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Actuators
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Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation
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Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4585

Special Issue Editors

Dr. Duk Shin

E-Mail Website
Guest Editor
Department of Mechatronics, Tokyo Polytechnic University, Atsugi 243-0297, Japan
Interests: BMI/BCI; rehabilitation robot
Dr. JaeHyo Kim

E-Mail Website
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

E-Mail Website
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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

17 pages, 3174 KiB  
Article
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
Viewed by 479
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Exploded views of the new TADA design comprising an outside U-joint and inside CAM wedges actuated by brushless DC motors. The bottom half is identical to the exploded top half shown. The motors directly rotate the wedges, which move the ankle to various sagittal and/or frontal angles.</p> Full article ">Figure 2
<p>Conceptual diagram showing the controller software architecture among the Linux and Windows computers. The primary Linux computer (Raspberry PI) contains the Brain and Motor nodes that control the TADA and collects data for the experiments. A secondary Windows computer communicates with the primary computer using a private mobile hotspot using ROS WI-FI communication. The secondary computer collects the data and allows for GUI interaction. The red and blue arrows describe inter- and intra-node communication.</p> Full article ">Figure 3
<p>Example diagrams showing the relationship of the wedge rotation, the TADA control angles, and the anatomical ankle angles for frontal plane variations in EV to IV ankle angles. A similar relationship controls the sagittal variation in PF to DF ankle angles. Intermediate angles can also be used for combined frontal and sagittal motions.</p> Full article ">Figure 4
<p>Plot of the actual SP across the experiment time. Each color represents a different condition. These scatter plots show stable matching of the intended SP with some variability. There are three commanded SPs of 250, 500, and 1000 μs.</p> Full article ">Figure 5
<p>One-sided violin plots of the various conditions of clock synchronization settings with a commanded SP of 250 μs. These violin plots show a kernel-density estimation of the data distribution, where the peak of the plot represents the data that are most dense. A vertical black line represents the controlled SP of 250 μs. Conditions 1–5 (<a href="#actuators-14-00084-t002" class="html-table">Table 2</a>) are shown. The conditions are ordered in this table to group the changes in I first, followed by the base condition (Condition 3) and then the changes in P.</p> Full article ">Figure 6
<p>Example data from one movement, plotting ankle angle commands (intended angle) and actual ankle angle based on the motors’ position sensors. The blue lines represent the IV angles, and the red lines represent the PF angles. The black dots are the 95% rise times, and the black Xs are for the settling times.</p> Full article ">Figure 7
<p>Violin plots of movement times for 95% rise and settling (left side and axis) and PF, IV errors (right side and axis) for the TADA angle changes. Each violin plot gives a kernel-density estimation of the data distribution, and it also contains the box and whisker diagrams to indicate first and third quartiles (Q1 and Q3 as black solid lines), the medians (black solid lines), and the means (black dashed lines).</p> Full article ">Figure 8
<p>Plot of PF and IV angles with the intended orientation (command with red marker) and actual position (based on the motors’ position sensors). The actual orientation is represented by blue dots with blue error bars (T shaped from the blue dot) for PF and IV errors. The full set of actual positions (blue dots) represents 291 TADA orientations, where each blue dot has 8–9 samples.</p> Full article ">
15 pages, 2824 KiB  
Article
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 379
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 [...] Read more.
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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Figure 1

Figure 1
<p>CAD of the lower-limb therapy device. The system housing, the foot plates, and the cases for the foot platforms were removed so as to show the drives and mechanical components. (1) Motor for linear movement, (2) motor for ankle dorsiflexion/plantarflexion, and (3) force sensor.</p> Full article ">Figure 2
<p>The prototype of the lower-limb therapy device on a medical bed (<b>a</b>) and with a test person (<b>b</b>): (1) touch screen, (2) emergency stop, (3) foot plate, and (4) force sensor.</p> Full article ">Figure 3
<p>Control algorithms. (<b>a</b>) Position control. (<b>b</b>) Force control.</p> Full article ">Figure 4
<p>System programming architecture. The red dotted line means communication between the Microcontroller and the Motor controller. The green dotted line indicates data export and transport using a USB-Key.</p> Full article ">Figure 5
<p>The pages for the user interface: (<b>a</b>) Testing, (<b>b</b>) Computer Game, and (<b>c</b>) Data.</p> Full article ">Figure 6
<p>An example of the computer game’s sinusoidal curve: (<b>a</b>) game setup, and (<b>b</b>) computer game shown on the touch screen. (1) Moving point and (2) coin.</p> Full article ">Figure 7
<p>Passive position control of the right leg of a representative participant (P5). (<b>a</b>,<b>b</b>) the position of the foot platform and the motor velocity to produce the linear movement. (<b>c</b>,<b>d</b>) the position of the ankle joint and the motor velocity to produce dorsiflexion/plantarflexion.</p> Full article ">Figure 8
<p>Force control of the active load on the right leg of the representative participant (P5). (<b>a</b>–<b>c</b>) are the force, motor current and the linear movement during the active training.</p> Full article ">
13 pages, 4213 KiB  
Article
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
Viewed by 611
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 [...] Read more.
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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Figure 1

