide in a deep neural network for improving accuracy of estimation by the deep neural network. The guide model generates a guiding first estimated patient parameter based on the guide model and patient input data. The deep neural network generates a second estimated patient parameter based on the deep neural network, the patient input data, and the guiding first estimated patient parameter. The deep neural network includes an input layer that receives the guiding first estimated patient parameter, and hidden layers including respective artificial neurons configured to perform a linear or nonlinear transformation on output of at least one artificial neuron from an adjacent layer in the deep neural network. An output layer receives at least one output from a hidden layer.

Model guided deep learning approach towards prediction of physical system behavior

Various embodiments of systems and methods for a learning environment for accessible virtual computer science education are disclosed.

Systems and methods for facilitating accessible virtual education

Various embodiments disclosed herein relate to systems and methods for an intelligent autonomous decision making module that maximizes the return of the most scientifically relevant dataset over the low bandwidth for experts to analyze further. A rule based knowledge extraction methodology is disclosed, guided by expert knowledge for all scientifically relevant geological landforms with respect to expert selected attributes. The datasets are subsequently prioritized based on how novel the instances are with respect to its rule and is used to update the rules. The effectiveness of the proposed approach is then determined by evaluating how acceptable the prioritization order is to experts and explaining the decisions to increase the interpretability of the assigned priority.

Systems and methods for expert guided rule based identification of relevant planetary images for downlinking over limited bandwidth

Systems and methods are described for facilitating non-verbal communication. An electronic processor receives data from the hand-gesture sensor indicative of one or more gestures performed by a user, determines at least one word or phrase corresponding to the data from the hand-gestures sensor, and outputs a text representation of the at least one word or phrase on the display screen. Some systems output the text representation as a natural language sentence based on the data from the hand-gesture sensor and linguistic prosody information determined based, at least in part, on image data of the user captured while performing the one or more gesture. Some systems also includes at least one brain sensor and generate an alternative natural language text sentence in response to detecting a signal from the brain sensor indicative of a contradiction response after displaying the natural language text sentence on the display screen.

Gesture recognition and communication

Methods and systems using magnetic resonance and ultrasound for tracking anatomical targets for radiation therapy guidance are provided. One system includes a patient transport configured to move a patient between and into a magnetic resonance (MR) system and a radiation therapy (RT) system. An ultrasound transducer is also provided that is hands-free and electronically steerable, securely attached to the patient, such that the ultrasound transducer is configured to acquire four-dimensional (4D) ultrasound images concurrently with one of an MR acquisition or an RT radiation therapy session. The system also includes a controller having a processor configured to use the 4D ultrasound images and MR images from the MR system to control at least one of a photon beam spatial distribution or intensity modulation generated by the RT system. The system determines the previously-acquired correct MR images that represent a specific motion state at some time, t, by a plurality of transformations that allow the representation of the position of fiducial markers in the corresponding ultrasound images to match that of a prior ultrasound acquisition.

Methods and systems to determine respiratory phase and motion state during guided radiation therapy

A Brain-Mobile Interface (BMoI) system is provided. A control circuit is configured to execute a predictive model to generate a defined number of predicted signal features in future time based on a number of signal features extracted from a first type sensory data (e.g., electroencephalogram (EEG) data). A predicted future mental state(s) can thus be generated based on the number of predicted signal features and used to trigger a corresponding action(s) in a BMoI application(s). In a non-limiting example, a second type sensory data (e.g., electrocardiogram (ECG) data) can be used to improve accuracy of the predictive model. By using the predicted signal features to generate the predicted future mental state(s) to control the BMoI application(s), it is possible to duty-cycle the BMoI system to help reduce power consumption and processing latency, thus allowing the BMoI application(s) to operate in real-time with improved accuracy and power consumption.

Brain-mobile interface optimization using internet-of-things

Methods and systems are described for sensing and recovery of a biological signal using generative-model-based compressive sensing. A transformation is applied to sparsify the quasi-periodic signal removing morphology parameters and leaving temporal parameters. The sparsified signal is sampled and the sampled signal data is transmitted to a base station. A homotopy recovery algorithm is applied to the received sampled signal data by the base station to recover the temporal parameters of the biological signal. Generative modelling is applied using previously captured morphology parameters to generate a reconstructed signal. Finally, the reconstructed signal is adjusted and scaled based on the recovered temporal parameters to provide a reconstructed signal that is diagnostically equivalent to the original biological signal.

Compressive sensing of quasi-periodic signals using generative models

A system and method for detecting motion is presented. The system and method includes identifying a region of interest in the plurality of images corresponding to a subject of interest. Furthermore, the system and method includes determining signal characteristics corresponding to the region of interest. In addition, the system and method includes generating a composite signal, where the composite signal comprises an aggregate of the signal characteristics corresponding to the region of interest. The system and method also includes analyzing the composite signal to detect motion in the region of interest.

System and method for detection of motion in dynamic medical images

Systems and methods are described for automatically identifying a food item. A color image and a thermal image are received by an electronic processor with a first food item in the field of view of both the color image and the thermal image. The electronic processor identifies a region of pixels in the color image that corresponds to the first food item based at least in part on a temperature intensity of the pixels in the identified region of pixels relative to other pixels in the thermal image. At least one feature is extracted from the identified region of pixels in the color image corresponding to the first food item and the electronic processor automatically identifies a type of food corresponding to the first food item based at least in part on the at least extracted feature.

Real-time diet assessment and food identification using thermal imaging

A global cross-carrier system for distributing mobile data. A sharer utilizes a mobile application to access the data share server to enable sharing of any unused data in the sharer's data plan with a wireless network carrier. A borrower associated with another carrier can utilize a mobile application to access the data share server to enable borrowing of any available data shared by other users. A data share server might optionally purchase data from one or more carriers, the one or more carriers need not be located within the same country. The sharer has an option of purchasing a data plan from the data share server. A borrower has the option of purchasing on-demand data in the form of a session if there are no sharers in the data pool.

System and method for distributing mobile data

Growing community of inventors

Niskayuna, NY, United States of America

Sandeep Narendra Gupta