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Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation

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A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Medical Instruments".

Deadline for manuscript submissions: closed (30 June 2025) | Viewed by 11064

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Special Issue Editors

Dr. Duk Shin

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Guest Editor

Department of Mechatronics, Tokyo Polytechnic University, Atsugi 243-0297, Japan
Interests: BMI/BCI; rehabilitation robot

Dr. JaeHyo Kim

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Guest Editor

Department of Mechanical and Control Engineering, Handong Global University, Pohang 37554, Republic of Korea
Interests: neuro robotics; rehabilitation robot; human motor control

Dr. Abdelkader Nasreddine Belkacem

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Guest Editor

Department of Computer and Network Engineering, United Arab Emirates University, Abu Dhabi, United Arab Emirates
Interests: brain computer interface; human-robot interaction; applied AI

Special Issue Information

Dear Colleagues,

Over time, the motor skills of older adults and people with neuromuscular disorders gradually decline, affecting both movement speed and accuracy. Intelligent healthcare and biomedical systems have had a major impact on this field over the past decade and are expected to revolutionize rehabilitation and the treatment of movement disorders caused by aging, stroke, and neuromuscular diseases. How to assess and support motor improvement in this field is crucial.

This requires more quantitative methods based on the collection and processing of biological signals as well as control actuators to assist and resist for rehabilitation and healthcare systems.

Relevant are advances in neural signal acquisition, machine learning processes of neural signals, and computer as well as robotic technologies for assisting humans. These areas have the potential to support rehabilitation and healthcare strategies by providing standards for biomedical engineering.

We invite researchers to submit original research papers and review articles that address novel methods for rehabilitation that promote advances to help patients and older adults with motor impairments, including brain–machine interfaces, prosthetics, rehabilitation robots, and control actuators. These new methods promote the advancement of intelligent healthcare and biomedical systems.

Potential topics include, but are not limited to, the following:

Actuator control methods for interactions between human and devices.
Novel rehabilitation/healthcare systems.
Assistive technologies for patients with motor control impairments.
Personalized rehabilitation interfaces for adapted physical activity.
New techniques using deep learning and machine learning.
Internet of Medical Things (IoMT).
Biomimetic robots and home support robots.

Dr. Duk Shin
Dr. JaeHyo Kim
Dr. Abdelkader Nasreddine Belkacem
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Actuators is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

intelligent healthcare and biomedical systems
rehabilitation
actuator control
biomimetic robots

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Published Papers (9 papers)

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Research

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14 pages, 1405 KiB

Open AccessArticle

Hybrid EEG-EMG Control Scheme for Multiple Degrees of Freedom Upper-Limb Prostheses

by Sorelis Isabel Bandes Rodriguez and Yasuharu Koike

Actuators 2025, 14(8), 397; https://doi.org/10.3390/act14080397 - 11 Aug 2025

Viewed by 124

Abstract

Upper-limb motor disabilities and amputation pose a significant burden on individuals, hindering their ability to perform daily activities independently. While various research studies aim to enhance the performance of current upper-limb prosthetic devices, electrically activated prostheses still face challenges in achieving optimal functionality. This paper explores the potential of utilizing electromyogram (EMG) and electroencephalogram (EEG) signals to not only decipher movement across multiple degrees of freedom (DOFs) but also offer a more intuitive means of control. In this study, six distinct control schemes for upper-limb prosthetic devices are proposed, each with different combinations of EEG and EMG signals. These schemes were designed to control multiple degrees-of-freedom movements, encompassing five different hand and forearm actions (hand-open, hand-close, wrist pronation, wrist supination, and rest-state). Using Linear Discriminant Analysis as a model results in classification accuracies of over 85% for combined EEG-EMG control schemes. The results suggest promising advancements in the field and show the potential for a more effective and user-friendly control interface for upper-limb prosthetic devices. Full article

(This article belongs to the Special Issue Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation)

