Sunil K. Agrawal

Trunk impairment from neurological conditions such as spinal cord injury, cerebral palsy, and stroke limits postural coordination and functional independence. Sense of agency (SoA), the perceived control over one’s movements, influences motor learning and rehabilitation engagement, yet has not been examined in seated postural training. This study investigates how robotic pelvic guidance and visual feedback (VF) designs influence motor coordination and SoA during a seated reaching task in virtual reality (VR), validated here with able-bodied subjects. Thirty-two healthy adults were randomly assigned to four groups ( n = 8) in a 2 × 2 between-subjects design: pelvic guidance (guided vs unguided) and VF type (control VF, CVF: a pursuit-tracking moving target, vs error-based VF, EVF: displaying only the hand–target error). In the guided condition, the pelvic Wheelchair Robot for Active Postural Support (pWRAPS) actively guided pelvic orientation during a four-direction hand-tracking game. In the unguided condition, the robot operated in transparent mode. Participants receiving pelvic guidance showed significantly greater improvements in task performance and pelvis–trunk–hand coordination than unguided participants, regardless of VF type. EVF influenced trunk–pelvis coordination independently of guidance, with CVF yielding greater trunk–pelvis improvement. Trunk–pelvis coordination emerged as the strongest predictor of SoA, and SoA increased significantly at post-test, suggesting that perceived agency reflects coordination quality rather than the presence or absence of robotic assistance. Critically, guidance improved coordination without diminishing SoA, indicating compatible biomechanical and psychological rehabilitation goals. These findings support integrating robotic pelvic guidance with VR-based feedback for seated postural rehabilitation.

The knee joint plays a critical role in locomotion but is susceptible to overuse injuries, motivating the development of assistive exoskeletons. Current designs face a fundamental trade-off between achieving kinematic compatibility with the knee’s complex polycentric motion and providing effective variable-stiffness functionality for biomechanical support. This study presents a novel cable-driven multisegment exoskeleton to reconcile these competing requirements through an integrated biomimetic design. The proposed system employs redundant rotational joints and a linear guide rail to passively accommodate natural joint kinematics while enabling wide-range stiffness regulation (0–207 Nm/rad) via active cable length adjustment. This single-actuator approach achieves dynamic stiffness regulation, deterministic torque transmission with an effective moment arm exceeding 70 mm, and seamless state modulation within a low-profile structure (0.63 kg). Benchtop characterization confirmed precise stiffness control across the operational range (rmse ≤ 0.035 Nm/rad). Human subject experiments revealed significant muscular effort reduction during demanding tasks without compromising natural joint kinematics, including 23.9% decrease in peak vastus lateralis activation during incline walking and 29.2% reduction during squatting compared to unassisted conditions. These results validate the exoskeleton’s ability to reconcile anatomical compatibility with physiologically relevant stiffness regulation, representing a significant advance in knee assistive technology with broad applications in clinical rehabilitation and physical performance augmentation. This study bridges a critical gap in knee exoskeleton development, offering a unified solution for comfortable and effective assistance across dynamic tasks.

