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Intelligent Rehabilitation and Assistive Robotics

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 6709

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Special Issue Editor

Dr. João Quintas

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Guest Editor

Laboratory for Automation and Systems, Instituto Pedro Nunes, 3030-199 Coimbra, Portugal
Interests: human-robot interaction; cloud robotics; human behaviour analysis; context awareness; active and assisted living

Special Issue Information

Dear Colleagues,

The European population of adults aged 65 and above is expected to reach nearly 30% by 2060. The increasing demand for healthcare and quality-of-life services to support the aging population has inspired researchers worldwide to explore the applicability of new technologies to support older adults to cope with the challenges of aging and live independently for longer periods of time.

Nowadays AI, IoT, cloud and edge computing, and robotics are highly interrelated and cannot be dissociated from each other. Furthermore, when integrated into MedTech, digital therapeutics, or assistive technologies (for example, in the field of active and assisted living (AAL)), several challenges must be addressed, including technological, standardization, and clinical challenges.

This Special Issue will welcome contributions addressing systematic reviews, clinical needs and requirements, technology and its standardisation, related to the main theme of “Intelligent Rehabilitation and Assistive Robotics”. These include, but are not limited to:

Artificial intelligent agents;
Human–machine interaction;
Robotics and autonomous systems;
Clinical needs and use scenarios;
Verification and validation studies;
Standardization;
Knowledge representation.

Dr. João Quintas
Guest Editor

Manuscript Submission Information

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Keywords

digital therapeutics
telerehabilitation
telemedicine
active and assistive living
robotics
artificial intelligence
standardization
human-machine interaction

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Published Papers (5 papers)

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Research

14 pages, 1654 KiB

Open AccessArticle

Validity of Deep Learning-Based Motion Capture Using DeepLabCut to Assess Proprioception in Children

by Maud van den Bogaart, Nina Jacobs, Ann Hallemans and Pieter Meyns

Appl. Sci. 2025, 15(7), 3428; https://doi.org/10.3390/app15073428 - 21 Mar 2025

Cited by 2 | Viewed by 230

Abstract

Proprioceptive deficits can lead to impaired motor performance. Therefore, accurately measuring proprioceptive function in order to identify deficits as soon as possible is important. Techniques based on deep learning to track body landmarks in simple video recordings are promising to assess proprioception (joint position sense) during joint position reproduction (JPR) tests in clinical settings, outside the laboratory and without the need to attach markers. Fifteen typically developing children participated in 90 knee JPR trials and 21 typically developing children participated in 126 hip JPR trials. Concurrent validity of two-dimensional deep-learning-based motion capture (DeepLabCut) to measure the Joint Reproduction Error (JRE) with respect to laboratory-based optoelectronic three-dimensional motion capture (Vicon motion capture system, gold standard) was assessed. There was no significant difference in the hip and knee JRE measured with DeepLabCut and Vicon. Two-dimensional deep-learning-based motion capture (DeepLabCut) is valid to assess proprioception with respect to the gold standard in typically developing children. Tools based on deep learning, such as DeepLabCut, make it possible to accurately measure joint angles in order to assess proprioception without the need of a laboratory and to attach markers, with a high level of automatization. Full article

(This article belongs to the Special Issue Intelligent Rehabilitation and Assistive Robotics)

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18 pages, 3853 KiB

Open AccessArticle

Using Machine Learning to Shorten and Adapt Fall Risk Assessments for Older Adults

by Lilyana Khatib, Adi Toledano-Shubi, Hilla Sarig Bahat and Hagit Hel-Or

Appl. Sci. 2025, 15(4), 1690; https://doi.org/10.3390/app15041690 - 7 Feb 2025

Viewed by 733

Abstract

Falls are a leading cause of injury and mortality among older adults, placing significant physical, emotional, and financial burdens on individuals, families, and healthcare systems. The early identification of fall risk and frequent reassessments during rehabilitation are essential for prevention and recovery. However, conventional assessments are time-intensive, rely on multiple motor tasks, and are typically conducted in specialized facilities, limiting their accessibility. This study introduces a novel machine learning-based computerized adaptive testing algorithm that personalizes testing to individual capabilities. The adaptive approach reduces task sequences by over 50% while maintaining high predictive accuracy. It also enables remote testing, predicting performance on complex tasks using as few as 2–3 simpler, accessible tasks. This innovation supports scalable online fall risk screening and frequent balance assessments during rehabilitation, offering a practical and efficient solution for both personalized and community-wide healthcare needs. Full article

(This article belongs to the Special Issue Intelligent Rehabilitation and Assistive Robotics)

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27 pages, 2574 KiB

Open AccessArticle

Implementation of COGNIVITRA, an Information- and Communications-Technology-Based Solution for Dual-Task Training, in Patients at Risk of Cognitive Impairment

by Judit Lopez Luque, Iñigo Chivite, Marina Serena, Clara Szymanski, David Benhsain, Ana Isabel Martins, Nelson Pacheco Rocha, Joana Pais, Vítor Tedim Cruz, João Quintas and Antoni Callen

