IEEE ... International Conference on Rehabilitation Robotics : [proceedings]最新文献

While electrocutaneous stimulation provides a lowcost approach to sensory feedback, it produces signal artifact which corrupts sEMG and can create errors in sEMG motor control. Eight volunteers with intact limbs controlled a 5-degree-of-freedom virtual hand using surface electromyography. The hand was trained by mimicking flexion and extension of each degree-of-freedom five times, holding each position for three seconds. Participants received sensory feedback in the form of electrocutaneous stimulation: 100 microsecond pulses at 100 Hz with amplitude linearly proportional to the force experienced by the index finger while pressing a virtual block. We compared ability to discriminate between block compliance when using training data with and without stimulation artifact. Though not statistically significant, randomly occurring stimulation during training improved ability to discriminate more compliant objects from 76 to 82 % and 72 to 85 % correct for two proportional control algorithms-a modified Kalman filter and a convolutional neural network. This approach requires no extra computational resources while providing improved motor control for bidirectional prostheses.

This work explores the concept of movement coordination over time; smooth multi-joint gestures are a hallmark of healthy and normative body function. Deviations from normative movement coordination are linked to various health conditions, such as stroke or injury, and can lead to further complications if not addressed. Identifying abnormal movement and quantifying its degree is therefore crucial. Although some quantitative assessments exist, final evaluations often require expert input from occupational or physical therapists. We investigate the characterization of joint movements using motion capture as an objective indicator of synchronous behavior and propose a new quantitative metric to assess device and rehabilitation interventions, synchronicity. We apply our metric to three studies with various populations that compare normative hand use to movement with wrist braces, transradial prostheses, and a wrist exoskeleton, revealing that synchronicity may be an indicator of device embodiment and function.

Patellofemoral osteoarthritis is a prevalent musculoskeletal disorder characterized by knee pain during physically demanding activities like stair climbing and sit-to-stand transitions. These movements require high knee extension torques, leading to increased quadriceps activation and patellofemoral joint compression, which aggravates pain. While external torque assistance at the knee joint could theoretically reduce joint loads, traditional exoskeletons have not proven effective in managing osteoarthritis due to their rigid actuation, cumber-some attachments, and inadequate control systems. We address these limitations by modifying a commercial post-operative knee brace with a highly-backdrivable actuator and adapting a task-agnostic torque-assist controller, originally designed for lifting and carrying tasks, to accommodate osteoarthritis patients. In pilot trials with four participants with patellofemoral osteoarthritis, our device facilitated substantial reductions in both pain and perceived difficulty across daily activities including stair/ramp navigation, walking, and sit-to-stand transitions. Across all participants and tasks, pain and difficulty were reduced by 0.82 and 0.57 points, respectively (on a scale of 0 to 4). Electromyography revealed decreased quadriceps activation, varying by participant and task. These preliminary findings motivate future research on backdrivable knee exoskeletons as a novel conservative treatment for patellofemoral osteoarthritis.

While robots are becoming increasingly valuable tools in neurorehabilitation, our limited understanding of the brain's response during human-robot interaction tasks restricts advancements in training programs to restore neural pathways after injury. Co-contraction is characteristic of several neuromuscular disorders, such as stroke and cerebral palsy, and it is often targeted by assessments or rehabilitation programs. Despite its importance, the neural mechanisms underlying cocontraction remain poorly understood. To address this gap, this study investigates the neural substrates of muscle co-contraction via functional magnetic resonance imaging (fMRI) during a dynamic motor task with an MR-compatible wrist robot. To establish suitable fMRI experimental conditions, we first conducted a behavioral study assessing muscle activity during a wrist-pointing task with four participants. Participants reached toward a target while experiencing four main perturbation conditions (no force, divergent force, constant force up, and constant force down), designed to elicit distinct force and impedance responses. Following this behavioral validation, five additional participants performed the wrist-pointing task during fMRI. Our results suggest localization of force and impedance control within the cortico-thalamic-cerebellar network. These findings provide new insights into the neural mechanisms of co-contraction, supporting the development of neurorehabilitation paradigms.

Wearable robotic devices for rehabilitation have gained particular interest as they can improve the intensity and repeatability of rehabilitative treatments, leading to better rehabilitation outcomes. However, objective characterization of the effects of robotic interventions is still missing. Here we leverage on muscle synergies theory to provide a quantitative assessment of the effects of an upper limb exoskeleton on the organization of reaching movements in healthy individuals. For this, we computed muscle synergies from 20 subjects performing a standardized reaching task. We found that the robotic assistance does not disrupt the physiological structure of reaching movement but modulates it: a higher number of motor modules are necessary to explain the structure of reaching when using the exoskeleton. We derived an objective analysis of the implications of the more complex and fragmented movement strategy, which can potentially inform the design and testing of robotic prototypes, with the ultimate aim of improving technology-supported rehabilitation interventions.