<sec> <title>BACKGROUND</title> With no cure currently available, lifestyle interventions aimed at slowing cognitive decline and delaying the onset of Alzheimer’s disease (AD) and related dementias are essential. However, existing cognitive training protocols often fall short in delaying dementia onset. We propose that interventions informed by the specific contributions of brain regions underlying cognition will yield greater efficacy. This pilot project focuses on spatial navigation (SN), as difficulties forming new and maintaining existing spatial memories represent a common and early symptom of AD, often leading to disorientation and loss of independence in daily life. Importantly, tau and amyloid-beta accumulation begins in brain regions critical for SN, including the medial temporal and posterior parietal cortices. Despite the relevance of SN impairments as an intervention target, few clinical trials have specifically addressed SN. Many existing protocols rely on desktop-based training, which deprives learners of movement-related sensory and kinematic information inherent to active navigation. Similarly, conventional neuroimaging methods require participants to remain immobile, limiting ecological validity. </sec> <sec> <title>OBJECTIVE</title> Our objective is to evaluate the feasibility of a fully immersive virtual-reality (VR) maze as a training paradigm, enabling participants to navigate mazes while walking in open space, thereby overcoming the aforementioned limitations. </sec> <sec> <title>METHODS</title> We employ Mobile Brain/Body Imaging (MoBI) to synchronously record whole-body movement and electroencephalography (EEG), enabling source-level analysis of brain activity during active navigation. The aims of this pilot project are to: (1) determine the feasibility of a VR-SN maze training protocol, and (2) explore training-induced neuroplasticity in older adults engaging in VR-SN exercises. We plan to enroll 25 older adults at risk of dementia, who will participate in sixteen 50-minute sessions over a 4-month period. VR mazes are designed to elicit distinct navigational strategies—allocentric and egocentric—during specific phases of maze learning (Stand/Encode vs. Walk/Navigate). Allocentric and egocentric strategies are known to rely primarily on the medial temporal and posterior parietal cortices, respectively. By integrating VR-SN maze training with source-localized, millisecond-precision brain measurements during active navigation, we will be able to identify, dissociate, and track brain dynamics underlying these strategies and assess their relationship with improvements in SN performance. The primary behavioral outcome will be navigation time. Neurophysiological outcome measures will include region-specific modulations in theta (3–7 Hz) and alpha (8–12 Hz) power. </sec> <sec> <title>RESULTS</title> Preliminary VR-MoBI findings indicate a significant increase in theta power as participants approached maze intersections, compared to a control condition in which navigation instructions were provided. </sec> <sec> <title>CONCLUSIONS</title> This pilot study will provide the foundation for a future randomized clinical trial designed to evaluate the efficacy of VR-SN training for improving navigational abilities and, ultimately, delaying cognitive decline in older adults at risk for dementia. </sec>

OBJECTIVE: Conventional gait assessments often fail to detect early, subclinical balance abnormalities that may become apparent under sensory conflict, such as visual perturbations. We investigated whether instrumented gait analysis combined with virtual reality (VR)-delivered visual perturbations can reveal task-dependent gait adaptations in healthy adults exposed to discordant sensory information. METHODS: Ten healthy adults completed a purpose-designed battery of 10 walking tasks spanning steady-state and balance-challenging conditions (e.g., tandem gait, obstacle negotiation, Timed Up and Go) on an instrumented walkway under four visual conditions: no virtual reality, unperturbed VR, optic flow-perturbed VR, and flickering light-perturbed VR. Spatiotemporal gait parameters were quantified to assess task- and condition-specific changes in dynamic gait control. RESULTS: Visual perturbations elicited task-dependent gait adaptations rather than uniform effects across the battery. Compared to unperturbed walking, optic flow was associated with selective increases in stride width during tasks involving rapid reorientation and rotational head movements, including pivot turning (0.10 vs. 0.06, p = 0.045) and horizontal head turns (0.12 vs. 0.08, p = 0.027). Temporal and spatial gait variability increased during tandem gait under optic flow (step time variability: 32.23 vs. 12.75, p = 0.036; stride length variability: 27.96 vs. 8.08, p = 0.041), reflecting impaired cycle-to-cycle consistency under heightened precision demands. CONCLUSION: VR-delivered visual perturbations revealed task-specific alterations in mediolateral stability, temporal consistency, and spatial gait scaling that were absent during unperturbed walking. A multi-task, perturbation-based assessment framework may therefore enhance sensitivity to subtle gait instability by exposing context-dependent limits of balance control.

