Apdm, Inc

Portland State University

Lsi Logic Corporation

Lsi Corporation

Other

Skyworks Solutions, Inc.

Semler Scientific, Inc.

James McNames

Pedro Mateo Riobo Aboy

Andrew S Greenberg

Stephen Dylan Berry

Bruce Joseph Whitefield

David A Abercrombie

Emery O Sugasawara

Robert Madge

Kevin Cota

David R Turner

W Robert Daasch

Daniel R Bockelman

Timothy Brandon

Sean Pearson

Gavin Gallino

Amit Kumar

Philip David Benz

Herbert J Semler

Gary Mills

Paul J Rudolph

Mahmoud El-Gohary

Sunghan Kim

Byungsool Moon

Sean Christopher Pearson

Thaddeus T Shannon, Iii

A system includes at least one processor and at least one memory storing program instructions that, when executed by the at least one processor, cause the system to send an acoustic ranging transmitter signal between a plurality of calibration reference positions and at least one anchor point, receive an acoustic ranging receiver signal associated with the acoustic ranging transmitter signal and with distances between the plurality of calibration reference positions and the at least one anchor point, and estimate a speed of sound based on the acoustic ranging receiver signal.

Wideband acoustic positioning with precision calibration and joint parameter estimation

Disclosed embodiments include an apparatus that comprises (a) a kinematics sensor module including an accelerometer, a gyroscope, a magnetometer, or combinations thereof; and (b) a bidirectional wireless communication module configured for wirelessly synchronizing the sampling time instances of the kinematics sensor module with the sampling time instances of at least a second wearable apparatus including a second kinematics sensor module.

Wireless synchronized apparatus and system

A method for estimating joint angles of a multi-segment limb from inertial sensor data accurately estimates and tracks the orientations of multiple segments of the limb as a function of time using data from a single inertial measurement unit worn at the distal end of the limb. Estimated joint angles are computed from measured inertial data as a function of time in a single step using a nonlinear state space estimator. The estimator preferably includes a tracking filter such as an unscented Kalman filter or particle filter. The nonlinear state space estimator incorporates state space evolution equations based on a kinematic model of the multi-segment limb.

Joint angle tracking with inertial sensors

Disclosed embodiments include an apparatus for generating a plurality of movement impairment indices from one or more kinematic signals to characterize movement disorders. Additionally we disclose methods for generating a plurality of movement impairment indices from one or more kinematic signals obtained from one or more kinematic sensors, said methods implemented in a digital computer with one or more processors in order to characterize movement disorders based on spectral analysis, regularity metrics, and time-frequency analysis.

Method and apparatus for continuous measurement of motor symptoms in parkinson's disease and essential tremor with wearable sensors

Disclosed embodiments include a complete system and platform which allows for continuous monitoring of movement disorders during normal daily activities in the clinic, home, and other normal daily environments. The system comprises: 1) a wearable apparatus for continuous monitoring of movement disorders, 2) a docking station, 3) a web server, and 4) methods for statistical analysis that generate movement impairment measures. Disclosed embodiments include a wearable movement monitoring apparatus comprising of (a) a sensor module including a plurality of low power micro-electromechanical systems kinematics sensors; (b) a microprocessor module including a low power microcontroller configured for device control, device status, and device communication; (c) a data storage module including a solid state local storage medium; (d) a wireless communication module including a low power surface mount transceiver and an integrated antenna; and (e) a power and docking module including a battery, an energy charging regulator circuit, and a docking connector.

System and apparatus for continuous monitoring of movement disorders

Disclosed embodiments include a movement monitoring apparatus comprising a wireless synchronization scheme. Depending on the particular embodiment such wireless synchronization scheme is a master synchronization scheme or a mesh synchronization scheme. Additionally, in a particular embodiment, the movement monitor further comprises a robust wireless data transfer data controller. The disclosure includes a description of the complete system, namely, the wireless synchronized movement monitors with robust data transfer capabilities, the docking station, the access point, and the computer-implemented analysis system.

Wireless synchronized movement monitoring apparatus and system

Methods and apparatus for determining a cardiac parameter from cardiovascular pressure signals including arterial blood pressure (ABP) and the photoplethysmographic signal to quantify the degree of amplitude modulation due to respiration and predict fluid responsiveness are disclosed. Disclosed embodiments include a method for assessing fluid responsiveness implemented in a digital computer with one or more processors comprising: (a) measuring a cardiovascular signal, and (b) computing a dynamic index predictive of fluid responsiveness from said cardiovascular signal using a nonlinear state space estimator. According to one particular embodiment, and without limitation, the nonlinear state space estimator is based on a model for cardiovascular signals such as arterial blood pressure or plethysmogram signals, and employs a marginalized particle filter to estimate a dynamic index predictive of fluid responsiveness that is substantially equivalent to a variation in pulse pressure of said cardiovascular signal.

Method and apparatus for assessment of fluid responsiveness

Wearable movement monitor docket station

Wireless access point

Disclosed embodiments of the invention include a method, system, and apparatus to monitor cardiovascular signals such as arterial blood pressure (ABP), pulse oximetry (POX), and intracranial pressure (ICP). The system can be used to calculate and monitor useful clinical information such as heart rate, respiratory rate, pulse pressure variation (PPV), harmonic phases, pulse morphology, and for artifact removal. The method uses a statistical state-space model of cardiovascular signals and a generalized Kalman filter (EKF) to simultaneously estimate and track the cardiovascular parameters of interest such as the cardiac fundamental frequency and higher harmonics, respiratory fundamental frequency and higher harmonics, cardiac component harmonic amplitudes and phases, respiratory component harmonic amplitudes and phases, and PPV.

Method, system, and apparatus for cardiovascular signal analysis, modeling, and monitoring

Wearable movement monitor

Personal inertial monitor docking station

Personal inertial monitor

A lightweight portable probe or transducer containing a transmissive or reflective electro-optical emitter and receptor in the infrared spectrum is fitted on a subject's finger or toe. Associated electronics energize and monitor the probe, detect cardio-rhythmic fluctuations therefrom, and process digital data over a prescribed window to produce a non-invasive, qualitative or quantitative measure of the subject's circulation. In accordance with one embodiment of the invention, a simple tri-color LED array is used to indicate the subject's circulation as being normal, reduced, or borderline.

Circulation monitoring system and method

A method of isolating sources of variance in parametric data includes steps of: (a) cleaning a data set of measurements for a plurality of parameters; (b) generating a principal component analysis basis from the cleaned data set; (c) estimating an independent component analysis model from the principal component analysis basis; (d) calculating percentages of variance for the plurality of parameters explained by each component in the estimated independent component analysis model; (e) if the calculated percentages of variance indicate that a component is a minor component, then transferring control to step (f), else transferring control to step (g); (f) removing the minor component from the principal component analysis basis and transferring control to step (c); and (g) generating as output the estimated independent component analysis model wherein no component of the independent component analysis model is a minor component.

Method of isolating sources of variance in parametric data

Growing community of inventors

Portland, OR, United States of America

James McNames