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Advances in Robotics and Sensors for Rehabilitation

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 10243

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Special Issue Editors

Dr. Vidas Raudonis

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Guest Editor

Department of Automation, Kaunas University of Technology, 51367 Kaunas, Lithuania
Interests: robotics; AI; computer vision; IVF (In Vitro Fertilization) technologies; early-stage embryo evaluation; uterus receptivity evaluation
Special Issues, Collections and Topics in MDPI journals

Dr. Arunas Lipnickas

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Guest Editor

Electrical and Electronics Engineering Faculty, Kaunas University of Technology, 51367 Kaunas, Lithuania
Interests: AI; computer vision; electrical drives

Dr. Ligita Šilinė

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Guest Editor Assistant

Department of Rehabilitation, Kauno Kolegija Higher Education Institution, Medicine Faculty, Kaunas, Lithuania
Interests: rehabilitation; health promotion; cognitive aging; exercise physiology Social media account

Special Issue Information

Dear Colleagues,

The field of rehabilitation is undergoing transformation driven by the convergence of robotics and sensor technologies. This Special Issue of Sensors, entitled “Advances in Robotics and Sensors for Rehabilitation”, explores this exciting intersection, showcasing cutting-edge research that is transforming how we recover from injury and illness.

Regaining mobility and independence is a core goal of rehabilitation. This Special Issue highlights the development of robotic assistive devices, such as exoskeletons and robotic wheelchairs, that physically aid patients in movement therapy. It delves into the critical role of sensors in these devices, providing real-time feedback on a patient’s progress and enabling personalized therapy plans.

Beyond physical assistance, this Special Issue explores the growing prominence of wearable sensors. These unobtrusive devices continuously monitor vital signs, movement patterns, and even environmental factors, allowing for remote patient monitoring and the prevention of secondary complications. The integration of artificial intelligence with sensor data further enhances rehabilitation by creating intelligent systems that adapt to individual needs and provide real-time feedback.

This Special Issue welcomes contributions that showcase the latest advancements in robotics, sensor technology, and the application of artificial intelligence methods for rehabilitation. We invite researchers to share their work on the following topics:

Novel robotic assistive devices for physical therapy;
Sensor integration for real-time patient monitoring;
Wearable technology for at-home rehabilitation;
Human–robot interaction in rehabilitation settings;
The application of artificial intelligence and machine learning for personalized therapy.

This Special Issue aims to accelerate the development of innovative solutions that empower individuals on their path to recovery by collaboration between robotics, sensors, and AI experts.

Dr. Vidas Raudonis
Dr. Arunas Lipnickas
Guest Editors

Dr. Ligita Šilinė
Guest Editor Assistant

Manuscript Submission Information

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Keywords

robotic assistive devices
sensor integration
wearable technology
remote patient monitoring

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Published Papers (8 papers)

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Research

17 pages, 1446 KB

Open AccessArticle

Robot-Assisted Gait Training Enhances Phase-Specific Torque Generation, Balance, and Motor Recovery in Hemiplegia

by Gökhan Özkoçak, Ecem Sorucu and Rocco Salvatore Calabrò

Sensors 2026, 26(10), 2920; https://doi.org/10.3390/s26102920 - 7 May 2026

Viewed by 407

Abstract

Gait dysfunction is a common and disabling consequence of stroke, frequently associated with impaired lower-limb torque generation and reduced balance. Robot-assisted gait training (RAGT) has emerged as a promising intervention; however, its phase-specific biomechanical effects remain incompletely characterized. This pilot mechanistic study investigated the effects of Walkbot-assisted gait training on phase-specific lower-limb torque and clinical outcomes in individuals with unilateral hemiplegia. Fifteen patients with hemiplegia underwent Walkbot-assisted gait training. Joint torque values were normalized to body mass (Nm/kg). Phase-specific torque was analyzed during the swing and stance phases for the affected and unaffected limbs. Pre–post differences were evaluated using the Wilcoxon signed-rank test. Functional balance and motor impairment were assessed using the Berg Balance Scale (BBS) and the Fugl–Meyer Assessment—Lower Extremity (FMA-LE). Significant torque increases were observed in both gait phases. Median swing-phase torque increased from 0.261 to 0.361 Nm/kg in the affected limb and from 0.254 to 0.334 Nm/kg in the unaffected limb (p ≤ 0.017). Stance-phase torque increased from 0.197 to 0.454 Nm/kg in the affected limb and from 0.158 to 0.471 Nm/kg in the unaffected limb. Clinical outcomes improved significantly, with median BBS scores increasing from 22.0 to 34.0 and FMA-LE scores from 14.0 to 24.0 (p = 0.001). Walkbot-assisted gait training was associated with significant phase-specific torque gains, accompanied by improvements in balance and lower-limb motor recovery. Full article

