Cionic, Inc.

Aliphcom

Openwave Systems Inc.

Jeremiah Robison

Lina Avancini Colucci

Andrew James Weitz

Michael Dean Achelis

Sidney Rafael Primas

Scott Fullam

Thomas Alan Donaldson

Michael Edward Smith Luna

Hosain Sadequr Rahman

Max Everett Utter, Ii

Richard Lee Drysdale

Travis Austin Bogard

Ren Gibbons

Joseph Edward Heitzeberg

James P La Fleur

Raymond Anthony Martino

Benoit Schillings

A mobility augmentation system monitors a user's motor intent data and augments the user's mobility based on the monitored motor intent data. A machine-learned model is trained to identify an intended movement based on the monitored motor intent data. The machine-learned model may be trained based on generalized or specific motor intent data (e.g., user-specific motor intent data). A machine-learned model initially trained on generalized motor intent data may be re-trained on user-specific motor intent data such that the machine-learned model is optimized to the movements of the user. The system uses the machine-learned model to identify a difference between the user's monitored movement and target movement signals. Based on the identified difference, the system determines actuation signals to augment the user's movement. The actuation signals determined can be an adjustment to a currently applied actuation such that the system optimizes the actuation strategy during application.

Mobility based on machine-learned movement determination

A mobility augmentation system monitors data representative of a user's motor intent and augments the user's mobility based on the monitored motor intent data. A machine-learned model is trained to identify an intended movement based on the monitored motor intent data. The machine-learned model may be trained based on generalized or specific motor intent data (e.g., user-specific motor intent data). A machine-learned model initially trained on generalized motor intent data may be re-trained on user-specific motor intent data such that the machine-learned model is optimized to the movements of the user. The system uses the machine-learned model to identify a difference between the user's monitored movement and target movement signals. Based on the identified difference, the system determines actuation signals to augment the user's movement. The actuation signals determined can be an adjustment to a currently applied actuation such that the system optimizes the actuation strategy during application.

Machine-learned movement determination based on intent identification

A mobility augmentation system assists a user's movement by determining a corresponding electrical stimulation for the movement. A wearable stimulation array includes sensors, electrodes, an electrode multiplexer, and a controller that executes the mobility augmentation system. The sensors measure movement data, and the mobility augmentation system applies a movement model to the measured movement data. The model can determine different electrical actuation instructions depending on the movement stimulated. For example, to stimulate a knee flexion, the movement model output enables a first set of the electrodes to operate as cathodes and a second set of electrodes to operate as anodes. To stimulate a knee extension, the first set of electrodes can be enabled to operate as anodes and a third set of electrodes as cathodes. The user can provide feedback of the applied stimulation, which the system can use to retrain the model and optimize the stimulation to the user.

Adaptive stimulation array for motor control

Adaptive stimulation array calibration

Techniques for media device, application, and content management using sensory input are described, including receiving input from one or more sensors coupled to a data-capable strapband, processing the input to determine a pattern, referencing a pattern library using the pattern, generating a control signal to a media application, the control signal being determined based on whether the pattern matches another pattern in the pattern library, and selecting a media file configured to be presented, the media file being selected using the control signal.

Media device, application, and content management using sensory input

Embodiments of the invention relates generally to electrical and electronic hardware, computer software, wired and wireless network communications, and computing devices, and more specifically to structures and techniques for managing power generation, power consumption, and other power-related functions in a data-capable strapband. Embodiments relate to a band including sensors, a controller coupled to the sensors, an energy storage device, a connector configured to receive power and control signals, and a power manager. The power manager includes at least a transitory power manager configured to manage power consumption of the band during a first power mode and a second mode. The band can be configured as a wearable communications device and sensor platform.

Power management in a data-capable strapband

Methods for accessing published content from a mobile device are described herein. In one aspect of the invention, an example of a process includes receiving a first message received from a mobile device over a network for accessing a destination, where the first message includes an identification associated with a destination and a predetermined indicator. In response to the first message, returning a second message to the mobile device, the second message including a link linking with a network-based resource associated with the destination. Other methods and apparatuses are also described.

Methods for accessing published contents from a mobile device

Growing community of inventors

San Francisco, CA, United States of America

Jeremiah Robison