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

Powered exoskeletons for SCI patients are mainly limited by their inability to balance dynamically during walking. To investigate and understand the control strategies of human bipedal locomotion, we developed INSPIIRE, a passive exoskeleton. This device constrains the movements of able-bodied subjects to only hip and knee flexions and extensions, similar to most current active exoskeletons. In this paper, we detail the modular design and the mechanical implementation of the device. In preliminary experiments, we tested whether humans are able to handle dynamic walking without crutches, despite the limitation of lateral foot placement and locked ankles. Five healthy subjects showed the ability to stand and ambulate at an average speed of 1 m/s after 5 minutes of self-paced training. We found that while the hip abduction/adduction is constrained, the foot placement was made possible thanks to the pelvis yaw and residual flexibility of the exoskeleton segments in the lateral plan. This result points out that INSPIIRE is a reliable instrument to learn sagitally-constrained human locomotion, and the potential of investigating more dynamic walking, which is shown as possible in this implementation, even if only flexion/extension of the hip and knee are allowed.

Eye gaze tracking is increasingly popular due to improved technology and availability. However, in assistive device control, eye gaze tracking is often limited to discrete control inputs. In this paper, we present a method for collecting both reactionary and control eye gaze signals to build an individualized characterization for eye gaze interface use. Results from a study conducted with motor-impaired participants are presented, offering insights into maximizing the potential of eye gaze for assistive device control. These findings can inform the development of continuous control paradigms using eye gaze.

As the world ages, rehabilitation and assistive devices will play a key role in improving mobility. However, designing controllers for these devices presents several challenges, from varying degrees of impairment to unique adaptation strategies of users. To use computer simulation to address these challenges, simulating human motions is required. Recently, deep reinforcement learning (DRL) has been successfully applied to generate walking motions whose goal is to produce a stable human walking policy. However, from a rehabilitation perspective, it is more important to match the walking policy's ability to that of an impaired person with reduced ability. In this paper, we present the first attempt to investigate the correlation between DRL training parameters with the ability of the generated human walking policy to recover from perturbation. We show that the control policies can produce gait patterns resembling those of humans without perturbation and that varying perturbation parameters during training can create variation in the recovery ability of the human model. We also demonstrate that the control policy can produce similar behaviours when subjected to forces that users may experience while using a balance assistive device.

Post-stroke upper extremity function can be improved by devices that support shoulder abduction. However, many of these devices provide limited assistance in activities of daily living due to their complexity and encumbrance. We developed and evaluated a passive, lightweight (0.6 kg) wearable device consisting of an aluminum frame and elastic bands attached to a posture vest to aid in shoulder abduction. The number and thickness of bands can be adjusted to provide supportive forces to the affected arm. We measured reachable workspace area and Wolf Motor Function Test (WMFT) performance in people with a history of stroke (n = 11) with and without the wearable. The device increased workspace area in 6 participants and improved average WMFT functional and timing scores in 7 and 12 tasks, respectively, out of 16 total tasks. On average, participants increased their arm motion within 20 cm of shoulder level by 22.4% and decreased their hand's average distance from trunk by 15.2%, both improvements in the device case.

This paper addresses the problem of online and adaptive gait pattern generation for powered lower-limb exoskeletons (PLLEs), exploiting the motion of sensorized crutches. We conduct a series of experiments with subjects walking with and without crutches to investigate the synergies of walking between upper and lower body segments, by adopting principal component analysis (PCA), We also evaluate the effect of using crutches on the walking synergies, and we demonstrate that upper and lower limb walking synergies undergo slight changes in that case. However, the upper and lower limb synergies remain evident and can be exploited in order to use the motion of crutches as an input to PLLEs to identify a desired motion of the lower limb. We propose a method to use the results of synergy analysis to shape gait parameters in the real-time control of PLLEs. To evaluate the scalability of our approach for real-world applications, we conduct a number of experiments with subjects wearing a PLLE and using sensorized crutches to adaptively change the gait parameters of walking steps, depending on upper body actions.

This paper presents the design and validation of a proof-of-concept prototype for a wearable rehabilitation device to incorporate arm swing during gait rehabilitation. Unlike current stationary exoskeletons used for rehabilitation of upper limbs' function, assisting arm swing during gait requires inducing faster arm flexion/extension movements while maintaining the users' arms unconstrained in other directions. We developed a portable and underactuated system with features such as a large workspace and backdrivability to induce arm swing. Its wide workspace allowed the wearers to easily move their arms in different directions without any constraints. A modified double parallelogram linkage (mDPL) is proposed to allow the device to mimic the natural workspace of an arm. Additionally, a pulley drive and weight compensation system were created to place the motor on the users' back reducing the hindering weight of the actuators on their arms. Our experiments demonstrated this arm-swing rehabilitator could successfully induce arm movements at different arm configurations with low (0.67 Hz) and high (1.1 Hz) frequencies corresponding to slow and fast walking.

