Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A symptom intervention system monitors data representative of a user's movement, identifies an onset of a symptom of a physical condition, and applies an actuation to intervene with the identified onset. A machine-learned model is trained to identify an onset of a symptom based on the monitored data . The system may use the machine-learned model to determine whether to modify an upcoming administration of a chemical stimulus that is administered to the user to treat their physical condition. The system may determine a modification to a dose or a time associated with the upcoming administration of the stimulus and apply the stimulus to the user based on the determined modification. The system may use the machine-learned model to determine that the user is exhibiting a particular symptom of their physical condition. Depending on the symptom, the system may depolarize or hyperpolarize neurons of the user.

Abstract: A symptom intervention system monitors data representative of a user's movement, identifies an onset of a symptom of a physical condition, and applies an actuation to intervene with the identified onset. A machine-learned model is trained to identify an onset of a symptom based on the monitored data . The system may use the machine-learned model to determine whether to modify an upcoming administration of a chemical stimulus that is administered to the user to treat their physical condition. The system may determine a modification to a dose or a time associated with the upcoming administration of the stimulus and apply the stimulus to the user based on the determined modification. The system may use the machine-learned model to determine that the user is exhibiting a particular symptom of their physical condition. Depending on the symptom, the system may depolarize or hyperpolarize neurons of the user.

Abstract: Embodiments relate generally to electrical and electronic hardware, computer software, wired and wireless network communications, and computing devices. More specifically, an insight micro-engine is configured to present insights relating a detected activity. According to an embodiment, a method includes detecting at a wearable computing device an absence of a communication link and activating an insight micro-engine configured to operate responsive to the absence of the communication link. Further, the method can include receiving a subset of sensor data from a first subset of sensors implemented in the wearable computing device, determining an activity based on a subset of triggers, determining an aggregate value representative of a state of the activity, correlating the aggregate value to a target value, and deriving data representing an insight that associates the aggregate value with a parameter.

Abstract: Techniques associated with a combination speaker and light source responsive to states of an environment based on sensor data are described, including a housing, a light source disposed within the housing and configured to be powered using a light socket connector coupled to the housing, a speaker coupled to the housing and configured to output audio, and a sensor device comprising a light and speaker controller, the sensor device configured to determine an environmental state and to generate environmental state data associated with the environmental state, the light and speaker controller configured to send a control signal to one or both of the light source and the speaker.

Abstract: 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.

Abstract: Techniques for data-capable band management in association with, for example, an autonomous advisory application and network communication data environment are described. In some examples, a method can detect a signal configured to initiate a synchronization function being transmitted from a wearable computing device to an application, as an example. In some cases, a method can include performing a synchronization function to, for example, transfer data from the wearable computing device to the application. Further, a method can include causing presentation to an interface on a computing device to display information associated with a wearable computing device, as determined by the data transferred to the application, for example, after a synchronization function is performed and/or a type of motion is synchronized.

Abstract: Techniques for data-capable band management in an integrated application and network communication data environment are described, including receiving input from one or more sensors coupled to a wearable computing device, receiving a parameter used to determine when a synchronization between the wearable computing device and a device in data communication with the wearable computing device is to be performed, processing the input to determine if the parameter is satisfied, performing the synchronization between the wearable computing device and the device if the parameter is satisfied, and presenting an interface on the device, the interface being configured to display information associated with the synchronization.

Abstract: 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.

Abstract: Techniques for media device, application, and content management using sensory input determined from a data-capable watch band 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.

Abstract: Techniques associated with a combination speaker and light source (“speaker-light device”) responsive to states of an organism based on sensor data are described, including generating chemical sensor data in response to one or more chemicals sensed by one or more chemical sensors in the same or different speaker-light devices. A scent generator may be activated to counter an odor caused by a chemical detected by the chemical sensor(s). The speaker-light device may activate an air mover operative to circulate ambient air over the chemical sensor. The speaker-light device may take an appropriate action in response to detected chemicals that affect states of the organism. Some or all of the actions taken may be taken by other devices in communication with the speaker-light device(s). An action may include generating and presenting a path or route to be taken by a user to an area of safety or reduced chemical concentration.

Abstract: Techniques associated with a combination speaker and light source responsive to states of an organism based on sensor data are described, including receiving state data from a wearable device configured to generate the state data using sensor data, generating light data using the state data, generating audio data using the state data, providing the light data and the audio data to a controller configured to generate one or more control signals using the light data and the audio data, the one or more control signals configured to adjust a light source and a speaker, the light source and the speaker implemented in a combination speaker and light source device.

Abstract: Techniques for determining a state of a user using wearable devices are described, including receiving motion-related data from a first sensor and user-related data from a second sensor, determining a state of a user as a function of the motion-related data and the user-related data, and displaying a graphic associated with the state of the user at a graphical user interface. The first sensor and the second sensor may be coupled to a wearable device. The user-related data may include galvanic skin response data. The graphic associated with the state of the user may be displayed at a web browser.

Abstract: Techniques for determining a state of a user using wearable devices are described, including receiving motion-related data from a first sensor and user-related data from a second sensor over a time period, determining a plurality of states of a user over the time period as a function of the motion-related data and the user-related data, determining a trend as a function of the plurality of states of the user, and displaying information associated with the trend at a user interface. The first sensor and the second sensor may be coupled to a wearable device. The user-related data may include galvanic skin response data. The information associated with the trend may be displayed at a web browser.

Abstract: Techniques associated with a combination speaker and light source (“speaker-light device”) responsive to states of an organism based on sensor data are described, including generating motion sensor data in response to a movement captured using a motion sensor, deriving movement data using a motion analysis module configured to determine the movement to be associated with one or more of a gesture, an identity, and an activity, using the motion sensor data, generating acoustic sensor data in response to sound captured using an acoustic sensor, deriving audio data using a noise removal module configured to subtract a noise signal from the acoustic sensor data, detecting a radio frequency signal using a communication facility, the radio frequency being associated with a personal device, obtaining state data from the personal device, and determining a desired light characteristic using the state data and one or both of the movement data and the audio data.