BACKGROUND: Restoring shoulder function is critical for upper-extremity rehabilitation following a stroke. The complex musculoskeletal anatomy of the shoulder presents a challenge for safely assisting elevation movements through robotic interventions. The level of shoulder elevation assistance in rehabilitation is often based on clinical judgment. There is no standardized method for deriving an optimal level of assistance, underscoring the importance of addressing abnormal movements during shoulder elevation, such as abnormal synergies and compensatory actions. This study aimed to investigate the effectiveness and safety of a newly developed shoulder elevation exoskeleton robot by applying a novel optimization technique derived from the muscle synergy index. METHODS: Twelve chronic stroke participants underwent an intervention consisting of 100 robot-assisted shoulder elevation exercises (10 × 10 times, approximately 40 min) for 10 days (4-5 times/week). The optimal robot assist rate was derived by detecting the change points using the co-contraction index, calculated from electromyogram (EMG) data obtained from the anterior deltoid and biceps brachii muscles during shoulder elevation at the initial evaluation. The primary outcomes were the Fugl-Meyer assessment-upper extremity (FMA-UE) shoulder/elbow/forearm score, kinematic outcomes (maximum angle of voluntary shoulder flexion and elbow flexion ratio during shoulder elevation), and shoulder pain outcomes (pain-free passive shoulder flexion range of motion [ROM] and visual analogue scale for pain severity during shoulder flexion). The effectiveness and safety of robotic therapy were examined using the Wilcoxon signed-rank sum test. RESULTS: All 12 patients completed the procedure without any adverse events. Two participants were excluded from the analysis because the EMG of the biceps brachii was not obtained. Ten participants (five men and five women; mean age: 57.0 [5.5] years; mean FMA-UE total score: 18.7 [10.5] points) showed significant improvement in the FMA-UE shoulder/elbow/forearm score, kinematic outcomes, and pain-free passive shoulder flexion ROM (P < 0.05). The shoulder pain outcomes remained unchanged or improved in all patients. CONCLUSIONS: The study presents a method for deriving the optimal robotic assist rate. Rehabilitation using a shoulder robot based on this derived optimal assist rate showed the possibility of safely improving the upper-extremity function in patients with severe stroke in the chronic phase.
Electromyography , Exoskeleton Device , Feasibility Studies , Muscle, Skeletal , Shoulder , Stroke Rehabilitation , Humans , Male , Female , Stroke Rehabilitation/methods , Middle Aged , Aged , Shoulder/physiopathology , Shoulder/physiology , Electromyography/methods , Muscle, Skeletal/physiopathology , Muscle, Skeletal/physiology , Range of Motion, Articular/physiology , Exercise Therapy/methods , Stroke/physiopathology , Robotics/methods , Biomechanical Phenomena/physiology , Adult
Background: : Walking disability caused by central nervous system injury often lingers. In the chronic phase, there is great need to improve walking speed and gait, even for patients who walk independently. Robot-assisted gait training (RAGT) has been widely used, but few studies have focused on improving gait patterns, and its effectiveness for motor function has been limited. This report describes the combination of "RAGT to learn the gait pattern" and "ankle robot training to improve motor function" in a patient with chronic stage brain injury. Case: : A 34-year-old woman suffered a traumatic brain injury 5 years ago. She had residual right hemiplegia [Fugl-Meyer Assessment-Lower Extremity (FMA-LE): 18 points] and mild sensory impairment, but she walked independently with a short leg brace and a cane. Her comfortable gait speed was 0.57 m/s without an orthosis, and her 6-m walk test distance was 240 m. The Gait Assessment and Intervention Tool (G.A.I.T.) score was 35 points. After hospitalization, ankle robot training was performed daily, with RAGT performed 10 times in total. Post-intervention evaluation performed on Day 28 showed: FMA-LE, 23 points; comfortable walking speed, 0.69 m/s; G.A.I.T., 27 points; and three-dimensional motion analysis showed ankle dorsiflexion improved from 3.22° to 12.59° and knee flexion improved from 1.75° to 16.54° in the swing phase. Discussion: : This is one of few studies to have examined the combination of two robots. Combining the features of each robot improved the gait pattern and motor function, even in the chronic phase.
