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Updated: Jul 30, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Robots for Elderly Care: Review, Multi-Criteria Optimization Model and Qualitative Case Study.

Bartosz Sawik^1,2,3, Sławomir Tobis⁴, Ewa Baum⁵

¹Department of Business Informatics and Engineering Management, AGH University of Science and Technology, 30-059 Krakow, Poland.

Healthcare (Basel, Switzerland)

|May 13, 2023

Summary

This study introduces an optimization model for assigning social and service robots in elderly care, considering robot use and caregiver stress. User involvement is crucial for successful robot adoption and integration.

Keywords:

artificial intelligence caregiver elderly care healthcare human-robot interaction mathematical integer programming multi-criteria optimization robotic assistant service robot social robot

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Area of Science:

Robotics
Gerontology
Human-Computer Interaction

Background:

Social and service robots offer potential for elderly care, but their efficient assignment and user acceptance require investigation.
The ENRICHME project explores long-term robot interaction for older adults with mild cognitive impairment to enhance independence.

Purpose of the Study:

To review current knowledge on robots in elderly care.
To propose a novel multi-criteria optimization model for robot assignment, incorporating robot utilization and caregiver stress.
To identify elderly and caregiver needs, requirements, and barriers for technology adoption.

Main Methods:

Literature review on social and service robots for elderly care.
Development of a conceptual multi-criteria optimization model for robot assignment.
Conducting four focus group discussions (FGDs) with older adults and caregivers (n=24) using a detailed script.

Main Results:

The optimization model uniquely considers robot utilization and caregiver stress levels.
FGDs revealed consistent user characteristics, robot functionalities, and adoption barriers.
Key findings emphasize the need for thorough planning, pre-training, ethical considerations, and user involvement in robot customization.

Conclusions:

User-centered design and involvement are essential for successful robot deployment in elderly care.
The proposed optimization model provides a framework for efficient robot assignment.
Addressing ethical, practical, and user-specific needs is critical for technology adoption among older adults and caregivers.