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 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 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.