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 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: The invention features methods for detecting the hydration state or vascular volume of a subject using a device capable of nuclear magnetic resonance (NMR) measurement. The methods involve exposing a portion of a tissue of the subject in vivo to a magnetic field and RF pulse from the device to excite hydrogen nuclei of water within the tissue portion, and measuring a relaxation parameter of the hydrogen nuclei in the tissue portion, the relaxation parameter being a quantitative measure of the hydration state or vascular volume of the subject as a whole. The invention also features devices and computer-readable storage media for performing the methods of the invention.

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: The invention features methods for detecting the hydration state or vascular volume of a subject using a device capable of nuclear magnetic resonance (NMR) measurement. The methods involve exposing a portion of a tissue of the subject in vivo to a magnetic field and RF pulse from the device to excite hydrogen nuclei of water within the tissue portion, and measuring a relaxation parameter of the hydrogen nuclei in the tissue portion, the relaxation parameter being a quantitative measure of the hydration state or vascular volume of the subject as a whole. The invention also features devices and computer-readable storage media for performing the methods of the invention.

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: The invention features methods for detecting the hydration state or vascular volume of a subject using a device capable of nuclear magnetic resonance (NMR) measurement. The methods involve exposing a portion of a tissue of the subject in vivo to a magnetic field and RF pulse from the device to excite hydrogen nuclei of water within the tissue portion, and measuring a relaxation parameter of the hydrogen nuclei in the tissue portion, the relaxation parameter being a quantitative measure of the hydration state or vascular volume of the subject as a whole. The invention also features devices and computer-readable storage media for per forming the methods of the invention.