University of California

Bruin Biometrics, LLC

Monterey Design Systems, Inc.

Medisens Wireless, Inc.

Synopsys, Inc.

Bruain Biometrics, LLC

Majid Sarrafzadeh

William J Kaiser

Alireza Mehrnia

Barbara M Bates-Jensen

Yeung Lam

Joseph Boystak

Michael Flesch

Lawrence Thomas Pileggi

Frank Tinghwa Wang

Salil Ravindra Raje

Mars Lan

Douglas B Boyle

Hyduke Noshadi

Gary K Yeap

Feroze P Taraporevala

Myung-Kyung Suh

Wenyao Xu

Foad Dabiri

Sharad Malik

Abhijeet Chakraborty

Lilly Shieh

Ming-Chun Huang

Hassan Ghasemzadeh

Dennis Yamamoto

Tong Gao

Christopher J Dunn

Jonathan S Woodbridge

Nitin Raut

Behrooz Yadegar

Alireza Vahdatpour

James S Koford

Miodrag Potkonjak

Victor Reggie Edgerton

Frank C Wang

Nicholas A Terrafranca

Roozbeh Jafari

David Gluss

Maxim A Batalin

Robert E Shortt

Satyamurthy Pullela

Xiaolong Li

Ani Nahapetian

Joseph T Rahmeh

Dinesh D Gaitonde

Daniel Sebastian Pan

Eric McCaughrin

Saro Meguerdichian

Emre Tuncer

Sam Jung Kim

Archie Li

Yau-Tsun Steven Li

Mahsan Rofouei

Satamurthy Pullela

Nabil Alshurafa

Jason J Liu

Ramin Ramezani

Jack Bobak Mortazavi

Christian Behrenbruch

Devin L Jindrich

Shaun S Ahmadian

Hagop Hagopian

Babak Moatamed

Kin Chung Chu

Navid Amini

Eric Collins

Arjun

Jason Holmes

Tammara Massey

Jordan Mendler

Arash Naeim

Simran Basi

Diego Villasenor

Bita Zadeh

Bijan Mapar

A handheld, conforming capacitive sensing apparatus configured to measure Sub-Epidermal Moisture (SEM) as a mean to detect and monitor the formation of pressure ulcers. The device incorporates an array of electrodes which are excited to measure and scan SEM in a programmable and multiplexed manner by a battery-less RF-powered chip. The scanning operation is initiated by an interrogator which excites a coil embedded in the apparatus and provides the needed energy burst to support the scanning/reading operation. Each electrode measures the equivalent sub-epidermal capacitance corresponding and representing the moisture content.

SEM scanner sensing apparatus, system and methodology for early detection of ulcers

A fabric-based pressure sensor array includes: (1) a first layer including M elongated conductive strips coated thereon; (2) a second layer including N elongated conductive strips coated thereon, the M elongated conductive strips extending crosswise relative to the N elongated conductive strips to define M&#xd7;N intersections; and (3) a unitary textile sheet extending between the first layer and the second layer so as to overlap the M&#xd7;N intersections, the textile sheet having a variable resistivity in response to applied pressure so as to define M&#xd7;N pressure sensors at locations corresponding to the M&#xd7;N intersections.

Fabric-based pressure sensor arrays including intersecting elongated conductive strips on opposite sides of a textile sheet

Congestive heart failure (CHF) is a leading cause of death in the United States. WANDA is a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with CHF. The first pilot study of WANDA showed the system's effectiveness for patients with CHF. However, WANDA experienced a considerable amount of missing data due to system misuse, nonuse, and failure. Missing data is highly undesirable as automated alarms may fail to notify healthcare professionals of potentially dangerous patient conditions. Embodiments of the present disclosure may utilize machine learning techniques including projection adjustment by contribution estimation regression (PACE), Bayesian methods, and voting feature interval (VFI) algorithms to predict both non-binomial and binomial data. The experimental results show that the aforementioned algorithms are superior to other methods with high accuracy and recall.

Systems and methods for missing data imputation

Systems and methods for automatically analyzing and selecting prominent channels from multi-dimensional biomedical signals in order to detect particular diseases or ailments are provided. Such systems and methods may be applied in different ways to obtain numerous benefits, such as lowering of power and processing requirements, reducing an amount of data acquired, simplifying hardware deployment, detecting non-trivial patterns, obtaining, clinical episode prognosis, improving patient care, and/or the like.

