Human body tumble protection method, device, equipment and medium

By collecting multimodal motion data in real time and using deep learning models to identify fall types, generating variable impedance control commands, and adjusting joint motor parameters, the problem of insufficient differentiated buffering and prediction in the fall prevention strategy of exoskeleton robots is solved, achieving precise protection against different fall types and improving safety and practicality.

CN122045991APending Publication Date: 2026-05-15SHENZHEN YIFANG INNOVATION TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202511994844.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-25
Publication Date
2026-05-15

AI Technical Summary

Technical Problem

Existing exoskeleton robots cannot achieve differentiated buffering in fall prevention strategies, resulting in limited protection effectiveness. Furthermore, they cannot predict and accurately identify fall types in advance, leading to delayed protection activation.

Method used

By collecting users' multimodal motion data in real time, a deep learning model combining convolutional neural networks and long short-term memory networks is used to identify fall types, generate variable impedance control commands, and adjust the stiffness and damping parameters of the joint motors to achieve flexible buffering.

Benefits of technology

It achieves accurate identification and prediction of different types of falls, and can trigger differentiated buffering before a fall, significantly reducing the risk of injury to users and improving the safety and practicality of exoskeleton robots.

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Abstract

The invention relates to the technical field of robots, and discloses a human body tumble protection method and device, an exoskeleton robot and a medium, and the method comprises the steps: obtaining multi-modal motion data, collected by a sensor in real time, of a user; inputting the multi-modal motion data into a tumble identification model for identification and classification to obtain a tumble type probability of the user; if the tumble probability is greater than a preset safety threshold, determining that the user is in a tumble trend, and generating a variable impedance control instruction matched with the target tumble type according to a preset strategy library; and determining a rigidity parameter and a damping parameter of a target joint motor according to the variable impedance control instruction, and determining a target output torque based on the joint motion data, so that the target joint motor performs fall protection on the user based on the target output torque. The fall type can be accurately recognized, flexible buffering can be achieved, and then the injury risk of a user is remarkably reduced.
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