Coapt LLC

Rehabilitation Institute of Chicago

Harlock Creative LLC

Blair Andrew Lock

Levi John Hargrove

Todd Kuiken

Robert Lipschutz

Gila Baer

Kevin Dwyer

Steven Hansen

Mary Mcmeekin

Dennis Tkach

Katherine Cai

Katherine Amy Cai

Xavier Nigel Oberhelman

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

In various aspects, systems and methods are described for a biometric identification and control for providing customizable security through authentication of biosignal representations and control. The biometric identification and control systems and methods comprise hardware and software components that are used to detect, via a biometric detection device, biometric signals of a user. These biosignals are analyzed by a processor communicatively coupled to the biometric detection device. After analyzation of the biosignals, a biometric profile is created and used with a security interface to access a secure resource or device or to output a command to another device.

Biometric identification and control systems and methods for providing customizable security through authentication of biosignal representations of one or more user-specific and user-selected gesture-intentions

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

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

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

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.

Systems and methods of myoelectric prosthesis control

A prosthetic assembly for a residual limb that includes a sleeve for attachment to a distal portion of the residual limb, where the attachment member has a diametrically oriented magnetic field, and a prosthesis with a socket for receiving the sleeve, where the prosthesis socket has a distal end with an attachment member having a corresponding diametrically oriented magnetic field. The diametrically oriented magnetic fields of the sleeve and prosthetic attachment members may be oriented on the prosthesis and the residual limb to achieve a predetermined rotational orientation between the prosthesis and the residual limb and will facilitate removal of the prosthesis with a twisting action.

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Chicago, IL, United States of America

Blair Andrew Lock