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%BodyWeight (BW) -Low, 14 ± 4%BW -Mid, and 18 ± 4%BW -High) and directions (left, right, front, back). Trunk excursion, ground reaction forces (GRF), and electromyography (EMG) amplitude were assessed. Perturbation magnitude significantly affected trunk excursion and GRFs in all tested directions (p ≤ 0.002), with larger modulations elicited by higher perturbation magnitudes. Lower limb EMG amplitude responses were less consistent. In some instances, perturbation magnitude did not affect EMG amplitude. In others, high-magnitude perturbations promoted larger EMG amplitude modulation compared to weaker perturbations, resulting in higher or lower levels of activation depending on muscle and direction. These findings contribute to characterize the residual postural control potential of the human spinal circuitry below the level of SCI, which should be considered when defining postural training protocols with spinal cord neuromodulation.

Understanding how humans coordinate multiple joints and body segments during complex whole-body tasks is essential for designing movement-based interventions. Yoga-inspired standing maneuvers, which combine upper-body alignment with lunging or stepping, provide a structured yet challenging experimental testbed. However, few studies have integrated spatiotemporal coordination and compensation strategies across different proficiency levels, limiting their adoption in rehabilitation and robotic applications. In this study, we analyzed whole-body kinematics during yoga standing maneuvers performed by practitioners and non-practitioners. Three-dimensional joint angle trajectories and spinopelvic alignment were quantified across movement initiation, posture holding, and return stages. Principal component analysis and continuous relative phase analysis were employed to characterize how redundancy is organized and coordinated. Practitioners exhibited smaller excursions in sagittal and rotational spinopelvic alignment and showed greater contributions from proximal trunk-pelvis coordination. This organization was adaptively modulated with increasing task demands through flexible inter-segment coordination. In contrast, non-practitioners exhibited greater involvement of distal joints, particularly ankle joints, and employed compensatory joint angle trajectories. Multivariate analysis further revealed that while lumbar motion strongly contributes to sagittal spinopelvic alignment in practitioners and non-practitioners, thoracic contributions differed, reflecting distinct patterns to achieve alignment. These findings demonstrate how task complexity and motor proficiency shape whole-body coordination and postural control during standing maneuvers. The quantitative coordination and alignment benchmarks identified here provide a basis for movement assessment and the development of rehabilitation and robotic assistance applications, with relevance for mitigating maladaptive compensation.

During everyday walking, secondary tasks such as looking at a phone or reading billboards on the streets often require deliberate head reorientation and attentional allocation. However, the effect of these concurrent head-orientation demands on gait regulation and secondary task performance has not been systematically quantified. We addressed this gap using an immersive virtual reality (VR) framework in overground walking with distinct head-orientation tasks. Thirteen healthy adults performed overground walking in a one-to-one, scale-matched virtual replica of the laboratory while simultaneously performing head-orientation tasks presented in VR using a head-mounted display. Participants performed five motor-vestibular (MV) tasks involving tracking a visual target, i.e., a ball, positioned egocentrically relative to the body (Neutral, Flexion, Extension, and Rotation tasks) or allocentrically fixed to the virtual room. Participants also performed three motor-vestibular-cognitive (MVC) tasks that combined head-orientation with concurrent cognitive demands: Stroop test, Arithmetic task, and Trail Making Test. Among MV tasks, orientation of the head in Extension, Rotation, and Allocentric tasks reduced gait speed, step length and forward margin of stability while increasing motor dual-task cost compared with orientation of the head in Neutral and Flexion tasks. MVC tasks further elevated motor dual-task cost compared to MV tasks. While gait adaptations across the MVC tasks were similar, the Trail Making Test had a significantly higher cognitive dual-task cost. This study provides a novel VR paradigm for quantifying gait and dual-task costs under head-orientation constraints, with implications for developing more sensitive gait assessments and dual-task training in those with motor and/or cognitive impairments.

