Mobility based on machine-learned movement determination

US12005573B2 · kind B2 · utility

Assignee

• Cionic, Inc. · US

Inventors

• Jeremiah Robison · San Francisco, US
• Michael Dean Achelis · Walnut Creek, US
• Lina Avancini Colucci · Los Altos, US
• Sidney Rafael Primas · Los Altos, US
• Andrew James Weitz · Bishop, US

Key dates

• Filing date: Dec 6, 2020
• Grant date: Jun 11, 2024
• Expiry date: Mar 15, 2043

Classification

• Technology area (CPC A): Human Necessities
• CPC primary: A61H2230/605
• WIPO field: Medical technology
• WIPO sector: Instruments

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.