Figure 1
<p>Elbow Exoskeleton: (<b>A</b>) soft pneumatic elbow exoskeleton; (<b>B</b>) experimental setup for data collection.</p> Full article ">Figure 2
<p>Pressure vs. angle plot with different weights.</p> Full article ">Figure 3
<p>Plot between mean squared error (MSE) and polynomial degree.</p> Full article ">Figure 4
<p>Plot between mean absolute error (MAE) and number of nearest neighbors (k).</p> Full article ">Figure 5
<p>Plot between mean absolute error (MAE) and tree depth.</p> Full article ">Figure 6
<p>Plot between OOB error and number of trees (<math display="inline"><semantics> <msub> <mi>n</mi> <mi>estimators</mi> </msub> </semantics></math>).</p> Full article ">Figure 7
<p>Plot between OOB error and number of trees (<math display="inline"><semantics> <msub> <mi>n</mi> <mi>estimators</mi> </msub> </semantics></math>).</p> Full article ">Figure 8
<p>Plot between mean absolute error (MAE) and number of neurons for different hidden layers.</p> Full article ">Figure 9
<p>Linear Regression fit with K Nearest Neighbors.</p> Full article ">
12 pages, 796 KiB  
Article
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 817
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 [...] Read more.
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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Figure 1
<p>An intrinsically actuated flexible fetoscope being steered to access difficult-to-reach points on the placental surface for a laser coagulation procedure.</p> Full article ">Figure 2
<p>Basic functional principle and geometry of a pneumatic artificial muscle, including (<b>a</b>) PAM at rest with labeled geometric parameters relating the braid geometry to actuator dimensions and (<b>b</b>) pressurized PAM contracting axially and exerting an actuation force.</p> Full article ">Figure 3
<p>Serially linked PAMs (<b>a</b>) at rest and (<b>b</b>) contracting during actuation. Proximal PAM ends are connected and fixed in place via a cable (green). Distal PAM ends are connected to the actuation target via a second cable (blue). An assembled prototype chain of linked PAMs are shown (<b>c</b>) at rest and (<b>d</b>) during actuation.</p> Full article ">Figure 4
<p>Test set-up for force and displacement measurements including (<b>a</b>) force sensor, (<b>b</b>) laser distance sensor, (<b>c</b>) pneumatic artificial muscle clamped in place for evaluation, (<b>d</b>) lockable linear slide, (<b>e</b>) pressure sensor, and (<b>f</b>) pressure control valve.</p> Full article ">Figure 5
<p>Results of comparative characterization testing for a single PAM and a group of 5 collaborative PAMs, including (<b>a</b>) maximum static force generation at variable frequency pressure actuation and (<b>b</b>) maximum stroke length when free to contract.</p> Full article ">Figure 6
<p>Comparative images of (<b>a</b>) the internal construction of the bending segment, (<b>b</b>) the maximum bend angle achieved for the 15 mm-long steerable bending segment actuated by a single 250 mm-long PAM, and (<b>c</b>) the same segment actuated by the collaborative output of <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>×</mo> <mn>50</mn> </mrow> </semantics></math> mm-long PAMs, showing the measured angle of the bent laser fiber relative to its unactuated position.</p> Full article ">Figure 7
<p>Bend angles achieved by actuation using a single 250 mm-long PAM compared to the collaborative output of five 50 mm PAMs.</p> Full article ">
22 pages, 8855 KiB  
Article
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 1 | Viewed by 1345
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 [...] Read more.
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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<p>Omnidirectional mobile lower limb exoskeleton robot.</p> Full article ">Figure 2
<p>Lower limb exoskeleton module.</p> Full article ">Figure 3
<p>Omnidirectional mobile platform kinematic model.</p> Full article ">Figure 4
<p>Lower limb exoskeleton kinematic model.</p> Full article ">Figure 5
<p>Overall kinematic model.</p> Full article ">Figure 6
<p>Lower limb workspace: (<b>a</b>) motion range in 2D coordinates; (<b>b</b>) motion range in 3D coordinates.</p> Full article ">Figure 7
<p>Rehabilitation robot control hardware.</p> Full article ">Figure 8
<p>Model of the interaction between the robot admittance controller and the human.</p> Full article ">Figure 9
<p>The passive and active training control strategies.</p> Full article ">Figure 10
<p>Admittance controller block diagram design.</p> Full article ">Figure 11
<p>The admittance controls the step response curve: (<b>a</b>) <span class="html-italic">M<sub>d</sub></span> = 0.5, <span class="html-italic">K<sub>d</sub></span> = 800; (<b>b</b>) <span class="html-italic">M<sub>d</sub></span> = 0.5, <span class="html-italic">B<sub>d</sub></span> = 40.</p> Full article ">Figure 12
<p>Wear devices and sensors.</p> Full article ">Figure 13
<p>The robot follows the human body as it walks: (<b>a</b>) walking trajectory; (<b>b</b>) walking training process.</p> Full article ">Figure 14
<p>Hip contact force.</p> Full article ">Figure 15
<p>Moving platform trajectory.</p> Full article ">Figure 16
<p>Exoskeleton−assisted trajectories in passive mode: (<b>a</b>) hip trajectory; (<b>b</b>) knee trajectory; (<b>c</b>) hip trajectory errors; (<b>d</b>) knee trajectory errors.</p> Full article ">Figure 16 Cont.
<p>Exoskeleton−assisted trajectories in passive mode: (<b>a</b>) hip trajectory; (<b>b</b>) knee trajectory; (<b>c</b>) hip trajectory errors; (<b>d</b>) knee trajectory errors.</p> Full article ">Figure 17
<p>Admittance −controlled exoskeleton trajectories in active mode: (<b>a</b>) hip trajectory; (<b>b</b>) knee trajectory; (<b>c</b>) hip trajectory error; (<b>d</b>) knee trajectory error; (<b>e</b>) hip time delay; (<b>f</b>) knee time delay.</p> Full article ">Figure 17 Cont.
<p>Admittance −controlled exoskeleton trajectories in active mode: (<b>a</b>) hip trajectory; (<b>b</b>) knee trajectory; (<b>c</b>) hip trajectory error; (<b>d</b>) knee trajectory error; (<b>e</b>) hip time delay; (<b>f</b>) knee time delay.</p> Full article ">
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Next-Generation Tools for Patient Care and Rehabilitation: A Review of Modern Innovations
Authors: Faisal Mehmood; Nazish Mumtaz; Asif Mehmood
Affiliation: Department of Biomedical Engineering, Gachon University, South Korea
Abstract: 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 actual-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 assessment delves into the evolution, cutting-edge programs, and destiny potential of those equipment, examining their capability to deliver progressed care even as 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.