Along with motor dysfunction, people with Parkinson's Disease (PD) often develop cognitive dysfunction, linked to the gait abnormality - freezing of gait (FOG). Spatial navigation in Virtual Reality Floor Mazes (VR-FM) provides a unique framework for studying the effects of cognitive load on walking, with the ability to manipulate the complexity of the cognitive load. In addition, mazes include turns which simulate indoor home environments that people with PD frequently traverse in their daily life. This study is aimed to examine the effects of increasing cognitive load, applied with VR-FM, on motor performance in PD subjects with and without FOG. This is particularly important in understanding Parkinson's Disease, as cognitive decline is a strong contributor to morbidity and mortality as the disease progresses and may be a contributing factor to FOG. Fourteen subjects with PD, including eight who exhibited FOG, completed VR-FM under three conditions: 1) control mazes where the path to the goal is displayed; 2) easy mazes with two or less decision points; and 3) hard mazes, with more than two decision points. In comparison to non-freezers, freezers took fewer spin steps, shorter and slower strides, and reduced medial-lateral sway of the center of mass. These deficits became worse with maze difficulty, accompanied by further degradation in balance measured by margin of stability. Increased cognitive load imposed by the VR-FM led to gait deterioration and a prioritization for balance in both freezers and non-freezers. This supports the use of VR-FM as a tool to investigate motor-cognitive interplay in PD. Freezers exhibit more pronounced deterioration in gait and balance in VR-FM. Hence, VR-FM can serve as a potential tool to characterize and identify freezers.

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 the tremendous growth of medical Magnetic Resonance (MR) images, it becomes an essential requirement for an automated extraction of diagnostically salient parts of the images to refine the transmission and storage process. Research method: This paper proposes an automated unsupervised machine learning segmentation technique named fuzzy c-means clustering based on modified region growing algorithm (RG-FCM) for the extraction of tumors from MR brain images. The average intensity value of the foreground region extracted by the Otsu thresholding technique is selected as seed point of the region growing algorithm, and spatial constraints extracted from modified region growing technique are then incorporated into the objective function of Fuzzy C-Means clustering to improve cluster separation and refine centroid selection, particularly in images with uneven illumination. Results and Discussion: The empirical evaluation exhibits the superior performance of the presented technique, and performs better than Possibilistic Fuzzy C-Means (PFCM) and conventional FCM. The mathematical results evaluated on a dataset of 60 MR images indicate the improvement in Jaccard and dice indices by (3-5) and (6-8) % from PFCM and conventional FCM respectively. Therefore, this incorporation of the modified region growing technique into FCM can be employed for real time processing due to its less execution time and can be expanded for the computer-assisted identification of abnormal tissue proliferation in MRI brain images.

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.

Individuals with trunk impairments from neurological conditions often struggle with postural coordination, making it difficult to maintain upright sitting and limiting both mobility and rehabilitation. This study introduces a novel seated postural training approach using the pelvic Wheelchair Robot for Active Postural Support (pWRAPS), which delivers active pelvic guidance in a virtual reality (VR) environment to facilitate upper body coordination. We investigated how pelvic guidance, combined with different visual feedback (VF) strategies, influences upper-body coordination during a four-direction hand-tracking game. The goal of this paper is to validate our methods and outcomes in able-bodied subjects. Thirty-two healthy adults were randomly assigned to four groups (eight per group) based on guidance (guided vs. unguided) and visual feedback (control vs. error-based). In the guided condition, pWRAPS actively guided the pelvis during reaching, while in the unguided condition, the robot remained transparent. Control visual feedback showed a moving hand target, whereas error-based visual feedback displayed hand-tracking errors. Participants who received pelvic guidance showed significantly greater improvements in task performance and body segmental coordination than those without guidance, regardless of visual feedback. Error-based visual feedback further influenced trunk-pelvis coordination, regardless of guidance. Additionally, upper trunk coordination was significantly associated with participants' sense of agency (SoA), reflecting their perceived control over the movement. While varying visual feedback across sessions may enhance engagement, combining it with pelvic guidance is key to sustaining effective training outcomes. These findings highlight a promising approach for seated postural rehabilitation, by integrating pWRAPS with virtual reality.