Systems and methods for automatic segment selection for multi-dimensional biomedical signals

A method for subject assessment in an environment includes receiving subject sensor data from a sensor positioned adjacent to a subject, receiving beacon signals from a beacons placed at different positions within the environment, processing the beacon signals to determine a location of the subject within the environment based on relative power levels of the received beacon signals, and processing the subject sensor data to determine an activity of the subject.

Subject assessment using localization, activity recognition and a smart questionnaire

A system includes an interface configured to receive time series data representing information from a plurality of sensors, and a processor configured to construct a behavior model based on the time series data. The processor identifies features in the time series data, divides the time series data of each of the identified features into segments, and extracts feature components from the segments. The processor further constructs a plurality of state graphs, each state graph including components connected by weighted edges, constructs a behavior graph, wherein the state graphs form vertices of the behavior graph, clusters the state graphs in the behavior graph; and selects a representative state graph from each cluster, wherein the behavior model includes the selected state graphs.

Fast behavior and abnormality detection

Systems, methods, and devices are disclosed that monitor the health status of patients. Dynamic task management functions apply data analytics to discretize continuous data values from monitored patients, and apply association rule mining techniques to prioritized required user tasks, to minimize the number of daily action items required by patients. The remaining action items maximize information gain, thereby improving the overall level of patient adherence and satisfaction without losing health monitoring effectiveness.

Task optimization in remote health monitoring systems

Systems and methods for generalized precursor pattern discovery that work with a wide range of biomedical signals and applications to detect a wide range of medical events are disclosed. In some embodiments, the methods and systems do not require domain-specific knowledge or significant reconfiguration based on the medical event being analyzed, hence it is also possible to discover patterns previously unknown to experts. In some embodiments, to build precursor pattern detection models, the system obtains annotated monitoring data. Positive and negative segments are extracted from the annotated monitoring data, and are preprocessed. Features are extracted from the preprocessed segments, and selected features are chosen from the extracted features. The selected features are classified to create the precursor pattern detection model The precursor pattern detection model may then be used in real time to detect occurrences of the medical event of interest.

Methods and systems for calculating and using statistical models to predict medical events

A method and apparatus for the detection, analysis, and identification of realistic human body and limb movements during sports actions for multiple uses comprises collecting the health information of the move, using the moves as a training and monitoring techniques in live usage settings, and as inputs to simulators and video game environments. At least one body-worn sensor transmits values wirelessly to a computing device that records the moves and calculates the health statistics then uses a machine-learning algorithm to analyze and identify the moves. These algorithms then output information and can simulate realistic moves, identify how far from these realistic moves the actual move is, and apply these moves to an electronic game. These algorithms, through various techniques, can distinguish movements, identify many different fine-grain movements, and enforce the full completion of these moves before registering them, thus preventing cheating in a realistic gaming scenario.

Near-realistic sports motion analysis and activity monitoring

Devices, methods, and systems for monitoring, classifying and encouraging activity through the use of an accelerometer or other sensor in a toy or other accessory (such as a backpack or watch) that records activity are disclosed. The activity is classified, using various motion classification algorithms, to determine the specific types and amount of activities performed and the times and duration for which they were performed. The classified data is used in the rest of the system, which allows users to view activity history, recommendations for how to improve health, and play games in which the player's game performance is effected by their activity levels. In games and on the website, feedback is also given to users to encourage and reward exercise, while handicapping users that are not active.

Devices, systems, and methods for monitoring, classifying, and encouraging activity

A location of a sensor is determined by: (1) receiving time series data including components in a plurality of dimensions, wherein the time series data correspond to measurements of the sensor that is applied to a subject; (2) determining a plurality of subsequences associated with the time series data, wherein each of the plurality of subsequences represents a characteristic pattern projected along one of the plurality of dimensions; (3) identifying a correlated subset of the plurality of subsequences as at least one instance of an activity of the subject; and (4) based on features of the correlated subset, determining the location of the sensor as applied to the subject.

Apparatus, system, and method for automatic identification of sensor placement

A sensor system and method configured to take multiple channels of sensors, and based on context and user behavior reflected in the signals, identifies specified channels for sensing according to a sensing policy. The sensing policy is used to reduce the amount of data sampled, such that it is possible to reconstruct the values of the non sampled sensors efficiently. The sensing policy is influenced by user and system's behavior and can be assigned either offline or in real time.

Energy aware sensor management for wearable medical systems optimization

Growing community of inventors

Anaheim Hills, CA, United States of America

Majid Sarrafzadeh