Title: Design, Fabrication, and Evaluation of a Prototype Exoskeleton for Arm Swing Training
Authors: Liam Hawthrone, Ali Faeghinejad, Babak Hejrati
Affiliation: University of Maine
Abstract: This paper presents the design, fabrication, and testing of a proof-of-concept arm swing rehabilitator exoskeleton (ASRE) to induce arm swing at different frequencies for integrating arm swing in gait training. Current exoskeletons mostly focus on the lower extremities for gait training, while the role of proper arm swing during walking and gait training is often overlooked. Also, limited research has been conducted to determine how much torque is needed to effect changes in arm swing parameters such as frequency and amplitude to provide a coordinated arm-leg response during gait training. The proposed prototype was designed to serve as a research tool to determine the amount of required torque to generate kinesthetic feedback at the users' arms and, thereby, induce appropriate arm swing patterns. To increase user comfort, the ASRE was designed with distal actuation, allowing the weight of the actuator to be supported on the user's back, with power being transferred to the arm through double parallelogram linkages, a novel pulley-belt system, and Bowden cables. We performed various evaluations including workspace analysis, static and dynamic load testing, and actuation frequency and amplitude evaluation. The results showed a large workspace with some limitations in shoulder internal and external rotation and with the capacity to generate torques of 10.74-15.09 N.m. The ASRE was found capable of producing actuation frequencies exceeding those of the average human arm swing. The goal of the ASRE was also to investigate the use of kinesthetic feedback to induce and alter arm swing through the evaluation of the subjects' responses to feedback. The results indicated that the ASRE was a successful experimental tool for exploring the feasibility of inducing arm swing at different frequencies and the use of kinesthetic feedback to do so.

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