BACKGROUND: Awareness of physical medicine and rehabilitation (PM&R) is limited among students, contributing to underrepresentation in the field. Underrepresentation can be tackled through early exposure, which has shown promise as early as high school in inspiring future physicians. OBJECTIVE: To develop a structured, multi-institutional half-day workshop to introduce high school students to PM&R, highlighting innovative technologies and approaches. DESIGN: The workshop involved collaboration between physicians, medical students, and premedical students from New York City. The curriculum included lectures on PM&R, discussion about virtual reality, and hands-on activities like ultrasound training, yoga, medical device design, and networking with doctors and students. SETTING: Multi-institutional. PARTICIPANTS: High school students. INTERVENTIONS: Half-day workshop. MAIN OUTCOME MEASURE: Baseline knowledge of PM&R, demographics, learning environment, and workshop learning objectives post workshop and at 3-month follow-up. RESULTS: Seventy-nine students attended the workshop and 69 students completed the preworkshop survey, with 65% self-identifying as female. Most participants (71%) belonged to an underrepresented racial minority group and/or were prospective first-generation medical students. None had prior experience shadowing or being mentored by a PM&R physician. Postworkshop results (n = 63) indicate a significant increase in the appreciation of virtual reality in medicine, yoga as rehab, the importance of continuity of care, and overall understanding of PM&R and the physiatrists' role (p < .05). Three-month follow-up (n = 23) demonstrates sustained outcomes and no significant differences in metrics such as interest in medicine and PM&R (p > .05). CONCLUSION: This workshop is the first to provide structured mentorship and lectures to high school students within PM&R, highlighting innovative technologies and approaches. The curriculum was well received by students and successfully promoted awareness and interest in PM&R, both short and long-term. The results could inspire other programs and specialties to adapt the workshop curriculum.

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 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 hard-ware 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.

<sec> <title>UNSTRUCTURED</title> 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. </sec>

Acute appendicitis is a common emergency condition requiring surgery, often diagnosed based on clinical symptoms. However, some cases present atypically, making imaging techniques crucial for accurate diagnosis. This study compared the diagnostic accuracy of ultrasound (US) and elastography (ES) in detecting acute appendicitis by correlating imaging findings with surgical outcomes. A total of 170 patients with suspected appendicitis underwent both US and ES before surgery. The results showed that ES had higher sensitivity and specificity than US alone, particularly in cases with subtle inflammation or tip appendicitis. ES effectively assessed the stiffness of the appendix, helping to distinguish between normal and inflamed tissue. The study concluded that combining ES with US improves diagnostic accuracy, reduces unnecessary surgeries, and aids in determining the severity of appendicitis, making it a valuable tool for clinical decision-making. Objective: The objective of the present study was to compare diagnostic accuracy of USG and ES in detection of acute appendicitis in correlation with operative findings. Materials and methods: A total of 170 patients who fulfilled the selection were enrolled in the study.

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.

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.

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.

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.

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.

Abstract Virtual reality (VR) is an innovative technology to use in Alzheimer’s clinical trials to improve cognitive and behavioral skills in older adults. However, the application of VR in older adults raises questions regarding their ability to use VR platforms and concerns regarding simulator sickness (i.e., dizziness). We compared the task completion time, accuracy, simulator sickness and acceptability of a virtual meal-preparing task between 8 young (M age 25, SD 2.6, M years of education 17.1 SD 1.6, 50% women) and 8 older adults at risk for dementia (M age 76.3, SD 7.9, M years of education 12.9 SD 8.3, 62.5% women, 62.5% Black or Hispanic, screened with AD8 dementia screening). Following a practice task, all participants completed the meal-preparing task developed using Unity 3D (V2022 LTS). Participants were seated, wore a headset, and used wireless hand controllers to navigate and perform the task. Results from two-sample t-test showed that i) older adults took a longer time to complete the task (75 Vs 235 seconds; p&amp;lt; 0.001) and ii) made more errors (p=0.04) compared to young adults but no participants reported simulator sickness (i.e., dizziness, headache, eyestrain, etc.). All participants reported high self-efficacy in performing similar virtual tasks. This pilot study shows that, even though older adults required more guidance during the practice task, they were still able to complete a simple virtual task within an average of &amp;lt; 5 minutes. Additionally, they reported high acceptability (i.e., self-efficacy, level of enjoyment) and willingness to engage in future VR interventions.