(This article belongs to the Special Issue Advances in Robotics and Sensors for Rehabilitation)

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16 pages, 1889 KB

Open AccessArticle

Model Predictive Control-Based Assist-as-Needed Strategy for Reducing Motor Slacking in Robot-Assisted Rehabilitation

by Choonggun Kim, Youngjin Moon and Jaesoon Choi

Sensors 2026, 26(9), 2740; https://doi.org/10.3390/s26092740 - 28 Apr 2026

Viewed by 714

Abstract

This study proposes a model predictive control (MPC)-based Assist-as-Needed (AAN) strategy for upper-limb rehabilitation robots, with particular emphasis on mitigating motor slacking. In conventional error-based AAN approaches, robotic assistance is regulated through a single coefficient tied to the tracking error; thus, a reduction in voluntary effort is absorbed into the assistive channel and remains obscured by a small tracking error. The proposed method decouples this mechanism by introducing a two-channel admittance structure, in which the robotic-assistance gain

A_{k}

and the user-participation-reflection gain

B_{k}

are jointly optimized within a single convex MPC formulation. The cost function addresses trajectory tracking, participation-aware force alignment, assistance suppression, and passivity, enforced through energy-tank constraints. The controller was validated in two experiments on a mobile upper-limb rehabilitation robot. The first experiment confirmed differential adaptation of

A_{k}

and

B_{k}

across three instructed contribution levels, with the participation ratio increasing from 0.103 to 0.879 as the contribution shifted from insufficient to appropriate. The second experiment compared the controller with an error-based AAN baseline and a forgetting-factor AAN baseline under an induced motor-slacking condition, in which the task-direction contribution was reduced to 45%. Under an identical synthesized input, the proposed controller yielded a lower aggregate human-contribution ratio of 0.282, compared with 0.595 and 0.535 for the two baselines, respectively. This indicates that the externally imposed reduction in participation was represented more explicitly in the controller allocation, rather than being masked by error-driven assistive compensation. These results suggest that the proposed approach extends AAN control toward a participation-preserving, anti-slacking strategy for robot-assisted rehabilitation. Full article

(This article belongs to the Special Issue Advances in Robotics and Sensors for Rehabilitation)

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16 pages, 1925 KB

Open AccessArticle

Advances in Ultrasonic Rehabilitation

by Vytautas Ostasevicius, Vytautas Jurenas, Laura Kizauskiene, Agne Paulauskaite-Taraseviciene, Joris Vezys, Algimantas Bubulis and Arnas Nakrosis

Sensors 2026, 26(8), 2428; https://doi.org/10.3390/s26082428 - 15 Apr 2026

Viewed by 1177

Abstract

The fundamental differences between high- and low-frequency ultrasound for medical purposes were demonstrated. A model describing the effect of ultrasound on erythrocyte aggregation was developed, and the rapid movement of erythrocytes toward the nodes of a standing acoustic wave was demonstrated, with its velocity compared to the rate of erythrocyte dissociation under the influence of shear forces. The t-test was used to assess the statistical significance of differences between two blood samples and confirmed the effect of low-frequency ultrasound intensity on erythrocyte aggregation. The study employed a patented low-frequency ultrasound transducer generating a traveling acoustic wave that produces shear forces capable of disrupting erythrocyte aggregates into individual erythrocytes. Since the developed technique is intended for human therapy, it is assumed that the proposed low-frequency ultrasound parameters are safe for flowing blood. Due to deeper and more precise penetration of the acoustic signal into tissues, this ultrasound transducer may be promising for improving microcirculation and alleviating patient condition without medication, as well as for reducing blood pressure and heart rate. The developed technique also enables more effective disruption of heart valve plaques and shows therapeutic potential for tumor treatment and in vivo drug encapsulation. Since erythrocytes in diabetic patients are stiffer than those in healthy individuals, their passage through capillaries is more difficult. Therefore, the developed and patented ultrasound-based sole stimulation technique may produce a positive physiological effect by stimulating blood flow in the capillaries of patients with foot ulcers. Full article

