Rehabilitation Institute of Chicago

Coapt LLC

Levi John Hargrove

Blair Andrew Lock

Richard Woodward

Aaron Young

Todd Kuiken

Tommaso Lenzi

Jon Sensinger

Robert Lipschutz

Kevin Dwyer

Mary Mcmeekin

Katherine Cai

Gila Baer

Dennis Tkach

Steven Hansen

Adaptive biosignal systems and methods are disclosed for switching one or more designations of biosignal sensors for dynamic adaptation and optimization of one or more biosignal detection devices. A sensor designation cycle is executed for a plurality of biosignal sensors of a biosignal detection device, each of the plurality of biosignal sensors configured to collect biosignal data of a user, and each of the plurality of biosignal sensors having a designation defining an electrical sensor modality modifiable by a switch communicatively coupled to the biosignal detection device. The plurality of biosignal sensors comprises at least a first biosignal sensor, a second biosignal sensor, and a third biosignal sensor, wherein the first biosignal sensor is designated as a reference sensor, and wherein the second biosignal sensor is designated as a measurement sensor. The sensor designation cycle comprises various algorithms for switching designations of the various sensors.

System and method for switching the designation of a biosignal sensor

Biometric robustness systems and methods are described for detecting biometric signals of a user and adaptively adjusting signal output to control biometric devices(s). In various aspects, the biometrics robustness systems and methods comprise determining a biometric signal pattern of the user based on analysis of biometric signals of the user. The biometric signals of the user are detected by one or more biometric sensors. The biometrics robustness systems and methods may comprise generating, by a processor communicatively coupled to the one or more biometric sensors, an adjusted control output based on an anomaly as detected within the biometric signal pattern. The biometrics robustness systems and methods may comprise providing, by the processor, an adjusted control output to a biometric device to control operation of the biometric device.

Biometrics robustness systems and methods for detecting biometric signals of a user and adaptively adjusting signal output to control one or more biometric devices

An apparatus comprising an arm band and an electromyographic (EMG) control module is disclosed. The apparatus includes an electromygraphic (EMG) control module configured to receive EMG information generated by an individual; identify a gesture class based on the EMG information, and train using the received EMG information and the gesture class. The gesture class corresponds to an intended gesture made by the individual.

Prosthetic virtual reality training interface and related methods

Biometric enabled virtual reality (VR) systems and methods are disclosed for detecting user intention(s) and modulating virtual avatar control based on the user intention(s) for creation of virtual avatar(s) or object(s) in holographic space, two-dimensional (2D) virtual space, or three-dimensional (3D) virtual space. A virtual representation of an intended motion of a user corresponding to an intention of muscle activation of the user is determined based on analysis of a biometric signal data of the user as collected by a biometric detection device. The virtual representation of the intended motion is used to modulate virtual avatar control or output to create at least one of a virtual avatar representing aspect(s) of the user or an object manipulated by the user in a holographic space, virtual 2D space, or virtual 3D space. The avatar or the object is created based on: (1) the biometric signal data of a user, or (2) user-specific specifications as provided by the user.

Biometric enabled virtual reality systems and methods for detecting user intentions and modulating virtual avatar control based on the user intentions for creation of virtual avatars or objects in holographic space, two-dimensional (2D) virtual space, or three-dimensional (3D) virtual space

Biometric enabled virtual reality (VR) systems and methods are disclosed for detecting user intention(s) and manipulating virtual avatar control based on the user intention(s) for providing kinematic awareness in holographic space, two-dimensional (2D) space, or three-dimensional (3D) virtual space. A virtual representation of an intended motion of a user corresponding to an intention of muscle activation of the user is determined based on analysis of a biometric signal data of the user as collected by a biometric detection device. The virtual representation of the intended motion is used to modulate virtual avatar control or output of a virtual avatar (representing one or more aspects of at least one of the user) or an object (as manipulated by the user) is manipulated in the holographic space, the virtual 2D, or the 3D space. The virtual avatar is rendered by a virtual interface configured to provide the kinematic awareness to the user in the holographic space, the 2D virtual space, or the 3D virtual space.

Biometric enabled virtual reality systems and methods for detecting user intentions and manipulating virtual avatar control based on user intentions for providing kinematic awareness in holographic space, two-dimensional (2D), or three-dimensional (3D) virtual space

An apparatus comprising an electromyographic (EMG) control module is disclosed. The apparatus includes an electromyographic (EMG) control module configured to receive EMG information generated by an individual; identify a gesture class based on the EMG information, and train using the received EMG information and the gesture class. The gesture class corresponds to an intended gesture made by the individual.

Embodiments of the invention provide for a prosthesis guided training system that includes a plurality of sensors for detecting electromyographic activity. A computing device, which can include a processor and memory, can extract data from the electromyographic activity. A real-time pattern recognition control algorithm and an autoconfiguring pattern recognition training algorithm can be stored in the memory. The computing device can determine movement of a prosthesis based on the execution of the real-time pattern recognition control algorithm. The computing device can also alter operational parameters of the real-time pattern recognition control algorithm based on execution of the autoconfiguring pattern recognition training algorithm.

Systems and methods for autoconfiguration of pattern-recognition controlled myoelectric prostheses

We present a novel swing phase control module for powered transfemoral prostheses based on minimum jerk theory. The control module allows physiologically appropriate swing movement at any walking speed, regardless of the stance controller action. Preliminary validation in a transfemoral amputee subject demonstrates that the control module provides physiological swing timing, without speed or patient-specific tuning.

Minimum jerk swing control for assistive device

This disclosure relates to a hierarchy of classifiers, including linear discriminant analysis (LDA) classifiers, arranged to provide simultaneous control of multiple degrees of freedom of a prosthetic. The high accuracy of the hierarchical approach allows pattern recognition techniques to be extended to permit simultaneous control, potentially allowing amputees to produce more fluid, life-like movements, ultimately increasing their quality of life. The hierarchy may also be used to control a biological interface that allows input to computers for persons with disabilities, used as a potential video-game controller, and used as an input interface for tablets, and phones.

Systems and methods for hierarchical pattern recognition for simultaneous control of multiple-degree of freedom movements for prosthetics

Embodiments of the systems and methods described herein relate to a control unit comprising a memory having stored thereon a feature database that includes feature data, a portion of which is labeled with a transition from an ambulation mode and a memory having stored thereon a pattern recognition controller that is trained using the labeled feature data, wherein the pattern recognition controller is configured to predict the ambulation mode of an assistive device and the control unit is configured to communicate with sensors coupled to the assistive device.

Ambulation prediction controller for lower limb assistive device

Embodiments of the invention provides myoelectric prosthesis control system that include a gel liner that has a plurality of layers and a plurality of leads at least partially positioned between the plurality of layers. In addition, a plurality of electrodes can be coupled to the leads and portions of the electrodes can also be positioned between the plurality of layers. At least some of the electrodes can include an electrode pole that is configured to contract the residual limb to detect electromyographic signals.

Growing community of inventors

Chicago, IL, United States of America

Levi John Hargrove