Design, Manufacturing, and Control of a Pneumatic-Driven Passive Robotic Gait Training System for Muscle-Weakness in a Lower Limb.

We designed and manufactured a pneumatic-driven robotic passive gait training system (PRPGTS), providing the functions of body-weight support, postural support, and gait orthosis for patients who suffer from weakened lower limbs. The PRPGTS was designed as a soft-joint gait training rehabilitation system. The soft joints provide passive safety for patients. The PRPGTS features three subsystems: a pneumatic body weight support system, a pneumatic postural support system, and a pneumatic gait orthosis system. The dynamic behavior of these three subsystems are all involved in the PRPGTS, causing an extremely complicated dynamic behavior; therefore, this paper applies five individual interval type-2 fuzzy sliding controllers (IT2FSC) to compensate for the system uncertainties and disturbances in the PRGTS. The IT2FSCs can provide accurate and correct positional trajectories under passive safety protection. The feasibility of weight reduction and gait training with the PRPGTS using the IT2FSCs is demonstrated with a healthy person, and the experimental results show that the PRPGTS is stable and provides a high-trajectory tracking performance.

Continuous Locomotion Mode and Task Identification for an Assistive Exoskeleton Based on Neuromuscular-Mechanical Fusion.

Human walking parameters exhibit significant variability depending on the terrain, speed, and load. Assistive exoskeletons currently focus on the recognition of locomotion terrain, ignoring the identification of locomotion tasks, which are also essential for control strategies. The aim of this study was to develop an interface for locomotion mode and task identification based on a neuromuscular-mechanical fusion algorithm. The modes of level and incline and tasks of speed and load were explored, and seven able-bodied participants were recruited. A continuous stream of assistive decisions supporting timely exoskeleton control was achieved according to the classification of locomotion. We investigated the optimal algorithm, feature set, window increment, window length, and robustness for precise identification and synchronization between exoskeleton assistive force and human limb movements (human-machine collaboration). The best recognition results were obtained when using a support vector machine, a root mean square/waveform length/acceleration feature set, a window length of 170, and a window increment of 20. The average identification accuracy reached 98.7% ± 1.3%. These results suggest that the surface electromyography-acceleration can be effectively used for locomotion mode and task identification. This study contributes to the development of locomotion mode and task recognition as well as exoskeleton control for seamless transitions.

Design and analysis of a lower limb assistive exoskeleton robot.

BACKGROUND: In recent years, exoskeleton robot technology has developed rapidly. Exoskeleton robots that can be worn on a human body and provide additional strength, speed or other abilities. Exoskeleton robots have a wide range of applications, such as medical rehabilitation, logistics and disaster relief and other fields. OBJECTIVE: The study goal is to propose a lower limb assistive exoskeleton robot to provide extra power for wearers. METHODS: The mechanical structure of the exoskeleton robot was designed by using bionics principle to imitate human body shape, so as to satisfy the coordination of man-machine movement and the comfort of wearing. Then a gait prediction method based on neural network was designed. In addition, a control strategy according to iterative learning control was designed. RESULTS: The experiment results showed that the proposed exoskeleton robot can produce effective assistance and reduce the wearer's muscle force output. CONCLUSION: A lower limb assistive exoskeleton robot was introduced in this paper. The kinematics model and dynamic model of the exoskeleton robot were established. Tracking effects of joint angle displacement and velocity were analyzed to verify feasibility of the control strategy. The learning error of joint angle can be improved with increase of the number of iterations. The error of trajectory tracking is acceptable.

Involvement of the Rostromedial Prefrontal Cortex in Human-Robot Interaction: fNIRS Evidence From a Robot-Assisted Motor Task.