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19 pages, 1517 KiB

Open AccessArticle

Continuous Estimation of sEMG-Based Upper-Limb Joint Angles in the Time–Frequency Domain Using a Scale Temporal–Channel Cross-Encoder

by Xu Han, Haodong Chen, Xinyu Cheng and Ping Zhao

Actuators 2025, 14(8), 378; https://doi.org/10.3390/act14080378 - 31 Jul 2025

Viewed by 201

Abstract

Surface electromyographic (sEMG) signal-driven joint-angle estimation plays a critical role in intelligent rehabilitation systems, as its accuracy directly affects both control performance and rehabilitation efficacy. This study proposes a continuous elbow joint angle estimation method based on time–frequency domain analysis. Raw sEMG signals were processed using the Short-Time Fourier Transform (STFT) to extract time–frequency features. A Scale Temporal–Channel Cross-Encoder (STCCE) network was developed, integrating temporal and channel attention mechanisms to enhance feature representation and establish the mapping from sEMG signals to elbow joint angles. The model was trained and evaluated on a dataset comprising approximately 103,000 samples collected from seven subjects. In the single-subject test set, the proposed STCCE model achieved an average Mean Absolute Error (MAE) of

2.96 \pm {0.24}^{\circ}

, Root Mean Square Error (RMSE) of

4.41 \pm {0.45}^{\circ}

, Coefficient of Determination (

R^{2}

) of

0.9924 \pm 0.0020

, and Correlation Coefficient (CC) of

0.9963 \pm 0.0010

. It achieved a MAE of

{3.30}^{\circ}

, RMSE of

{4.75}^{\circ}

R^{2}

of 0.9915, and CC of 0.9962 on the multi-subject test set, and an average MAE of

15.53 \pm {1.80}^{\circ}

, RMSE of

21.72 \pm {2.85}^{\circ}

R^{2}

0.8141 \pm 0.0540

, and CC of

0.9100 \pm 0.0306

on the inter-subject test set. These results demonstrated that the STCCE model enabled accurate joint-angle estimation in the time–frequency domain, contributing to a better motion intent perception for upper-limb rehabilitation. Full article

(This article belongs to the Special Issue Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation)

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18 pages, 3167 KiB

Open AccessArticle

by Piotr Falkowski, Maciej Pikuliński, Tomasz Osiak, Kajetan Jeznach, Krzysztof Zawalski, Piotr Kołodziejski, Andrzej Zakręcki, Jan Oleksiuk, Daniel Śliż and Natalia Osiak

Actuators 2025, 14(7), 324; https://doi.org/10.3390/act14070324 - 30 Jun 2025

Viewed by 272

Abstract

Rehabilitation robotic systems have been developed to perform therapy with minimal supervision from a specialist. Hence, they require algorithms to assess and support patients’ motions. Artificial intelligence brings an opportunity to implement new exercises based on previously modelled ones. This study focuses on analysing the similarities in upper extremity movements during activities of daily living (ADLs). This research aimed to model ADLs by registering and segmenting real-life movements and dividing them into sub-tasks based on joint motions. The investigation used IMU sensors placed on the body to capture upper extremity motion. Angular measurements were converted into joint variables using Matlab computations. Then, these were divided into segments assigned to the sub-functionalities of the tasks. Further analysis involved calculating mathematical measures to evaluate the similarity between the different movements. This approach allows the system to distinguish between similar motions, which is critical for assessing rehabilitation scenarios and anatomical correctness. Twenty-two ADLs were recorded, and their segments were analysed to build a database of typical motion patterns. The results include a discussion on the ranges of motion for different ADLs and gender-related differences. Moreover, the similarities and general trends for different motions are presented. The system’s control algorithm will use these results to improve the effectiveness of robotic-assisted physiotherapy. Full article

(This article belongs to the Special Issue Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation)

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17 pages, 3174 KiB

Open AccessArticle

Real-Time Motor Control Using a Raspberry Pi, ROS, and CANopen over EtherCAT, with Application to a Semi-Active Prosthetic Ankle

