{
  "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.",
  "assignees": [
    "CIONIC INC"
  ],
  "authors": [
    "ACHELIS MICHAEL DEAN",
    "COLUCCI LINA AVANCINI",
    "PRIMAS SIDNEY RAFAEL",
    "ROBISON JEREMIAH",
    "WEITZ ANDREW JAMES"
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  "title": "Mobility based on machine-learned movement determination",
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