Robotic systems for upper-limb rehabilitation in multiple sclerosis: a SWOT analysis and the synergies with virtual and augmented environments

Front Robot AI. 2024 Feb 27:11:1335147. doi: 10.3389/frobt.2024.1335147. eCollection 2024.

Abstract

The robotics discipline is exploring precise and versatile solutions for upper-limb rehabilitation in Multiple Sclerosis (MS). People with MS can greatly benefit from robotic systems to help combat the complexities of this disease, which can impair the ability to perform activities of daily living (ADLs). In order to present the potential and the limitations of smart mechatronic devices in the mentioned clinical domain, this review is structured to propose a concise SWOT (Strengths, Weaknesses, Opportunities, and Threats) Analysis of robotic rehabilitation in MS. Through the SWOT Analysis, a method mostly adopted in business management, this paper addresses both internal and external factors that can promote or hinder the adoption of upper-limb rehabilitation robots in MS. Subsequently, it discusses how the synergy with another category of interaction technologies - the systems underlying virtual and augmented environments - may empower Strengths, overcome Weaknesses, expand Opportunities, and handle Threats in rehabilitation robotics for MS. The impactful adaptability of these digital settings (extensively used in rehabilitation for MS, even to approach ADL-like tasks in safe simulated contexts) is the main reason for presenting this approach to face the critical issues of the aforementioned SWOT Analysis. This methodological proposal aims at paving the way for devising further synergistic strategies based on the integration of medical robotic devices with other promising technologies to help upper-limb functional recovery in MS.

Keywords: SWOT; augmented reality; digital health; multiple sclerosis; rehabilitation; robotics; virtual reality.

Publication types

  • Review

Grants and funding

The authors declare that financial support was received for the research, authorship, and publication of this article, part of ENACT, a FISM (Fondazione Italiana Sclerosi Multipla) Special Project. ENACT is supported by FISM cod. 2021/Special/003 and financed with the “5 per mille” public funding. IIT (Istituto Italiano di Tecnologia) co-funded the study. This work was also carried out within the framework of the project “RAISE - Robotics and AI for Socioeconomic Empowerment”. RAISE has been supported and funded by European Union - NextGenerationEU. However, the views and opinions expressed are those of the authors alone and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.