Coordinated control of the trunk and pelvis is critical for performing functional upper-body movements, particularly during standing. Deficits in trunk-pelvis coordination are common in populations with neurological or musculoskeletal impairments, contributing to poor balance and limited functional mobility. In this proof-of-concept study, we investigated training strategies in healthy participants to establish a baseline for future rehabilitation applications. Twenty-four individuals were assigned to one of three groups: (i) control, without assistance (Ctrl), (ii) robotic assistance at the trunk, specifically at the thorax (T), and (iii) robotic assistance applied concurrently at the thorax and pelvis (T-P). Training was delivered using the Robotic Upright Stand Trainer (RobUST), which provides assist-as-needed forces based on deviations from target trajectories and normative thorax-pelvis coordination patterns. Participants were trained to perform elliptical thorax movements while standing, a task with progressively increasing postural demands. Results showed that T-P assistance enabled participants to achieve larger ellipse sizes during training compared to T assistance, suggesting that pelvic support facilitated greater exploration of range of motion. Post-training, ellipse tracing accuracy improved in all groups, but only the T-P and Ctrl groups demonstrated significant gains in movement smoothness. Learning-curve analysis further revealed that while T-P participants required a longer acclimatization period, they ultimately achieved higher combined learning metrics than the T group. These findings highlight the potential of trunk-pelvis coordinated assistance to promote greater improvements in postural control than assistance limited to the trunk. The results provide a foundation for developing trunk-pelvis interventions aimed at improving postural control in clinical populations.

Due to their high power-to-weight ratio, modular and reconfigurable architectures, and inherent compliance, cable-driven rehabilitation robots (CDRRs) provide safe, lightweight, backdrivable solutions for gait and movement rehabilitation. However, they continue to face unique control challenges due to cable properties and user variability. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this systematic review explores control strategies for lower-limb CDRRs from the past decade. Out of the 968 studies initially identified, 70 met the selection criteria and were classified into six categories: position and velocity, force- and torque-based, compliance-based, model-based and optimal, learning-based and intention-informed, and hierarchical frameworks. Our analysis revealed a chronological evolution from traditional classical control toward more personalized, adaptive, learning-based, and intention-driven methods. Impedance and admittance control remain fundamental for ensuring safety, while newer approaches enable user-specific and environment-responsive assistance. This review proposes a unified hierarchical framework linking high-level intent detection to low-level actuation providing researchers and developers with a structured understanding of the control landscape for cable-driven lower-limb exoskeletons in healthcare and beyond. Control strategies were also linked to clinical outcomes to relate them to functional improvements across patient populations. Advancing CDRRs will require unified, multi-layer architectures that couple constraint-aware model-based control with adaptive and intention-driven learning to achieve safe, scalable, and clinically meaningful rehabilitation.

Cable-driven wrench applicators (CDWAs) are parallel robotic systems that apply controlled wrenches to the robot end-effector through cable actuation. The presented study introduces a framework for the performance evaluation of CDWAs based on dedicated metrics. It focuses on the geometric analysis of n-cable CDWAs controlling n−2 wrench components and on the experimental comparison of a 4-cable architecture with an 8-cable CDWA. The geometric analysis reveals intrinsic properties of the 4-cable system’s tension distribution and inherent limits in achieving specific control objectives. Both simulations and experimental validation demonstrate that the 4-cable CDWA attains comparable performance in wrench control while requiring higher tensions, yet offers greater ease of use and mechanical simplicity.