(This article belongs to the Special Issue Advances in Robotics and Sensors for Rehabilitation)

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28 pages, 6758 KB

Open AccessArticle

Measurement-Based Optimization of a Lightweight Upper-Extremity Rehabilitation Exoskeleton for Task-Oriented Treatment

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

Sensors 2026, 26(6), 1849; https://doi.org/10.3390/s26061849 - 15 Mar 2026

Viewed by 611

Abstract

Contemporary physiotherapy requires technological tools to provide effective therapy to the increasing group of patients with neurological conditions, among others. This can be achieved with rehabilitation robots, which can also be exoskeletons—wearable devices that mobilize multiple joints with complex motions representing activities of daily living. To perform kinesiotherapy conveniently in home-like environments, the exoskeletons need to be relatively lightweight. The paper presents the methodology for decreasing the mass of the exoskeleton design with real-life data-driven simulations of motions, followed by multibody dynamics simulations, and finite element method (FEM) multistep optimization. The process includes sequential initial parametric optimization, topology optimization, and final parametric optimization. The steps are used to set initial dimensional and material parameters, extract new geometrical features, and adjust the final geometry dimensions of a new design. The presented case of the SmartEx-Home exoskeleton resulted in a total mass reduction of almost 50% for the main construction elements while meeting the criteria of the minimum safety factor and maximum internal stress and strain for all components. The final design was manufactured and tested with humans, reflecting an almost fully automatic passive and active therapy. Full article

(This article belongs to the Special Issue Advances in Robotics and Sensors for Rehabilitation)

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22 pages, 3329 KB

Open AccessArticle

Action-Aware Multimodal Wavelet Fusion Network for Quantitative Elbow Motor Function Assessment Using sEMG and Robotic Kinematics

by Zilong Song, Pei Zhu, Cuiwei Yang, Daomiao Wang, Jialiang Song, Daoyu Wang, Fanfu Fang and Yixi Wang

Sensors 2026, 26(3), 804; https://doi.org/10.3390/s26030804 - 25 Jan 2026

Viewed by 611

Abstract

Accurate upper-limb motor assessment is critical for post-stroke rehabilitation but relies on subjective clinical scales. This study proposes the Action-Aware Multimodal Wavelet Fusion Network (AMWFNet), integrating surface electromyography (sEMG) and robotic kinematics for automated Fugl-Meyer Assessment (FMA-UE)-aligned quantification. Continuous Wavelet Transform (CWT) converts heterogeneous signals into unified time-frequency scalograms. A learnable modality gating mechanism dynamically weights physiological and kinematic features, while action embeddings encode task contexts across 18 standardized reaching tasks. Validated on 40 participants (20 post-stroke, 20 healthy), AMWFNet achieved 94.68% accuracy in six-class classification, outperforming baselines by 9.17% (Random Forest: 85.51%, SVM: 85.30%, 1D-CNN: 91.21%). The lightweight architecture (1.27 M parameters, 922 ms inference) enables real-time assessment-training integration in rehabilitation robots, providing an objective, efficient solution. Full article

(This article belongs to the Special Issue Advances in Robotics and Sensors for Rehabilitation)

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22 pages, 2935 KB

Open AccessArticle

Gender and Age-Related Decline in Lower Limb Standing Muscle Strength: Benchmarking for Rehabilitation Assessment

by Vidas Raudonis, Viktorija Staneikaite, Ugnė Kubiliūtė, Raimondas Kubilius, Sarah Grube, Maximilian Neidhardt, Alexander Schlaefer and Gediminas Tankevičius

Sensors 2026, 26(1), 69; https://doi.org/10.3390/s26010069 - 22 Dec 2025

Cited by 1 | Viewed by 1199

Abstract

This study aimed to demonstrate a novel sensor-based measuring stand for objective assessment of multi-directional lower limb muscle strength and to establish essential, age- and gender-stratified normative benchmarks. This cross-sectional study measured relative leg strength (N/kg) across six standing movements in 99 healthy, non-professional athletes (males and females aged 20–30, 40–50, and 60–70 years). Results confirmed that men exhibited significantly greater strength than women across all six directions (17% to 35% difference). Furthermore, a marked age-related decline was consistently observed in both sexes, with the largest and most clinically relevant differences (often exceeding 30%) concentrated in the transition to the 60–70-year range. Methodologically, these findings are limited to demonstrating age-related differences rather than longitudinal decline and are specific to an active, healthy cohort. This study demonstrates the sensor-based stand as an efficient, objective tool for comprehensive strength assessment, but its clinical utility is prospective and requires further validation against diverse and pathological patient populations. Full article

