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

Electromyographic controls based on machine learning rely on the stability and repeatability of signals related to muscular activity. However, such algorithms are prone to several issues, making them non-viable in certain applications with low tolerances for delays and signal instability, such as exoskeleton control or teleimpedance. These issues can become dramatic whenever, e.g., muscular activity is present not only when the user is trying to move but also for mere gravity compensation, which generally becomes more prominent the more proximal a muscle is. A substantial part of this instability is attributed to electromyography's inherent heteroscedasticity. In this study, we introduce and characterize an adaptive filter for sEMG features in such applications, which automatically adjusts its own cutoff frequency to suit the current movement intention. The adaptive filter is tested offline and online on a regression-based joint torque predictor. Both the offline and the online test show that the adaptive filter leads to more accurate prediction in terms of root mean square error when compared to the unfiltered prediction and higher responsiveness of the signal in terms of lag when compared to the output of a conventional low-pass filter.

This paper presents an experimental comparison of multiple admittance control dynamic models implemented on a five-degree-of-freedom arm exoskeleton. The performance of each model is evaluated for transparency, stability, and impact on point-to-point reaching. Although ideally admittance control would render a completely transparent environment for physical human-robot interaction (pHRI), in practice, there are trade-offs between transparency and stability-both of which can detrimentally impact natural arm movements. Here we test four admittance modes: 1) Low-Mass: low inertia with zero damping; 2) High-Mass: high inertia with zero damping; 3) Velocity-Damping: low inertia with damping; and 4) a novel Velocity-Error-Damping: low inertia with damping based on velocity error. A single subject completed two experiments: 1) 20 repetitions of a single reach-and-return, and 2) two repetitions of reach-and-return to 13 different targets. The results suggest that the proposed novel Velocity-Error-Damping model improves transparency the most, achieving a 70% average reduction of vibration power vs. Low-Mass, while also reducing user effort, with less impact on spatial/temporal accuracy than alternate modes. Results also indicate that different models have unique situational advantages so selecting between them may depend on the goals of the specific task (i.e., assessment, therapy, etc.). Future work should investigate merging approaches or transitioning between them in real-time.

Monitoring the human-exoskeleton interface (HEI) is vital for user safety in assistive exoskeletons. Considering interaction forces during design can improve comfort and efficiency and reduce resistance and inertia. Challenges include covering the lower limb area without interfering with user-robot interaction. This paper presents a force-sensitive resistor (FSR) based sensing sleeve for use within the HEI. The design includes 30 sensors and works independently of it to assess attachment modalities. System characterisation tests the system with human trials. Demonstrating that a low-cost, flexible sensing array can accurately monitor HEI. This provides a promising tool for assessing human-robot interaction and investigating wearable robotic device use.

In the field of gait rehabilitation lower limb exoskeletons have received a lot of interest. An increasing number of them are revised to be adapted for post-stroke rehabilitation. These exoskeletons mostly work in complement of conventional physiotherapy in the subacute phase to practice gait training. For this gait training the reference trajectory generation is one of the main issues. This is why it usually consists in reproducing some averaged healthy patient's gait pattern. This paper's purpose is to display the online trajectory generation (OTG) algorithm developed to provide reference trajectories applied to gait-oriented tasks designed based on conventional physiotherapy. This OTG algorithm is made to reproduce trajectories similar to the ones a therapist would follow during the same tasks. In addition, experiments are presented in this paper to compare the trajectories generated with the OTG algorithm for two rehabilitation tasks with the trajectories followed by a therapist in the same conditions. During these experiments the OTG is implemented in a runtime system with a 500µs cycle time on a bench able to emulate late and early patients' interaction. These experiments results assess that the OTG can work at a 500µs cycle time to reproduce a similar trajectory as the one followed by the therapist during the two rehabilitation tasks implemented.

This paper presents a novel impedance controller for THINGER (THumb INdividuating Grasp Exercise Robot), a 2-degree-of-freedom (DOF) spherical 5-bar exoskeleton designed to augment FINGER (Finger INdividuating Grasp Exercise Robot). Many rehabilitation and assessment tasks, for which THINGER is designed, are improved by rendering near-zero impedance during physical human-robot interaction (pHRI). To achieve this goal, the presented impedance controller includes several novel features. First, a reference trajectory is omitted, allowing free movements. Second, force-feedback gains are reduced near actuator limits and a saturation function limits the maximum commanded force; both allow more responsive (higher) force-feedback gains within the workspace and mitigate transient oscillations caused by external disturbances. Finally, manipulability-based directional force-feedback gains help improve rendered impedance isotropy. Validation experiments included free exploration of the workspace, following a prescribed circular thumb motion, and intentional exposure to external disturbances. The experimental results show that the presented impedance controller significantly reduces impedance to subject-initiated motion and accurately renders the desired isotropic low-impedance environment.