Accurate prediction of joint angle during walking from surface electromyography (sEMG) offers the potential to infer movement intention and therefore represents a potentially useful approach for adaptive control of wearable robotics. Here, we present the use of a recurrent neural network (RNN) with gated recurrent units (GRUs) and an attention mechanism to estimate knee angle during overground walking from sEMG and its initial offline validation in healthy adolescents. Our results show that the attention mechanism improved estimation accuracy by focusing on the most relevant parts of the input dataset within each time window, particularly muscles active during knee excursion. Sensitivity analysis revealed knee extensor and flexor muscles to be most salient in accurately estimating joint angle. Additionally, we demonstrate the ability of the GRU-RNN approach to accurately estimate knee angle during overground walking in a child with cerebral palsy (CP) in the presence of exoskeleton knee extension assistance. Collectively, our findings establish the initial feasibility of using this approach to estimate user movement from sEMG, which is particularly important for developing robotic exoskeletons for children with neuromuscular disorders such as CP.

Accurate assessment of hand dexterity plays a critical role in informing rehabilitation and care of upper-limb hemiparetic stroke patients. Common upper-limb assessments, such as the Box and Blocks Test and Nine Hole Peg Test, primarily evaluate gross motor function in terms of speed. These assessments neglect an individual's ability to finely regulate grip force, which is critical in activities of daily living, such as manipulating fragile objects. Here we present the Electronic Grip Gauge (EGG), an instrumented fragile object that assesses both gross and fine motor function. Embedded with a load cell, accelerometer, and Hall-effect sensor, the EGG measures grip force, acceleration, and relative position (via magnetic fields) in real time. The EGG can emit an audible "break" sound when the applied grip force exceeds a threshold. The number of breaks, transfer duration, and applied forces are automatically logged in real-time. Using the EGG, we evaluated sensorimotor function in implicit grasping and gentle grasping for the non-paretic and paretic hands of 3 hemiparetic stroke patients. For all participants, the paretic hand took longer to transfer the EGG during implicit grasping. For 2 of 3 participants, grip forces were significantly greater for the paretic hand during gentle grasping. Differences in implicit grasping forces were unique to each participant. This work constitutes an important step towards more widespread and quantitative measures of sensorimotor function, which may ultimately lead to improved personalized rehabilitation and better patient outcomes.

VR rehabilitation is an established field by now, however, it often refers to computer screen-based interactive rehabilitation activities. In recent years, there was an increased use of VR-headsets, which can provide an immersive virtual environment for real-world tasks, but they are lacking any physical interaction with the task objects and any proprioceptive feedback. Here, we focus on Embodied Virtual Reality (EVR), an emerging field where not only the visual input via VR-headset but also the haptic feedback is physically correct. This happens because subjects interact with physical objects that are veridically aligned in Virtual Reality. This technology lets us manipulate motor performance and motor learning through visual feedback perturbations. Bill-EVR is a framework that allows interventions in the performance of real-world tasks, such as playing pool billiard, engaging end-users in motivating life-like situations to trigger motor (re)learning - subjects see in VR and handle the real-world cue stick, the pool table and shoot physical balls. Specifically, we developed our platform to isolate and evaluate different mechanisms of motor learning to investigate its two main components, error-based and reward-based motor adaptation. This understanding can provide insights for improvements in neurorehabilitation: indeed, reward-based mechanisms are putatively impaired by degradation of the dopaminergic system, such as in Parkinson's disease, while error-based mechanisms are essential for recovering from stroke-induced movement errors. Due to its fully customisable features, our EVR framework can be used to facilitate the improvement of several conditions, providing a valid extension of VR-based implementations and constituting a motor learning tool that can be completely tailored to the individual needs of patients.

Essential Tremor (ET) is the most frequent movement disorder in adults. Upper-limb exoskeletons are a promising solution to alleviate ET symptoms. We propose a novel wrist exoskeleton for tremor damping. The TuMove exoskeleton is light-weight, portable, easy to use, and designed for ADLs and activities requiring hand dexterity. We validated the effectiveness of our exoskeleton by inducing forearm tremors using TENS on 5 healthy subjects. Our results show that wrist ranges are generally kept in most of the ROM needed in ADLs. The damping system reduced more than 30% of the tremor's angular velocity during drinking and pouring tasks. Furthermore, the completion time of the Archimedes spiral was decreased by 2.76 seconds (13.0%) and for the 9-Hole-Peg-Test by 2.77 seconds (11.8 %), indicating a performance improvement in dexterity tasks.