After a severe spinal cord injury (SCI), the human spinal circuitry receiving epidural stimulation can generate lower limb postural responses to sensory inputs associated with trunk perturbation. Here, we assessed the effects of different trunk perturbative forces on standing postural responses in six individuals with chronic, motor complete SCI receiving epidural stimulation to facilitate standing. The robotic upright stand trainer (RobUST) provided constant assistance for pelvic control and delivered precise trunk perturbations with different magnitudes (10 ± 4

Individuals with cerebral palsy (CP) experience significant impairments in lower limb mobility, which severely limit their daily activities and overall quality of life. Robotic exoskeletons have emerged as a cutting-edge solution to assist in the rehabilitation of individuals with CP by improving their motor functions. This systematic review, conducted following PRISMA guidelines, critically evaluates lower limb robotic exoskeletons specifically designed for individuals with CP, focusing on their design, rehabilitation interfaces, and clinical effectiveness. The review includes research papers published between 2010 and 2024, analyzing 30 lower limb exoskeletons reported in 57 papers. We analyze each exoskeleton, focusing on its technological features, user experience, and clinical outcomes. Notably, we identify a trend in which researchers are increasingly adapting exoskeleton functions to the specific needs of individual users, facilitating personalized rehabilitation approaches. Additionally, we highlight critical gaps in current research, such as the lack of sufficient long-term evaluations and studies assessing sustained therapeutic impacts. While ease of use remains crucial for these devices, there is a pressing need for user-friendly designs that promote prolonged engagement and adherence to therapy. This comprehensive review of existing gait rehabilitation exoskeleton technologies aimed to inform future design and application, ultimately contributing to the development of devices that better address the needs of individuals with CP and enhance their motor functions and quality of life.

Spatial navigation has been used as a behavioral marker of cognitive impairments. Floor Maze Tests (FMT) are used to characterize navigation where subjects physically move through a two-dimensional maze drawn on the floor. A Virtual Reality version of FMT (VR-FMT) has been developed, which provides a 3-dimensional navigation environment where the height of the maze walls can be altered. For both FMT and VR-FMT, the time used to complete the maze has been reported as the outcome measure to characterize the cognitive function. This study aims to show new performance metrics derived from spatial-temporal gait and balance parameters during navigation through the maze and their association with the cognitive scores in subjects with probable dementia. Sixty-five older adults with probable dementia participated in an experiment where subjects walked in VR-FMT with two wall heights, 2 centimeters (no wall condition) and 2 meters (wall condition). Our results showed that in no wall condition, the gait and balance parameters during navigation were associated with cognitive scores measuring attention and executive function. In wall condition, besides attention and executive function, gait parameters showed a correlation with the scores of the auditory memory. This paper showed that the spatial-temporal gait and balance parameters during spatial navigation are important metrics of cognitive function in addition to the completion time. VR-FMT with walls can help identify early memory impairments in individuals.

Dynamic postural control during sitting is essential for functional mobility and daily activities. Extended reality (XR) presents a promising solution for posture training in addressing conventional training limitations related to patient accessibility and ecological validity. We developed a remote XR rehabilitation system with markerless motion tracking for sitting posture training. Forty-two healthy subjects participated in this proof-of-concept pilot study. Each subject completed 24 rounds of multi-directional reach tasks using the system and 24 rounds without it. Motion data were collected via online meetings using built-in camera in the user's laptop. Functional reach test scores were analyzed to assess the impact of the system on motor performance. Four standard questionnaires were used to assess the effects of this system on presence, simulator sickness, engagement, and enjoyment. Our results indicate that the remote XR training system significantly improved functional reach performance and proved highly effective for telerehabilitation. XR interaction also enhanced training engagement and enjoyment. By bridging the spatial gap between patients and therapists, this system enables personalized and engaging home-based intervention. Additionally, it facilitates more natural movements by eliminating body marker constraints and laboratory limitations. This study should serve as a stepping stone to advancing novel remote XR rehabilitation systems.