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.

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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">◦</sup> 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 Traction of the head-neck is important in the treatment of patients suffering from neck pain due to degeneration of the intervertebral discs. Conventional neck traction is provided manually by experienced physical therapists who maintain a desired orientation of the head-neck relative to the trunk while applying the traction. It is postulated that innovative designs of neck exoskeletons can provide the same function both flexibly and accurately. This article presents a novel architecture of a parallel mechanism whose base sits on the human shoulders with 4 parallel chains, each chain having a revolute-revolute-universal-revolute ( RRUR ) structure, while the end-effector is connected rigidly to the human head. Each chain has five degrees-of-freedom (DOF) and applies a constraint on the motion of the end-effector. As a result, this parallel mechanism allows two DOFs to the end-effector. These are (i) forward flexion or lateral bending of the head and (ii) vertical translation. An important motivation for the current design with RRUR structure is to characterize the range of forward flexion/lateral bending of the head-neck with this structure and the vertical translation to the end-effector. A physical prototype was constructed and tested to evaluate the performance of this mechanism in hardware for the proposed application.

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.

Individuals with neurological impairments and deficits in trunk control have difficulty maintaining and controlling upright postures during static or dynamic sitting. Passive devices may support the trunk in a wheelchair at the expense of the user's mobility, but opportunities for postural rehabilitation training may be compromised as a result. This paper presents novel seated postural training using a pelvic Wheelchair Robot for Active Postural Supports (pWRAPS) to improve upper body coordination. These tasks are performed within the pelvic range of motion (ROM) in a Virtual Reality (VR) environment. We investigated the effects of active pelvic guidance on upper body coordination during reaching tasks in different directions. Sixteen able-bodied young adult volunteers were assigned to two groups (eight per group). The first group completed the VR task in transparent mode, i.e., without pWRAPS actively guiding the pelvis. The second group completed the same task while their pelvis was actively guided by pWRAPS. The results support our hypothesis that the group with active pelvic guidance is able to adjust their pelvic trajectory closer to the assigned target than the control group without active pWRAPS. Moreover, after training, the group with active pelvic guidance exhibits better hand-tracking and pelvic accuracy than the control group. This indicates that the actively guided subjects simultaneously adjust their upper body and pelvis movements towards their targeted trajectories. This new training approach can potentially be adapted to postural rehabilitation for wheelchair users with limited trunk control.

(1) Background. High-level spinal cord injury (SCI) disrupts trunk control, leading to an impaired performance of upright postural tasks in sitting and standing. We previously showed that a novel robotic postural stand training with spinal cord epidural stimulation targeted at facilitating standing (Stand-scES) largely improved standing trunk control in individuals with high-level motor complete SCI. Here, we aimed at assessing the effects of robotic postural stand training with Stand-scES on sitting postural control in the same population. (2) Methods. Individuals with cervical (n = 5) or high-thoracic (n = 1) motor complete SCI underwent approximately 80 sessions (1 h/day; 5 days/week) of robotic postural stand training with Stand-scES, which was performed with free hands (i.e., without using handlebars) and included periods of standing with steady trunk control, self-initiated trunk and arm movements, and trunk perturbations. Sitting postural control was assessed on a standard therapy mat, with and without scES targeted at facilitating sitting (Sit-scES), before and after robotic postural stand training. Independent sit time and trunk center of mass (CM) displacement were assessed during a 5 min time window to evaluate steady sitting control. Self-initiated antero-posterior and medial-lateral trunk movements were also attempted from a sitting position, with the goal of covering the largest distance in the respective cardinal directions. Finally, the four Neuromuscular Recovery Scale items focused on sitting trunk control (Sit, Sit-up, Trunk extension in sitting, Reverse sit-up) were assessed. (3) Results. In summary, neither statistically significant differences nor large Effect Size were promoted by robotic postural stand training for the sitting outcomes considered for analysis. (4) Conclusions. The findings of the present study, together with previous observations, may suggest that robotic postural stand training with Stand-scES promoted trunk motor learning that was posture- and/or task-specific and, by itself, was not sufficient to significantly impact sitting postural control.

