ELM-GA-Based Active Comfort Control of a Piggyback Transfer Robot

The improvement of comfort in the human–robot interaction for care recipients is a significant challenge in the development of nursing robots. The existing methods for enhancing comfort largely depend on subjective comfort questionnaires, which are prone to unavoidable errors. Additionally, traditio...

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Bibliographic Details
Published in:Machines (Basel) Vol. 13; no. 8; p. 748
Main Authors: Feng, Liyan, Wang, Xinping, Liu, Teng, Qi, Kaicheng, Zhang, Long, Zhang, Jianjun, Guo, Shijie
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.08.2025
Subjects:
active comfort control
Active control
Aircraft
Algorithms
Comfort
Contact angle
Contact force
Control methods
Electromyography
extreme learning machine
genetic algorithm
Genetic algorithms
Kinematics
latissimus dorsi activation
Liu, Timothy
Machine learning
Motorcycles
Muscles
Neural networks
Nursing
Physiology
piggyback transfer robot
Prediction models
Robotics industry
Robots
Rotation
Service robots
Trajectory optimization
China
ISSN:2075-1702, 2075-1702
Online Access:Get full text
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Summary:The improvement of comfort in the human–robot interaction for care recipients is a significant challenge in the development of nursing robots. The existing methods for enhancing comfort largely depend on subjective comfort questionnaires, which are prone to unavoidable errors. Additionally, traditional passive movement control approaches lack the ability to adapt and effectively improve care recipient comfort. To address these problems, this paper proposes an active, personalized intelligent control method based on neural networks. A muscle activation prediction model is established for the piggyback transfer robot, enabling dynamic adjustments during the care process to improve human comfort. Initially, a kinematic analysis of the piggyback transfer robot is conducted to determine the optimal back-carrying trajectory. Experiments were carried out to measure human–robot contact forces, chest holder rotation angles, and muscle activation levels. Subsequently, an Online Sequential Extreme Learning Machine (OS-ELM) algorithm is used to train a predictive model. The model takes the contact forces and chest holder rotation angle as inputs, while outputting the latissimus dorsi muscle activation levels. The Genetic Algorithm (GA) is then employed to dynamically adjust the chest holder’s rotation angle to minimize the difference between actual muscle activation and the comfort threshold. Comparative experiments demonstrate that the proposed ELM-GA-based active control method effectively enhances comfort during the piggyback transfer process, as evidenced by both subjective feedback and objective measurements of muscle activation.
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ISSN:2075-1702
2075-1702
DOI:10.3390/machines13080748