Hybrid exoskeleton, comprising an exoskeleton interfaced with functional electrical stimulation (FES) technique, is conceptualized to complement the weakness of each other in automated neuro-rehabilitation of sensory-motor deficits. The externally actuating exoskeleton cannot directly influence neurophysiology of the patients, while FES is difficult to use in functional or goal-oriented tasks. The latter challenge is largely inherited from the fact that the dynamics of the muscular response to FES is complex, and it is highly user- and state-dependent. Due to the retardation of the muscular contraction response to the FES profile, furthermore, a commonly used model-free control scheme, such as PID control, suffers performance. The challenge in FES control is exacerbated especially in the presence of the actuation redundancy between the volitional activity of the user, powered exoskeleton, and FES-induced muscle contractions. This study therefore presents trajectory tracking performance of the hybrid exoskeleton in a novel model-based hybrid exoskeleton scheme which entices user-specific FES model-predictive control.

Back support soft exosuits are promising solutions to reduce risk of musculoskeletal injuries at workplaces resulting from physically demanding and repetitive lifting tasks. Design of novel active exosuits address the impact on the muscle activity and metabolic costs but do not consider other critical aspects such as comfort and user perception during the intended tasks. Thus, in this study, we describe a novel soft active exosuit in line with its impact on physiological and subjective measures during lifting. We tested four healthy participants who performed repetitive lifting tasks with and without this exosuit. The exosuit provided assistance proportional to the lumbar flexion angle measured using an inertial measurement unit. We measured the participant's multimodal physiological measures including surface electromyography, metabolic cost, heart rate, and skin temperature. We also measured subjective scores on user exertion, task load, and device acceptability. All participants perceived a reduction in task load when using the exosuit. Three participants showed reduction of muscle activity for the erector spinae muscles. The metabolic costs and heart rate reserve reduced for two participants, with similar trends for skin temperature. For future development of workplace exosuits, we recommend incorporating assessments of both physiological and subjective measures, considering the user-dependent response to the exosuit.

Accurate gait phase estimation algorithms can be used to synchronize the action of wearable robots to the volitional user movements in real time. Current-day gait phase estimation methods are designed mostly for rhythmic tasks and evaluated in highly controlled walking environments (namely, steady-state walking). Here, we implemented adaptive Dynamic Movement Primitives (aDMP) for continuous real-time phase estimation in the most common locomotion activities of daily living, which are level-ground walking, stair negotiation, and ramp negotiation. The proposed method uses the thigh roll angle and foot-contact information and was tested in real time with five subjects. The estimated phase resulted in an average root-mean-square error of 3.98% ± 1.33% and a final estimation error of 0.60% ± 0.55% with respect to the linear phase. The results of this study constitute a viable groundwork for future phase-based control strategies for lower-limb wearable robots, such as robotic prostheses or exoskeletons.

Recent human-centred design studies suggest that acoustic noise could affect the physical use and psychological acceptance of a biomedical device. These aspects are especially relevant in the prosthetic field, in which device loudness is often related to rejection. The aim of the study is to inquire on the possibility to reduce the acoustic noise emitted by a robotic leg prosthesis by improving its casing. First, acoustic noise emissions are characterized experimentally using an anechoic chamber, both for the whole prosthesis, and for its actuator (i.e., noise source) in isolation. The characterizations show that the whole prosthesis including its casing amplify the actuator noise, and that noise emissions are concentrated within a certain frequency range. Based on these findings, the prosthesis casing has been redesigned to include a panel of Helmholtz resonator-based acoustic metamaterials as proof of concept, which attenuate respective noise emissions. Experimental validations show that the use of such metamaterials in the prosthesis casing can significantly reduce noise emissions without compromising on prosthesis size and weight.

Immobilization due to various reasons can lead to disuse muscle atrophy. If prolonged, the circumstance is exacerbated and may lead to joint contracture, dysfunction, and long-term sequela. Thus, a balanced exercise regimen is crucial. While able-bodied individuals can perform a variety of exercises, bedridden patients typically resort to exercising primarily with bicycle ergometers. However, since the pedaling trajectory with ergometers is confined to the sagittal plane, muscles responsible for medial-lateral movement and balance are not effectively trained. Furthermore, the direction of joint reaction forces, which is crucial for specific patients with ligament injuries, recurrent dislocations, and medial osteoarthritis, is not well facilitated. Thus, it would be beneficial for patients without full body weight support ability to train ab-/ad-ductor muscles by altering the direction of extrinsic load via ergometers. In this study, we present a novel Tilted-Plane Ergometer and proof-of-concept experiment with one healthy subject. The results suggest that subtle changes in ergometer configurations lead to different movements, joint alignments, and muscle recruitment patterns.