Appl. Sci. 2024, 14(17), 7906; https://doi.org/10.3390/app14177906 - 5 Sep 2024

Viewed by 1152

Abstract

Mild cognitive impairment (MCI) is characterized by a modest decline in cognitive function that, while noticeable, does not severely impact daily life, allowing individuals to maintain their independence—a key factor distinguishing it from dementia. Currently, there are no treatments available that can modify the course of the disease, although cognitive and physical activities have shown potential in slowing its progression. In response to the need for more accessible cognitive care, COGNIVITRA, an information- and communications-technology-based solution, was developed to extend cognitive training into the home environment. This platform not only facilitates communication between patients and care providers but also holds promise for enhancing cognitive care accessibility and potentially influencing the economic aspects of healthcare institutions. To evaluate the usability, impact, and effectiveness of COGNIVITRA, a 12-week (6 mandatory + 6 voluntary) multicenter study was conducted, with an expected total sample size of 20 professionals, 90 patients and 20 caregivers and involving two settings (clinical and home settings) and the collection of various data types at baseline and after 6 or 12 weeks of training, including sociodemographic information, cognitive assessments, and usability metrics. These metrics included the System Usability Scale (SUS), the International Classification of Functioning-Based Usability Scales (ICF-US I and II), the Unified Theory of Acceptance and Use of Technology (UTAUT), health-related quality of life measures such as the EQ-5D-5L, cognitive domain assessments via the Montreal Cognitive Assessment (MoCA), and physical assessments such as the Timed 25-Foot Walk (T25-FW) test. The study included 22 patients, 2 caregivers, and 24 professionals. The usability evaluation revealed that patients, particularly those participating in the home study, showed improved SUS scores, suggesting an enhanced user experience with the platform. The ICF-US I results further supported this finding by indicating that COGNIVITRA was particularly effective as a supportive tool in terms of satisfaction and ease of learning. Despite a higher incidence of errors during the home study, the observational grid questionnaire demonstrated high success rates for task completion. Professionals involved in the study also reported high SUS scores and provided positive feedback regarding device usability. Overall, the participants expressed increased satisfaction with the platform, as reflected in their responses. The UTAUT analysis confirmed a generally positive attitude toward the use of COGNIVITRA. However, when assessing effectiveness, the analysis revealed a noninferiority positive trend in the EQ-5D-5L, T25-FW, and MoCA scores, indicating that while there were positive changes, they were not statistically significant. Full article

(This article belongs to the Special Issue Intelligent Rehabilitation and Assistive Robotics)

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18 pages, 7392 KiB

Open AccessArticle

Assistance in Picking Up and Delivering Objects for Individuals with Reduced Mobility Using the TIAGo Robot

by Francisco J. Naranjo-Campos, Ainhoa De Matías-Martínez, Juan G. Victores, José Antonio Gutiérrez Dueñas, Almudena Alcaide and Carlos Balaguer

Appl. Sci. 2024, 14(17), 7536; https://doi.org/10.3390/app14177536 - 26 Aug 2024

Viewed by 1500

Abstract

Individuals with reduced mobility, including the growing elderly demographic and those with spinal cord injuries, often face significant challenges in daily activities, leading to a dependence on assistance. To enhance their independence, we propose a robotic system that facilitates greater autonomy. Our approach involves a functional assistive robotic implementation for picking, placing, and delivering containers using the TIAGo mobile manipulator robot. We developed software and routines for detecting containers marked with an ArUco code and manipulating them using the MoveIt library. Subsequently, the robot navigates to specific points of interest within a room to deliver the container to the user or another designated location. This assistance task is commanded through a user interface based on a web application that can be accessed from the personal phones of patients. The functionality of the system was validated through testing. Additionally, a series of user trials were conducted, yielding positive feedback on the performance and the demonstration. Insights gained from user feedback will be incorporated into future improvements to the system. Full article

(This article belongs to the Special Issue Intelligent Rehabilitation and Assistive Robotics)

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22 pages, 8790 KiB

Open AccessArticle

Lessons Learned from Investigating Robotics-Based, Human-like Testing of an Upper-Body Exoskeleton

by Marc Kilian Klankers, Adrian Rudloff, Pouya Mohammadi, Niclas Hoffmann, Seyed Milad Mir Latifi, Ramazan Gökay, Rajal Nagwekar, Robert Weidner and Jochen J. Steil

Appl. Sci. 2024, 14(6), 2481; https://doi.org/10.3390/app14062481 - 15 Mar 2024

Cited by 1 | Viewed by 1877

Abstract

Assistive devices like exoskeletons undergo extensive testing not least because of their close interaction with humans. Conducting user studies is a time-consuming process that demands expert knowledge, and it is accompanied by challenges such as low repeatability and a potential lack of comparability between studies. Obtaining objective feedback on the exoskeleton’s performance is crucial for developers and manufacturers to iteratively improve the design and development process. This paper contributes to the concept of using robots for objective exoskeleton testing by presenting various approaches to a robotic-based testing platform for upper-body exoskeletons. We outline the necessary requirements for realistically simulating use cases and evaluate different approaches using standard manipulators as robotic motion generators. Three approaches are investigated: (i) Exploiting the anthropomorphic structure of the robotic arm and directly placing it into the exoskeleton. (ii) Utilizing a customized, direct attachment between the robot and exoskeleton. (iii) Attaching a human arm dummy to the robot end effector to simulate a more realistic interface with the exoskeleton. Subsequently, we discuss and compare the results against the aforementioned requirements of a systematic testing platform. Our conclusion emphasizes that achieving objective and realistic testing necessitates highly specialized hardware, algorithms, and further research to address challenging requirements. Full article

(This article belongs to the Special Issue Intelligent Rehabilitation and Assistive Robotics)

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