by Kieran M. Nichols, Rebecca A. Roembke and Peter G. Adamczyk

Actuators 2025, 14(2), 84; https://doi.org/10.3390/act14020084 - 10 Feb 2025

Cited by 2 | Viewed by 1718

Abstract

This paper focused on the implementation method and results of modifying a Raspberry Pi 4 for real-time control of brushless direct-current motors, with application in a semi-active two-axis ankle prosthesis. CANopen over EtherCAT was implemented directly on the Raspberry Pi to synchronize real-time communication between it and the motor controllers. Kinematic algorithms for setting ankle angles of zero to ten degrees in any combination of sagittal and frontal angles were implemented. To achieve reliable motor communication, where the motors continuously move, the distributed clock synchronization of Linux and Motor driver systems needs to have a finely tuned Proportional-Integral compensation and a consistent sampling period. Data collection involved moving the ankle through 33 unique pre-selected ankle configurations nine times. The system allowed for quick movement (mean settling time 0.192 s), reliable synchronization (standard deviation of 4.51 microseconds for sampling period), and precise movement (mean movement error less than 0.2 deg) for ankle angle changes and also a high update rate (250 microseconds sampling period) with modest CPU load (12.48%). This system aims to allow for the prosthesis to move within a single swing phase, enabling it to efficiently adapt to various speeds and terrains, such as walking on slopes, stairs, or around corners. Full article

(This article belongs to the Special Issue Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation)

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15 pages, 2824 KiB

Open AccessArticle

The Technical Development of a Prototype Lower-Limb Therapy Device for Bed-Resting Users

by Juan Fang, Adrien Cerrito, Simón Gamero Schertenleib, Patrick von Raumer and Kai-Uwe Schmitt

Actuators 2025, 14(2), 60; https://doi.org/10.3390/act14020060 - 26 Jan 2025

Viewed by 872

Abstract

It is generally recommended that bed-resting patients be mobilised early to promote recovery. The aim of this work was to develop and evaluate the usability of a prototype in-bed lower-limb therapy device that offers various training patterns for the feet and legs, featuring an intuitive user interface and interactive exergames. Based on clinical interviews, the user requirements for the device were determined. The therapy device consisted of two compact foot platforms with integrated electric motors and force sensors. Movement control strategies and a user interface with computer games were developed. Through a touch screen, the target force and position trajectories were defined. Using automatic position and force control algorithms, the device produced leg flexion/extension with synchronised ankle plantarflexion/dorsiflexion as well as leg pressing with adjustable resistive loading. An evaluation test on 12 able-bodied participants showed that the device produced passive (mean position control errors: 8.91 mm linearly and 1.62° in the ankle joints) and active leg training (force control error: 2.52 N). The computer games were proven to be interesting, engaging, and responsive to the training movement. It was demonstrated that the device was technically usable in terms of mechatronics, movement control, user interface, and computer games. The advancements in well-controlled movement, multi-modal training patterns, convenient operation, and intuitive feedback enable the compact therapy device to be a potential system for bed-resting users to improve physical activity and cognitive functionality. Full article

(This article belongs to the Special Issue Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation)

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13 pages, 4213 KiB

Open AccessArticle

Machine Learning Models for Assistance from Soft Robotic Elbow Exoskeleton to Reduce Musculoskeletal Disorders

by Sanjana Suresh, Inderjeet Singh and Muthu B. J. Wijesundara

Actuators 2025, 14(2), 44; https://doi.org/10.3390/act14020044 - 22 Jan 2025

Cited by 1 | Viewed by 1436

Abstract

Musculoskeletal disorders are very common injuries among occupational and healthcare workers. These injuries are preventable in many scenarios using exoskeleton-based assistive technology. Soft robotics initiates an evolution in exoskeleton devices due to their safe human interactions, ergonomic design, and adaptive characteristics. Despite their enormous advantages, it is a challenging task to model and control soft robotic devices due to their inherent nonlinearity and hysteresis. Learning-based approaches are becoming more popular to overcome these limitations. This work proposes an approach to estimate the pressure input for a pneumatically actuated soft robotic elbow exoskeleton to assist occupational workers to avoid musculoskeletal disorders. An elbow exoskeleton design made up of modular pneumatic soft actuators is discussed, which helps to flex/extend an elbow joint. Machine learning (ML) approaches are used to develop a relationship between the air pressure, the bending angle of the elbow, and the percentage of the weight of the arm to be assisted by the exoskeleton. The most popular and widely used regression-based ML approaches are applied and compared in terms of accuracy and computation cost. Further, a modified KNN (K-Nearest Neighbor) approach is proposed, which outperforms the accuracy of other approaches. Full article

(This article belongs to the Special Issue Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation)

