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This is a record of a document published elsewhere, first published by the United States Patent and Trademark Office, as US-2026097485-A1, on 9 April 2026. OpenTechnical did not first disclose it. The identifier, hashes and timestamps below record what this library served and when, not when the material became public. View the original.

OT-2026-000000919

Mobility based on machine-learned movement determination

Published
Permanent URL
Classification
Robotics & Automation › Robot Architectures
Inventors
ACHELIS MICHAEL DEAN, COLUCCI LINA AVANCINI, PRIMAS SIDNEY RAFAEL, ROBISON JEREMIAH, WEITZ ANDREW JAMES
Assignee
CIONIC INC
Original source
US-2026097485-A1, the United States Patent and Trademark Office, 9 April 2026
Document SHA-256
60af9f06f5f18d74fcd8fcde8a69626f909dba4019e26bfa4bcda9f8ec23f102

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.

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Citation

OpenTechnical Publication OT-2026-000000919, “Mobility based on machine-learned movement determination,” published September 15, 2026 at 19:03:35 UTC, https://opentechnical.org/p/OT-2026-000000919.

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