Robotic exoskeletons are being increasingly used in clinics for the treatment of medicable disabilities. These exoskeletons, which closely couple with patients’ limbs, need to move in harmony with the endoskeleton motions. To achieve coordination, exoskeletons should be transparent; in other words, they should not interfere with natural human motion or their underlying coordination strategies. Transparency can be achieved through a bio-inspired exoskeleton design and also by implementing appropriate force control methods to maneuver exoskeleton motions. A new hybrid active-passive Gait Exoskeleton-Assisted Rehabilitation (GEAR) robot is presented here for the rehabilitation of lower limb disabilities. The GEAR robot is designed to enhance transparency incorporating a flexible hip joint and a biomimetic knee joint. The proposed GEAR robot also integrates a Remote Centered Motion (RCM) based passive mechanism to support torso and pelvic motions in two planes and features actuated exoskeleton legs in the sagittal plane for treadmill-assisted walking. The exoskeleton legs are actuated at their hip and knee joints using backdrivable actuators. To provide a natural walking experience, the hip joints of the exoskeleton legs offer two passive degrees of freedom in the frontal and transverse planes in addition to the actuated sagittal plane motion. The biomimetic design of the exoskeleton knee joint ensures alignment with the human anatomical knee joint by closely tracking the latter’s instantaneous center of rotation (ICR). To evaluate GEAR robot’s transparency, a comparative study was conducted, involving three healthy subjects. The participants walked freely on a treadmill and then with the GEAR robot operated first in a completely backdrivable (i.e., passive) mode and subsequently in an active mode. The sEMG data collected during these experiments were analyzed to assess robot’s transparency.

Up to a third of dementia cases may be preventable by engaging in protective behaviors, such as staying cognitively active, according to observational data. Yet, current cognitive training protocols to delay dementia onset often fall short. This project focuses on spatial navigation (SN), the ability to travel familiar/unfamiliar environments. Tau and amyloid-beta accumulation starts in regions subserving SN. Even though SN difficulties present an important target, there are few clinical trials aimed at SN. We developed a full-immersive virtual-reality (VR) maze which participants learn to navigate and use Mobile Brain Body Imaging (MoBI) to record body movement and EEG to record and analyze brain activity during active movement through space. We designed VR mazes to induce different navigational strategies (allocentric and egocentric) at different periods (Stand/Encode and Walk/Navigate) of maze learning. Allocentric and egocentric spatial strategies rely on mediotemporal and posterior parietal cortex regions, respectively. So far, we collected data in 10 individuals showing that Stand/Encode time increases as maze complexity increases. Furthermore, we show a significant increase in theta power as participants navigate towards an intersection that requires a memory-based directional decision. Region-specific modulations in theta (3-7Hz) and alpha (8-12Hz) power we enable us to identify, dissociate, and track participants’ brain dynamics applying mediotemporal-based allocentric and posterior parietal-based egocentric navigational strategies and test relationships with improvements in SN. This pilot study will position us to design a future randomized clinical trial to test efficacy to improve navigational abilities, and thereby delay cognitive decline in older adults at-risk for dementia.

Wearable sensors are widely used to assess spatiotemporal gait parameters and their variability, which are critical for fall risk prediction. However, the impact of gait analysis accuracy and precision on fall risk prediction remains unexplored. This study collected gait data from 95 older adults using instrumented footwear on an instrumented walkway which is recognized as a system with gold standards during the 6-minute walking test. Participants were classified into fallers and non-fallers based on retrospective fall history (falls in the 6 months prior to completing the experiment), prospective fall occurrence (falls in the subsequent 6 months after completing the experiment), and a combination of both. Gait parameters and their variability were estimated using three algorithms: the conventional foot displacement method and two support vector regression (SVR) techniques. These features were used to develop fall risk prediction models with four machine learning classifiers: logistic regression, decision tree, support vector machine, and artificial neural network. Our findings demonstrate that the accuracy and precision of gait analysis algorithms significantly influence the estimation of gait parameters and their variability, directly impacting fall risk prediction performance. Using a support vector classifier, the area under the receiver operating characteristic curve (AUC) values for predicting retrospective falls, prospective falls, and either fall type increased from 0.79, 0.84, and 0.77 (conventional method) to 0.85, 0.89, and 0.83 (SVR). These findings show the importance of refining gait analysis accuracy and precision in future studies that aim to use wearable sensors for fall risk assessment in older adults.