This study introduces a body-weight-support (BWS) robot actuated by two pneumatic artificial muscles (PAMs). Conventional BWS devices typically use springs or a single actuator, whereas our robot has a split force-controlled BWS (SF-BWS), in which two force-controlled actuators independently support the left and right sides of the user's body. To reduce the experience of weight, vertical unweighting support forces are transferred directly to the user's left and right hips through a newly designed harness with an open space around the shoulder and upper chest area to allow freedom of movement. A motion capture evaluation with three healthy participants confirmed that the proposed harness does not impede upper-body motion during laterally identical force-controlled partial BWS walking, which is quantitatively similar to natural walking. To evaluate our SF-BWS robot, we performed a force-tracking and split-force control task using different simulated load weight setups (40, 50, and 60 kg masses). The split-force control task, providing independent force references to each PAM and conducted with a 60 kg mass and a test bench, demonstrates that our SF-BWS robot is capable of shifting human body weight in the mediolateral direction. The SF-BWS robot successfully controlled the two PAMs to generate the desired vertical support forces.
Genu recurvatum (knee hyperextension) is a common problem after stroke. It is important to promote the coordination between knee and ankle movements during gait; however, no study has investigated how multi-joint assistance affects genu recurvatum. We are developing a gait training technique that uses robotized knee-ankle-foot orthosis (KAFO) to assists the knee and ankle joints simultaneously. This report aimed to investigate the safety of robotized KAFO-assisted gait training (Experiment 1) and a clinical trial to treat genu recurvatum in a patient with stroke (Experiment 2). Six healthy participants and eight patients with chronic stroke participated in Experiment 1. They received robotized KAFO-assisted gait training for one or 10 sessions. One patient with chronic stroke participated in Experiment 2 to investigate the effect of robotized KAFO-assisted gait training on genu recurvatum. The patient received the training for 30 min/day for nine days. The robot consisted of KAFO and an attached actuator of four pneumatic artificial muscles. The assistance parameters were adjusted by therapists to prevent genu recurvatum during gait. In Experiment 2, we evaluated the knee joint angle during overground gait, Fugl-Meyer Assessment of lower extremity (FMA-LE), modified Ashworth scale (MAS), Gait Assessment and Intervention Tool (G.A.I.T.), 10-m gait speed test, and 6-min walk test (6MWT) before and after the intervention without the robot. All participants completed the training in both experiments safely. In Experiment 2, genu recurvatum, FMA-LE, MAS, G.A.I.T., and 6MWT improved after robotized KAFO-assisted gait training. The results indicated that the multi-joint assistance robot may be effective for genu recurvatum after stroke.
Improving human motor performance via physical guidance by an assist robot device is a major field of interest of the society in many different contexts, such as rehabilitation and sports training. In this study, we propose a Bayesian estimation method to predict whether motor performance of a user can be improved or not by the robot guidance from the user's initial skill level. We designed a robot-guided motor training procedure in which subjects were asked to generate a desired circular hand movement. We then evaluated the tracking error between the desired and actual subject's hand movement. Results showed that we were able to predict whether a novel user can reduce the tracking error after the robot-guided training from the user's initial movement performance by checking whether the initial error was larger than a certain threshold, where the threshold was derived by using the proposed Bayesian estimation method. Our proposed approach can potentially help users to decide if they should try a robot-guided training or not without conducting the time-consuming robot-guided movement training.
Pneumatically actuated muscles (PAMs) provide a low cost, lightweight, and high power-to-weight ratio solution for many robotic applications. In addition, the antagonist pair configuration for robotic arms make it open to biologically inspired control approaches. In spite of these advantages, they have not been widely adopted in human-in-the-loop control and learning applications. In this study, we propose a biologically inspired multimodal human-in-the-loop control system for driving a one degree-of-freedom robot, and realize the task of hammering a nail into a wood block under human control. We analyze the human sensorimotor learning in this system through a set of experiments, and show that effective autonomous hammering skill can be readily obtained through the developed human-robot interface. The results indicate that a human-in-the-loop learning setup with anthropomorphically valid multi-modal human-robot interface leads to fast learning, thus can be used to effectively derive autonomous robot skills for ballistic motor tasks that require modulation of impedance.
