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

Brain plasticity plays a significant role in functional recovery after stroke, but the specific benefits of hand rehabilitation robot therapy remain unclear. Evaluating the specific effects of hand rehabilitation robot therapy is crucial in understanding how it impacts brain activity and its relationship to rehabilitation outcomes. This study aimed to investigate the brain activity pattern during hand rehabilitation exercise using functional magnetic resonance imaging (fMRI), and to compare it before and after 3-week hand rehabilitation robot training. To evaluate it, an fMRI experimental environment was constructed to facilitate the same hand posture used in rehabilitation robot therapy. Two stroke survivors participated and the conjunction analysis results from fMRI scans showed that patient 1 exhibited a significant improvement in activation profile after hand rehabilitation robot training, indicative of improved motor function in the bilateral motor cortex. However, activation profile of patient 2 exhibited a slight decrease, potentially due to habituation to the rehabilitation task. Clinical results supported these findings, with patient 1 experiencing a greater increase in FMA score than patient 2. These results suggest that hand rehabilitation robot therapy can induce different brain activity patterns in stroke survivors, which may be linked to patient-specific training outcomes. Further studies with larger sample sizes are necessary to confirm these findings.

The present study introduces a new gamified stepper device designed for bilateral lower limb rehabilitation, which is combined with a 3-D exergame. To the best of our knowledge, this is the initial study to utilize the stepping exercise for seated lower limb rehabilitation. The device comprises a stepping mechanism and a magnetic encoder. The modified stepper facilitates the bilateral training in the lower limb within its workspace. The magnetic encoder provides real-time rotational angle data during the exercise. A task-specific exergame platform was created and integrated with the device to enhance user compliance and engagement with the exercise. Experiments were conducted with ten healthy individuals with no history of lower limb injury to evaluate the system's feasibility for providing bilateral training and the effectiveness of the exergame platform. Participants were asked to perform bilateral lower limb exercise with a metronome and gamified stepper device in a seated position. Lower limb range of motion (ROM) and EMG activations were recorded during the exercises. The results indicate that the device was capable of providing cyclical ROM training with reduced muscle activation of the lower limb, and the exergame platform increased motivation to continue the exercises. This study can serve as the foundation for developing a robotic version of the proposed stepper device.

Assisting persons during physical therapy or augmenting their performance often requires precise delivery of an intervention. Robotic devices are perfectly placed to do so, but their intervention highly depends on the physical human-robot connection. The inherent compliance in the connection leads to delays and losses in bi-directional power transmission and can lead to human-robot joint axes misalignment. This is often neglected in the literature by assuming a rigid connection and has a negative impact on the intervention's effectiveness and robustness. This paper presents the preliminary results of a study that aims to close that gap. The study investigates what model forms and parameters best capture human-robot connection dynamics across different persons, connection designs (cuffs), and cuff strapping pressures. The results show that the linear spring-damper model is the best compromise, but its parameters must be adjusted for each individual and different conditions separately.

Back-support exoskeletons are commonly used in the workplace to reduce low back pain risk for workers performing demanding activities. However, for the assistance of tasks differing from lifting, back-support exoskeletons potential has not been exploited extensively. This work focuses on the use of an active back-support exoskeleton to assist carrying. A control strategy is designed that modulates the exoskeleton torques to comply with the task assistance requirements. In particular, two gait phase detection frameworks are exploited to adapt the exoskeleton assistance according to the legs' motion. The control strategy is assessed through an experimental analysis on ten subjects. Carrying task is performed without and with the exoskeleton assistance. Results prove the potential of the presented control in assisting the task without hindering the gait movement and improving the usability experienced by users. Moreover, the exoskeleton assistance significantly reduces the lumbar load associated with the task, demonstrating its promising use for risk mitigation in the workplace.

After experiencing brain damage, stroke patients commonly suffer from motor and sensory impairments that impact their ability to perform volitional movements. Visuo-proprioceptive integration is a critical component of voluntary movement, allowing for accurate movements and a sense of ownership over one's body. While recent studies have increased our understanding of the balance between visual compensation and proprioceptive deficits in stroke patients, quantitative methods for studying multisensory integration are still lacking. This study aimed to evaluate the feasibility of adapting a 3D visuo-proprioceptive disparity (VPD) task into a 2D setting using an upper-limb robotic platform for moderate to severe chronic stroke patients. To assess the implementation of the 2D task, a cohort of unimpaired healthy participants performed the VPD task in both a 3D and 2D environment. We used a computational Bayesian model to predict errors in visuo-proprioceptive integration and compared the model's predictions to real behavioral data. Our findings indicated that the behavioral trends observed in the 2D and 3D tasks were similar, and the model accurately predicted behavior. We then evaluated the feasibility of our task to assess post-stroke deficits in a patient with severe motor and sensory deficits. Ultimately, this work may help to improve our understanding of the significance of visuo-proprioceptive integration and aid in the development of better rehabilitation therapies for improving sensorimotor outcomes in stroke patients.

Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis. This could greatly benefit rehabilitation by enabling more accurate tracking of outcomes and providing more sensitive tools for research. There are numerous steps between obtaining videos to extracting accurate biomechanical results and limited research to guide many critical design decisions in these pipelines. In this work, we analyze several of these steps including the algorithm used to detect keypoints and the keypoint set, the approach to reconstructing trajectories for biomechanical inverse kinematics and optimizing the IK process. Several features we find important are: 1) using a recent algorithm trained on many datasets that produces a dense set of biomechanically-motivated keypoints, 2) using an implicit representation to reconstruct smooth, anatomically constrained marker trajectories for IK, 3) iteratively optimizing the biomechanical model to match the dense markers, 4) appropriate regularization of the IK process. Our pipeline makes it easy to obtain accurate biomechanical estimates of movement in a rehabilitation hospital.

Extensive research has established and widely acknowledged the important contribution of human hand sensory receptors in providing tactile feedback. The absence of these receptors results in a poor perception of the environment, impairing our efficient manipulation skills. Although the literature emphasizes the significance of normal forces in human grasping, further investigations should point toward the role of shear forces in this process. This paper presents an analysis of human everyday grasping activities through the use of 20 three-axis magnetic soft skin force sensors, in the form of rings and bands, that measure both normal and shear forces. Our study includes twelve tasks that cover various grasping requirements. Results highlight the importance of spatial information and the usefulness of shear forces in the prediction of unexpected changes that can not be always observed in normal forces. Tactile sensing can ultimately be integrated into prosthetic and rehabilitation devices for improved control and potentially provide sensory feedback to the user.

Evaluating progress throughout a patient's rehabilitation process helps choose effective treatment and formulate personalised and evidence-based rehabilitation interventions. The evaluation process is difficult due to the limitations of current clinical assessments. They lack the ability to reflect sensitive changes continuously throughout the rehabilitation process. Kinematic features have been extracted from individual's movement to address this problem due to their sensitivity and continuity. However, choosing appropriate kinematic features for rehabilitation evaluation has always been challenging. This paper exploits the application of kinematic features to classify movement patterns and movement qualities. 12 kinematic features were firstly extracted from a 7-segment triangle pattern of motion to monitor the learning progress with more numbers of drawing attempts. A statistical analysis was then conducted to compare the selected kinematic features with the clinically validated normalised jerk. Two supervised machine learning models were finally developed to classify movement patterns and movement qualities based on the selected kinematic features. The study was based on data recorded from 14 participants using a single position sensor. 6 kinematic features were able to reflect sensitive changes during the experiment and all kinematic features contributed to the classification tasks. Consistent with the literature, the results indicated that features based on movement velocity were the most beneficial in the classification tasks.

Robotic rehabilitation has emerged as a promising approach to enhance motor recovery after stroke, but there is limited knowledge about its efficacy in individuals who have experienced severe stroke. The study presented in this paper aims to analyze the effect of robotic therapy on the recovery of patients with severe stroke when combined with conventional rehabilitation therapies, and we want to observe whether there is a relationship between the clinical assessment provided by the therapist and the data recorded by the robotic device. Participants were divided into an experimental group and a control group, both receiving 15 sessions of conventional therapy in three consecutive weeks, but the experimental group underwent three out of five sessions per week with a robotic device. Both groups were evaluated using clinical scales, and in addition the experimental group was evaluated using an assessment game incorporated in the robotic device that provides session data such as the level of assistance needed by each user to complete the activity, or the score obtained in the game. These preliminary results showed that patients who received robot-assisted therapy had better motor function recovery compared to those who only received conventional therapy. In addition, it is also observed that the robot assistance needed by patients in the experimental group decreased as the sessions progressed, suggesting that robot-assisted therapy could be an effective tool for severe stroke patients.