Extracting stride length and velocity from wearable sensors is traditionally based on the double integration of accelerometer data with zero-velocity update (ZUPT) technique. However, this approach might not be suitable for institutionalized older adults, whose clear zero-velocity phase cannot be detected accurately. While deep learning models have been proposed to overcome this limitation, these approaches need subject-specific labeled data, which are difficult to collect in practice, to calibrate the models. We show that 2-D deep convolutional neural networks (DCNNs) can be used to extract accurate estimates of stride length and velocity with instrumented footwear. Leave-one-subject-out cross-validation is used to avoid overfitting of the results to deep learning models on data collected from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}={95}$ </tex-math></inline-formula> institutionalized older adults with two different stride definitions during the 6-min walk test (6MWT). When stride is defined from the initial contact (IC) to the next IC, DCNNs result in better interrater reliability and improved performance by 38.8% and 31.2% relative to conventional techniques (i.e., the double integration method with ZUPT technique) for stride length and velocity, respectively. The performance of DCNNs does not degrade substantially when the stride is defined from the foot-flat (FF) phase (i.e., zero-velocity phase) to the next FF phase. Deep learning models are robust to intersubject variability without requiring subject-specific labeled data, indicating the potential of their use for out-of-the-lab gait analysis. Their performance is independent of stride definitions, making them more suitable for institutionalized older adults than conventional techniques, where ICs can be reliably detected even when they do not occur under the hindfoot.

Cervical traction is a common and effective treatment for degenerative disk diseases and pain in the cervical spine. However, the manual or mechanical methods of applying traction to the head-neck are limited due to variability in the applied forces and orientation of the head-neck relative to the shoulder during the procedure. Current robotic neck braces are not designed to provide independent rotation angles and independent vertical translation, or traction, to the brace end-effector connected to the head, making them unsuitable for traction application. This work proposes a novel architecture of a robotic neck brace, which can provide vertical traction to the head while keeping the head in a prescribed orientation, with flexion and lateral bending angles. In this paper, the kinematics of the end-effector attached to the head relative to a coordinate frame on the shoulders are described as well as the velocity kinematics and force control. The paper also describes benchtop experiments designed to validate the position control and the ability of the brace to provide a vertical traction force. It was shown that the maximum achievable end-effector orientations are 16° in flexion, 13.9° in extension, and ± 6.5° in lateral bending. The kinematic model of the active brace was validated using an independent motion capture system with a maximum root mean square error of 2.41°. In three different orientations of the end-effector, neutral, flexed, and laterally bent, the brace was able to provide a consistent upward traction force during intermittent force application. In these configurations, the force error has standard deviations of 0.55, 0.29, and 0.07N, respectively. This work validates the mechanism's ability to achieve a range of head orientations and provide consistent upward traction force in these orientations, making it a promising intervention tool in cases of cervical disk degeneration.

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 accessibilit

Virtual reality (VR) and augmented reality (AR) are emerging technologies in rehabilitation and have the potential to be combined with robot-assisted training (RAT). In this study, we investigate the effects of adding VR and AR into sitting posture training involving a robotic Trunk Support Trainer (TruST). Sixty-three healthy subjects were randomly assigned to three groups: physical reality group (PR group), AR group, and VR group. During training, subjects practice multidirectional reach tasks with robotic assistance provided by TruST. Reach targets were real in the PR group but virtual in the AR and VR groups. Training environments were real in the PR and AR groups, but virtual in the VR group. Before and after training, all subjects underwent a functional reach test to measure changes in motor performance, gains in workspace area and postural control flexibility. Additionally, they completed five standard questionnaires assessing presence, immersion, simulator sickness, engagement, and enjoyment. Our results indicate that both VR and AR significantly enhanced the effectiveness of TruST-assisted training. However, the VR group experienced a higher simulator sickness compared to the AR group. This comparative study sheds light on the added value of VR and AR in RAT and should serve as a stepping stone for the development of novel XR-enhanced RAT platforms and paradigms for training.