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12 pages, 796 KiB

Open AccessArticle

Tug-of-War-Style High-Force Fluidic Actuation for Small Diameter Steerable Instruments

by Robert Lathrop, Mouloud Ourak, Jan Deprest and Emmanuel Vander Poorten

Actuators 2024, 13(11), 444; https://doi.org/10.3390/act13110444 - 7 Nov 2024

Viewed by 1068

Abstract

Miniature steerable instruments have the potential to reduce the invasiveness of therapeutic interventions and enable new treatment options. Traditional ways of driving such instruments rely on extrinsic systems due to the challenge of miniaturizing and embedding intrinsic actuators that are powerful enough near the instrument tip or within the instrument shaft. This work introduces a method to amplify the output force of fluidic actuators by connecting their outputs in parallel but distributing them serially in currently underutilized space along the device’s long axis. It is shown that this new approach makes it possible to realize a significant force amplification within the same instrument diameter, producing a

380 %

higher static force and a further driving motion of the steerable bending segment 55.6° than an actuator representing the current state of the art, all while occupying a similar footprint. Full article

(This article belongs to the Special Issue Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation)

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22 pages, 8855 KiB

Open AccessArticle

Passive and Active Training Control of an Omnidirectional Mobile Exoskeleton Robot for Lower Limb Rehabilitation

by Suyang Yu, Congcong Liu, Changlong Ye and Rongtian Fu

Actuators 2024, 13(6), 202; https://doi.org/10.3390/act13060202 - 25 May 2024

Cited by 4 | Viewed by 1897

Abstract

As important auxiliary equipment, rehabilitation robots are widely used in rehabilitation treatment and daily life assistance. The rehabilitation robot proposed in this paper is mainly composed of an omnidirectional mobile platform module, a lower limb exoskeleton module, and a support module. According to the characteristics of the robot’s omnidirectional mobility and good stiffness, the overall kinematic model of the robot is established using the analytical method. Passive and active training control strategies for an omnidirectional mobile lower limb exoskeleton robot are proposed. The passive training mode facilitates the realization of the goal of walking guidance and assistance to the human lower limb. The active training mode can realize the cooperative movement between the robot and the human through the admittance controller and the tension sensor and enhance the active participation of the patient. In the simulation experiment, a set of optimal admittance parameters was obtained, and the parameters were substituted into the controller for the prototype experiment. The experimental results show that the admittance-controlled rehabilitation robot can perceive the patient’s motion intention and realize the two walking training modes. In summary, the passive and active training control strategies based on admittance control proposed in this paper achieve the expected purpose and effectively improve the patient’s active rehabilitation willingness and rehabilitation effect. Full article

(This article belongs to the Special Issue Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation)

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Review

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22 pages, 296 KiB

Open AccessReview

Next-Generation Tools for Patient Care and Rehabilitation: A Review of Modern Innovations

by Faisal Mehmood, Nazish Mumtaz and Asif Mehmood

Actuators 2025, 14(3), 133; https://doi.org/10.3390/act14030133 - 8 Mar 2025

Cited by 1 | Viewed by 2083

Abstract

This review article explores the transformative impact of next-generation technologies on patient care and rehabilitation. The advent of next-generation tools has revolutionized the fields of patient care and rehabilitation, providing modern solutions to improve scientific outcomes and affected person studies. Powered through improvements in artificial intelligence, robotics, and smart devices, these improvements are reshaping healthcare with the aid of improving therapeutic approaches and personalizing treatments. In the world of rehabilitation, robotic devices and assistive technology are supplying essential help for people with mobility impairments, promoting more independence and healing. Additionally, wearable technology and real-time tracking systems permit continuous fitness information monitoring, taking into consideration early analysis and extra effective, tailored interventions. In clinical settings, these modern-day innovations have automated diagnostics, enabled remote patient-monitoring, and brought virtual rehabilitation systems that expand the reach of clinical experts. This comprehensive review delves into the evolution, cutting-edge programs, and destiny potential of that equipment by examining their capability to deliver progressed care even while addressing growing needs for efficient healthcare solutions. Furthermore, this review explores the challenges related to their adoption, including ethical considerations, accessibility barriers, and the need for refined regulatory standards to ensure their safe and widespread use. Full article

(This article belongs to the Special Issue Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation)

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