This study evaluated if a lower-extremity exoskeleton affects error between marker-based ($M^{+}$) and markerless ($mathbf{M}^{boldsymbol{-}}$) motion capture in children with crouch gait. Two participants (P1: female, P2: male, ages 6 and 11 years old, cerebral palsy and spina bifida, both with assistive devices) walked with (Exo) and without (NoExo) an exoskeleton spanning the knee and ankle. Mean absolute difference (MAD) between $mathrm{M}^{+}$ and $mathrm{M}^{-}$ of gait cycle normalized hip, knee, and ankle angles were analyzed. Key outcomes of an ongoing clinical trial including measures of sagittal knee angle and gait speed were also compared. $mathrm{M}^{-}$ correlated with $mathrm{M}^{+}$ kinematics, but MAD ranged 1.57-17.0 deg. Except for sagittal (of P1) and frontal (of P2) hip angles, the Exo increased MAD (range 0.74-8.7 deg). $mathrm{M}^{-}$ underestimated knee flexion at initial ground contact, peak knee extension in stance, and peak knee extension in swing. No significant difference in MAD (Exo vs NoExo) was observed except for total joint excursion which had distinct trends for P 1 and P 2. For peak knee extension, a primary endpoint of crouch gait severity, the Exo increased MAD beyond minimum clinically meaningful differences (5 deg). Gait speed was consistent, with MAD $<0.03 ~mathrm{m} / mathrm{s}$ for all observations (NoExo vs Exo $mathbf{p = 0. 2 2}$). While $mathbf{M}^{-}$ has potential to ease gait assessment, early results warrant caution for use with wearable devices, highlighting the need for kinematic estimation algorithms to accommodate diverse end users in clinical and research settings.

In this study, we investigate muscle activation patterns and postural sway in older adults across different stance conditions with varying challenges. Muscle activity from lower limb and trunk muscles was recorded in seven older adults during four stance tasks: double stance, tandem stance, tandem stance with a cognitive task, and tandem stance with combined cognitive and motor tasks (TSCM). We analyzed CoP (centre of pressure) features, EMG (electromyography) patterns, and the coherence between EMG-CoP, to gain insights into neuromuscular coordination and balance control. Our findings indicate that task complexity significantly impacts postural stability, with tandem stances-particularly TSCM-leading to increased instability along the anterior-posterior axis. Key stabilizing muscles, such as the gastrocnemius medialis and tibialis anterior, showed heightened activation and strong EMGCoP coherence under complex tasks, highlighting their essential role in counteracting gravitational forces to maintain balance. A lateral asymmetry in muscle activation was also observed, with left-side muscles consistently showing greater activity than right-side counterparts, suggesting lateralized contributions to postural control. These results emphasize the importance of specific muscles for stability in challenging postural tasks and may offer valuable insights for designing targeted interventions, including robotic rehabilitation systems and assistive technologies, to enhance balance and reduce fall risk in older adults.

This paper addresses the challenge of designing human-like reference trajectories for exoskeleton-aided rehabilitation, with a focus on mimicking human joint coordination while addressing clinical requirements. Redundant kinematic chains in human biomechanics pose challenges to trajectory planning: state-of-the-art algorithms often do not explicitly address the problem of replicating natural movements nor do they provide a suitable performance over a wide range of human motions. To address this challenge, this paper proposes a geodesics-based computational method that incorporates joint-level constraints, in addition to energy and level of comfort criteria to solve the problem of redundancy and better emulate human movements. Using upper-limb data retrieved with an exoskeleton platform, the advanced method demonstrated significant performance gains over standard approaches like the minimum-jerk model and cubic polynomial planning, and leads to human-like trajectories, while closely aligning with human demonstrations, both at the configuration (joints) and task-space (hand) level. In particular, we provide detailed comparisons across various motion types and subjects, demonstrating the versatility of the proposed method and its strong potential for application in clinical and assisted living settings.

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.

During physical Human-Robot Interaction (pHRI) for limb mobilization, humans and robots may contribute simultaneously to motion. Some Assist-AsNeeded (AAN) strategies rely on models, while others are purely reactive. This paper presents a reactive AAN control for an end-effector robot in upper limb rehabilitation that weights commands dynamically based on local performance. Volunteers in tests followed a planar circular trajectory with visual feedback. Statistical analysis confirms that assistance is provided as needed, balancing performance across users and hands. Additionally, global metrics - including completion time, tracking errors, force, and disagreement - improve compared to standalone trajectories.