Activity-based training and lumbosacral spinal cord epidural stimulation (scES) have the potential to restore standing and walking with self-balance assistance after motor complete spinal cord injury (SCI). However, improvements in upright postural control have not previously been addressed in this population. Here, we implemented a novel robotic postural training with scES, performed with free hands, to restore upright postural control in individuals with chronic, cervical ( n = 5) or high-thoracic ( n = 1) motor complete SCI, who had previously undergone stand training with scES using a walker or a standing frame for self-balance assistance. Robotic postural training re-enabled and/or largely improved the participants' ability to control steady standing, self-initiated trunk movements and upper limb reaching movements while standing with free hands, receiving only external assistance for pelvic control. These improvements were associated with neuromuscular activation pattern adaptations above and below the lesion. These findings suggest that the human spinal cord below the level of injury can generate meaningful postural responses when its excitability is modulated by scES, and can learn to improve these responses. Upright postural control improvements can enhance functional motor recovery promoted by scES after severe SCI.

BACKGROUND: Transthyretin cardiac amyloidosis (ATTR cardiac amyloidosis) is caused by variant (ATTRv) or wild type (ATTRwt) transthyretin. While gait abnormalities have been studied in younger patients with ATTRv amyloidosis, research on gait in older adults with ATTR cardiac amyloidosis is lacking. Given ATTR cardiac amyloidosis' association with neuropathy and orthopedic manifestations, we explore the gait in this population. METHODS: Twenty-eight older male ATTR cardiac amyloidosis patients and 11 healthy older male controls walked overground with and without a dual cognitive task. Gait parameters: stride width, length, velocity and stance time percentage were measured using an instrumented mat. ATTR amyloidosis patients were further categorized based on clinical and functional assessments. RESULTS: We found significant gait differences between ATTR cardiac amyloidosis patients and healthy controls; patients had more variable, slower, narrower and shorter strides, with their feet spending more time in contact with the ground as opposed to in swing. However, the observed gait differences did not correlate with clinical and functional measures of ATTR cardiac amyloidosis severity. CONCLUSIONS: Our results suggest that gait analysis could be a complementary tool for characterizing ATTR cardiac amyloidosis patients and may inform clinical care as it relates to falls, management of anticoagulation, and functional independence.

There is a growing interest in the research and development of Cable Driven Rehabilitation Devices (CDRDs) due to multiple inherent features attractive to clinical applications, including low inertia, lightweight, high payload-to-weight ratio, large workspace, and modular design. However, previous CDRDs have mainly focused on modifying motor impairment in the sagittal plane, despite the fact that neurological disorders, such as stroke, often involve postural control and gait impairment in multiple planes. To address this gap, this work introduces a novel framework for designing a cable-driven lower limb rehabilitation exoskeleton which can assist with bi-planar impaired posture and gait. The framework used a lower limb model to analyze different cable routings inspired by human muscle architecture and attachment schemes to identify optimal routing and associated parameters. The selected cable routings were safeguarded for non-interference with the human body while generating bi-directional joint moments. The subsequent optimal cable routing model was then implemented in simulations of tracking reference healthy trajectory with bi-planar impaired gait (both in the sagittal and frontal planes). The results showed that controlling joints independently via cables yielded better performance compared to dependent control. Routing long cables through intermediate hinges reduced the peak tensions in the cables, however, at a cost of induced additional joint forces. Overall, this study provides a systematic and quantitative in silico approach, featured with accessible graphical user interface (GUI), for designing subject-specific, safe, and effective lower limb cable-driven exoskeletons for rehabilitation with options for multi-planar personalized impairment-specific features.