Cable-driven wrench applicators are widely used in robotics due to their lightweight design, large workspace, and ability to exert wrenches. Usually, a subset of wrench components needs to be precisely controlled depending on the application at hand, whereas the other wrench components need to be managed according to some criteria. This paper presents a framework for analyzing and comparing cable systems equipped with an arbitrary number of cables greater than the cardinality of the wrench subset they need to apply. Three performance metrics, including one for selecting the tension distribution and two for evaluating cable tensions and the quality of the applied wrench, are introduced. The proposed framework is applied to assess the feasibility of reducing the number of cables to be used in a rehabilitation task, enhancing accessibility and ease of use for the physiotherapist during the exercise. Simulation results validate this possibility.

Navigation through a floor maze has been used to assess cognition of individuals. In this paper, we analyze novel performance outcomes in gait and balance during navigation in Virtual Reality Floor Mazes (VRFM). The unique contributions of this paper are: 1) We analyze changes in the gait and balance as subjects navigate through the mazes of different difficulty. These performance metrics are more discriminatory when compared to completion time, which is used frequently in the literature; 2) Walking steps are classified into straight steps, turn steps, and spin steps as mazes contain frequent turns; 3) Maze difficulty is defined by the number of decision points. We conducted experiments with ten young healthy subjects across three conditions: 1) Control Mazes (CM) wherein the path from the start to the goal was displayed; 2) Easy Mazes (EM) which contained a maximum of two decision points; 3) Hard Mazes (HM) which contained more than two decision points. The results showed that in hard mazes, subjects took smaller and slower steps with increased gait variability when compared to control mazes or easy mazes. Spin steps showed an increased mediolateral margin of stability in hard mazes compared to easy mazes. The mediolateral center of mass displacement was smaller in straight steps and turn steps in hard mazes when compared to control mazes. These results provide new performance metrics to evaluate navigation in floor mazes. These performance metrics describe how the spatiotemporal parameters of gait change in mazes of different difficulty as opposed to completion time which is a cumulative measure of gait performance.

This paper introduces a 3D parallel robot with three identical five-degree-of-freedom chains connected to a circular brace end-effector, aimed to serve as an assistive device for patients with cervical spondylosis. The inverse kinematics of the system is solved analytically, whereas learning-based methods are deployed to solve the forward kinematics. The methods considered herein include a Koopman operator-based approach as well as a neural network-based approach. The task is to predict the position and orientation of end-effector trajectories. The dataset used to train these methods is based on the analytical solutions derived via inverse kinematics. The methods are tested both in simulation and via physical hardware experiments with the developed robot. Results validate the suitability of deploying learning-based methods for studying parallel mechanism forward kinematics that are generally hard to resolve analytically.

Specialists globally employ various clinical scales and instruments to assess balance, gait, and motor functions in children with cerebral palsy (CP). Selecting appropriate assessment tools is essential for planning studies, developing effective treatment strategies, and tracking clinical outcomes. Given the diversity in assessment needs - whether evaluating dynamic, functional, or static balance - there is a need to identify the most suitable tools for each aspect. Therefore, the primary objective of this review is to critically analyze current clinical and instrument-based assessment methods in the literature to determine the most effective approaches for pediatric CP. This systematic review retrieved 1,812 papers, of which only 23 met the inclusion criteria and presented assessment methods for evaluating balance and motor functions in pediatric CP. These methods were further organized into clinical and instrument-based assessment groups. Among clinical examinations, the Pediatric Balance Scale and Gross Motor Function Measures were considered gold standards and featured in eight studies. In contrast, postural sway measured with the Biodex Balance System, Gait Stability Indices from the GAITRite system, and EMG sensing were the predominant instrument-based observations. Despite this variety, a consensus on the best assessment methods remains lacking. This review highlights the potential of integrating AI-driven metrics that combine clinical and instrument-based data to enhance precision and individualized care. Future research should focus on creating integrated, individualized profiles to better capture the unique capabilities of children with CP, enabling more personalized and effective intervention strategies.

