Abstract: Disclosed is a robotic mobile support system configured to autonomously follow a subject with impaired mobility from a close but safe distance behind the subject and react to movements of the subject's torso and upper body to provide dynamic support for the subject and stop the subject from falling. The system comprises a mobile base vehicle, a robotic arm installed on the mobile base vehicle, and a LIDAR sensor for detecting the distance to a subject and the direction/speed of the subject. The robotic arm comprises one or more adjustable rear and side soft supports configured to support the subject and stop the subject from falling. The system can also comprise an onboard computer configured to process LIDAR data to control the movement of the mobile base vehicle and the robotic arm to stop the subject from falling.

Abstract: Disclosed is a robotic mobile support system configured to autonomously follow a subject with impaired mobility from a close but safe distance behind the subject and react to movements of the subject's torso and upper body to provide dynamic support for the subject and stop the subject from falling. The system comprises a mobile base vehicle, a robotic arm installed on the mobile base vehicle, and a LIDAR sensor for detecting the distance to a subject and the direction/speed of the subject. The robotic arm comprises one or more adjustable rear and side soft supports configured to support the subject and stop the subject from falling. The system can also comprise an onboard computer configured to process LIDAR data to control the movement of the mobile base vehicle and the robotic arm to stop the subject from falling.

Abstract: Disclosed is a robotic mobile support system configured to autonomously follow a subject with impaired mobility from a close but safe distance behind the subject and react to movements of the subject's torso and upper body to provide dynamic support for the subject and stop the subject from falling. The system comprises a mobile base vehicle, a robotic arm installed on the mobile base vehicle, and a LIDAR sensor for detecting the distance to a subject and the direction/speed of the subject. The robotic arm comprises one or more adjustable rear and side soft supports configured to support the subject and stop the subject from falling. The system can also comprise an onboard computer configured to process LIDAR data to control the movement of the mobile base vehicle and the robotic arm to stop the subject from falling.

Abstract: A system and method for modeling bio-signals in the form of non-linear stochastic oscillators by extracting time series data from a subject into a series of summary fit parameters and comparing the unknown fit parameters to a set of normative fit parameters to determine whether the subject should be included in a group or not. The method includes data collection, feature extraction and then comparison of fit parameters from a non-linear stochastic oscillator model to a normative standard to make the in or out determination for a particular group. The system includes a processor programmed to perform the steps of the method.

Abstract: A system and method for modeling bio-signals in the form of non-linear stochastic oscillators by extracting time series data from a subject into a series of summary fit parameters and comparing the unknown fit parameters to a set of normative fit parameters to determine whether the subject should be included in a group or not. The method includes data collection, feature extraction and then comparison of fit parameters from a non-linear stochastic oscillator model to a normative standard to make the in or out determination for a particular group. The system includes a processor programmed to perform the steps of the method.

Abstract: A method of extracting brain frequency sub bands corresponding to a medical condition such as Alzheimer's Disease from EEG time series data of a patient includes the steps of applying wavelet transforms to the EEG time series data to generate a continuous wavelet transformation time series at each wavelet scale, calculating Wavelet Entropy (WE) and Sample Entropy (SE) directly from the Continuous Wavelet Transformation time series at each wavelet scale, calculating arithmetic or geometric means and accumulations across scale ranges of interest; and selecting data from major brain frequency sub-bands as candidate sets of extraction features for analysis as a diagnostic signature for the medical condition. Diagnostic signatures for Alzheimer's disease are found when values of WE or SE are in certain ranges when EEG data is collected and analyzed in connection with certain analytical tasks such as an Eyes Open task.

Abstract: A modular exoskeletal device adapted to fit the lower extremities of a patient during rehabilitation. The device has only two actuators during the standing stage of rehabilitation. Two additional actuators can be added, as modules, during the walking stage of rehabilitation. The actuators are affixed to the patient and provide controlled motion to at least one of the joints of the patient. A stationary control unit is separated from the patient. The control unit communicates with and directs the actuators, and has a hybrid control algorithm, such that the actuator forces are adjusted as the patient regains control of some joint motions, which is based upon the sliding-mode control theory. A back brace is affixed to the patient and helps to keep the torso of the patient in a stable, substantially vertical position.