(This article belongs to the Special Issue Advances in Robotics and Sensors for Rehabilitation)

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15 pages, 1734 KB

Open AccessArticle

Mechanical Analysis for Active Movement of Upper Limb Rehabilitation Robots to Alleviate Shoulder Pain in Patients with Stroke Hemiplegia and Frozen Shoulder

by Seok Jin Bang, Jung-Soo Lee, Dong Hyeon Song, Seung Yeob Ryu and Kwang Gi Kim

Sensors 2025, 25(21), 6644; https://doi.org/10.3390/s25216644 - 30 Oct 2025

Viewed by 1498

Abstract

Shoulder disorders, including frozen shoulder resulting from stroke-induced hemiplegia, significantly reduce a patient’s ability to perform activities of daily living, thereby necessitating repeated rehabilitation. Consequently, extensive research has been conducted on rehabilitation robots to assist in upper-limb motor recovery. The shoulder moves according to the scapulohumeral rhythm. Considering the biomechanical characteristics of the shoulder joint, the rehabilitation robot was designed to replicate a similar kinematic environment using actuators and linkages that emulate the structures of the upper arm, shoulder, and clavicle. To ensure precise operation, the kinematic accuracy of the robot was pre-evaluated. Kinematic analyses were conducted using MATLAB, and the results were compared with coordinate data from the mechanical design to evaluate positional accuracy. In addition, the convergence and accuracy of joint-angle estimation for target positions were analyzed. The forward kinematic analysis revealed that the average positional error between the measured and target coordinates ranged from 0.5% to 2.8%, with the Base Motor–Back Motor segment exhibiting the highest error (2.8%). The inverse kinematic analysis demonstrated stable convergence to the target positions through iterative computations using the Gauss–Newton method, confirming that the actual motion could be accurately reproduced within the designed range of motion. Full article

(This article belongs to the Special Issue Advances in Robotics and Sensors for Rehabilitation)

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12 pages, 1186 KB

Open AccessArticle

Cardiac Function and Fatigue During Exoskeleton-Assisted Sit-to-Stand Maneuver and Walking in People with Stroke with Moderate to Severe Gait Disability: A Pilot Cross-Sectional Study

by Raimondas Kubilius, Darius Ruočkus, Vitalija Stonkuvienė, Rugilė Vareikaitė, Rebecca Cardini and Thomas Bowman

Sensors 2025, 25(1), 172; https://doi.org/10.3390/s25010172 - 31 Dec 2024

Cited by 2 | Viewed by 2580

Abstract

Background. Wearable powered exoskeletons could be used to provide robotic-assisted gait training (RAGT) in people with stroke (PwST) and walking disability. The study aims to compare the differences in cardiac function, fatigue, and workload during activities of daily living (ADLs), while wearing an exoskeleton. Methods. Five PwST were recruited in this pilot cross-sectional study. We observed three experimental conditions: walking without and with the UAN.GO exoskeleton and walking with the UAN.GO combined with the OPTIGO walker. Each condition included five trials related to ADLs such as sitting and walking. Results. No statistically significant difference was found between heart rate and R–R of ECG data while comparing all the observed conditions during each respective trial. The NASA Task Load Index did not show significant differences across all trials, except for a significant difference between Condition 2 and Condition 3 in Trial 4 (p = 0.043). However, walking and sit-to-stand tasks seem to be more challenging according to the NASA-TLX. Only one participant scored over 70 points on the System Usability Scale. The TSQ-WT scores for conditions 2 and 3 were 62 (56.5–72.5) and 70 (66.5–75) points, respectively. Conclusions. This study suggests that UAN.GO exoskeleton could be used for RAGT in PwST with disability without compromising cardiovascular function. Full article

(This article belongs to the Special Issue Advances in Robotics and Sensors for Rehabilitation)

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