Abstract: 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.

Abstract: 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.

Abstract: Disclosed embodiments include a multi-function wearable apparatus comprising (a) a sensor module including a plurality of low power solid state kinematics sensors,(b) a microprocessor module comprising a low power microcontroller configured for device control, device status, and device communication; (c) a data storage module comprising a solid state local storage medium, said data storage module configured for sampling and storage of kinematics data; (d) a wireless communication module comprising a low power bidirectional transceiver wherein said wireless communication module is configured for communicating and synchronizing sampling time instances of said sensor module with signals from a second apparatus; and (e) a power module comprising a battery and an energy charging regulator. According to one embodiment, the wearable apparatus is a watch capable of quantifying human movement.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed embodiments relate to methods, apparatuses, and systems for characterizing gait. Specifically, disclosed embodiments are related methods, apparatuses, and systems for characterizing gait with wearable and wirelessly synchronized inertial measurement units. These include a method for gait characterization that comprises (a) detecting zero-velocity periods using two or more wearable and wirelessly synchronized movement monitoring devices including a triaxial accelerometer and a triaxial gyroscope and (b) calculating temporal measures of gait during walking by estimating the change in position and orientation during each step.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed embodiments include a movement monitoring system and apparatus for objective assessment of movement disorders of a subject, comprising (a) one or more movement monitors, and (b) a computer-implemented analysis system comprising one or more protocols and associated data analysis methods to objectively quantify movement disorders based on movement data acquired by the movement monitors. According to one embodiment, the movement monitors are robust wireless synchronized movement monitors and the protocols include one or more tests for assessment of neural control of balance.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed embodiments include a system for managing clinical data comprising: (a) a server configured to receive data from one or more external devices, and (b) a clinical data management application comprising one module for storing raw movement data received directly from at least one external device. The system is especially adapted for research in movement disorders and contains modules for investigators, collaborators, clinical subjects, and objective devices to upload movement disorders data, analyze data, obtain results of automatic analysis, publish results, and collaborate with other investigators.

Abstract: 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.

Abstract: Disclosed embodiments include a complete integrated system designed to support continuous monitoring and objective analysis of movement disorders. According to one embodiment the integrated system allows for continuous monitoring of movement disorders during normal daily activities in home and other normal daily environments, as well as in the clinic. The integrated system comprises: 1) wearable movement monitoring devices including a plurality of inertial sensors, 2) a docking station with wireless capabilities, 3) a secure web-enabled data server, and 4) statistical signal processing methods, all of which are integrated to enable monitoring and analysis of movement disorders.

Abstract: 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 analsysis.

Abstract: An integrated collaborative platform which allows for data sharing, data analysis, knowledge creation and sharing, problem solving, trading, and accelerated scientific discovery by collaborating teams which may be formed on an ad-hoc basis among users of the system is disclosed. The platform is designed to accelerate research and improve clinical care of chronic conditions. It provides a central place to facilitate interactions between the many different groups that participate in these activities. The central features of the system can be tailored to best suit each chronic condition.