This pilot study offers preliminary evidence that a virtual meal-preparation task is feasible for older adults and highlights that the community engagement studios are an effective approach to generate community-informed strategies to enhance intervention designs and reach.

This paper presents the development of a wearable ankle rehabilitation robot based on a 3-RRR spherical parallel mechanism (SPM) to support multi-DOF recovery through pitch, roll, and yaw motions. The system features a compact, ergonomic structure designed for comfort, safety, and compatibility with ankle biomechanics. A complete design-to-dynamics pipeline has been implemented, including structural design, kinematic modeling for motion planning, and Lagrangian-based dynamic modeling for torque estimation and simulation analysis. Preliminary simulations verify stable joint coordination and smooth motion tracking under representative rehabilitation trajectories. The control framework is currently being developed to enhance responsiveness across the workspace. Future work will focus on integrating personalized modeling and adaptive strategies to address kinematic singularities through model based control. This work establishes a foundational platform for intelligent, personalized ankle rehabilitation, enabling both static training and potential extension to gait-phase-timed assistance.

This paper describes a parallel-actuated robotic mechanism designed to provide two degrees-of-freedom (DOF) to the end-effector relative to a fixed base. In a potential application as a head-neck traction brace, these two independent DOFs are the vertical translation of the head with respect to shoulders and a specified orientation of the head in lateral bending. Motivated by recommended clinical methods to apply traction forces on the head, it is designed to provide vertical traction force on the head while tilted in a specific orientation. The design has four chains starting from a base stationed at the shoulders, each chain having 5 DOFs. Each chain imposes a single constraint on the motion of the end-effector. Together, four chains would apply four constraints, allowing only two DOFs of motion to the end-effector. Two out of four component chains are actively driven by linear actuators. Our kinematic studies show that the achievable workspace of this mechanism with a specific stroke length of actuators of +/- 50 mm results in 175-222 mm of vertical translation and up to +/- 9. of lateral bending. The lateral bending is coupled to the flexion/extension angle of the end-effector. A physical prototype was constructed to investigate the functional realization of the design in hardware. Overall, the physical prototype validated the motion of the theoretical model despite potential errors in the fabrication, making the design a candidate for potential head-neck traction application.

Abstract Objective This article introduces a dynamic neck brace to measure the full range of motion (RoM) of the head–neck. This easy-to-wear brace was used, along with surface electromyography (EMG), to study changes in movement characteristics after neck dissection (ND) in a clinical setting. Methods The brace was inspired by the head–neck anatomy and was designed based on the head–neck movement of 10 healthy individuals. A 6 degrees-of-freedom open-chain structure was adopted to allow full RoM of the head–neck with respect to the shoulders. The physical model was realized by 3D printed materials and inexpensive sensors. Five subjects, who underwent unilateral selective ND, were assessed preoperative and postoperative using this prototype during the head–neck motions. Concurrent EMG measurements of their sternocleidomastoid, splenius capitis, and trapezius muscles were made. Results Reduced RoM during lateral bending on both sides of the neck was observed after surgery, with a mean angle change of 8.03° on the dissected side (95% confidence intervals [CI], 3.11–12.94) and 9.29° on the nondissected side (95% CI, 4.88–13.69), where CI denotes the confidence interval. Axial rotation showed a reduction in the RoM by 5.37° (95% CI, 2.34–8.39) on the nondissection side. Neck extension showed a slight increase in the RoM by 3.15° (95% CI, 0.81–5.49) postoperatively. Conclusions This brace may serve as a simple but useful tool in the clinic to document head–neck RoM changes in patients undergoing ND. Such a characterization may help clinicians evaluate the surgical procedure and guide the recovery of patients.