Assistive exoskeleton robots are being widely applied in neurorehabilitation to improve upper-limb motor and somatosensory functions. During robot-assisted exercises, the central nervous system appears to highly attend to external information-processing (IP) to efficiently interact with robotic assistance. However, the neural mechanisms underlying this process remain unclear. The rostromedial prefrontal cortex (rmPFC) may be the core of the executive resource allocation that generates biases in the allocation of processing resources toward an external IP according to current behavioral demands. Here, we used functional near-infrared spectroscopy to investigate the cortical activation associated with executive resource allocation during a robot-assisted motor task. During data acquisition, participants performed a right-arm motor task using elbow flexion-extension movements in three different loading conditions: robotic assistive loading (ROB), resistive loading (RES), and non-loading (NON). Participants were asked to strive for kinematic consistency in their movements. A one-way repeated measures analysis of variance and general linear model-based methods were employed to examine task-related activity. We demonstrated that hemodynamic responses in the ventral and dorsal rmPFC were higher during ROB than during NON. Moreover, greater hemodynamic responses in the ventral rmPFC were observed during ROB than during RES. Increased activation in ventral and dorsal rmPFC subregions may be involved in the executive resource allocation that prioritizes external IP during human-robot interactions. In conclusion, these findings provide novel insights regarding the involvement of executive control during a robot-assisted motor task.

Kinematic effects of a passive lift assistive exoskeleton.

The VT-Lowe's exoskeleton was designed to help support the back during repetitive lifting tasks. This study focused on the kinematic differences between lifting with and without the exoskeleton (With-Exo and Without-Exo) over three different lifting styles (Freestyle, Squat, and Stoop) and two different box weights (0% and 20% of bodyweight). Twelve young and healthy males (Age 23.5 +/- 4.42 years; Height 179.33 +/- 6.37 cm; Weight 80.4 +/- 5.59 kg) participated in this study. Variables analyzed include the ankle and knee angles and angle between the Shoulder-Hip-Knee (SHK); the shoulder, elbow, and wrist heights; and the lifting speed and acceleration. The relationships between the torso angle, SHK angle, center of mass of the torso, torso torque, box height, as well as electromyography (EMG) data from a related study were also analyzed. On average, wearing the exoskeleton resulted in a 1.5 degree increase in ankle dorsiflexion, a 2.6 degree decrease in knee flexion, and a decrease of 2.3 degrees in SHK angle. Subjects' shoulder, elbow, and wrist heights were slightly higher while wearing the exoskeleton, and they lifted slightly more slowly while wearing the exoskeleton. Subjects moved more quickly while bending down as compared to standing up, and with the 0% bodyweight box as compared to the 20% bodyweight box. The values for Freestyle lifts generally fell in between Squat and Stoop lift styles or were not significantly different from Squat. EMG data from the leg muscles had relationships with torso torque while the back and stomach muscles showed no significant relationships.

A passive exoskeleton reduces peak and mean EMG during symmetric and asymmetric lifting.

The VT-Lowe's exoskeleton is a novel passive lift-assistive device designed to offload the back muscles during repetitive lifting. In this study, the effect of the exoskeleton on electromyographic (EMG) signals was investigated in four different lifting types (stoop, squat, freestyle and asymmetric) and two box weights (0% and 20% of body weight). Twelve young healthy adults ages 18-31 years (mean = 22.75, SD = 4.35) were participants. The EMG signals for twelve muscles (iliocostalis erector spinae (IL), longissimus erector spinae (LT), multifidus (MF), bicep femoris (BF), vastus lateralis (VL) and abdominal external oblique (AEO) muscles) were measured. The exoskeleton significantly decreased the peak and mean activity of back muscles (IL and LT) by 31.5% and 29.3%, respectively, for symmetric lifts and by 28.2% and 29.5%, respectively, for asymmetric lifts. The peak and mean EMG of leg muscles were significantly reduced by 19.1% and 14.1% during symmetric lifts, and 17.4% and 14.6% during asymmetric lifts. Although the exoskeleton reduced the activation of back and leg muscles, it slightly increased the activity of external oblique muscles, although this was not statistically significant. In conclusion, the exoskeleton is promising as a lift-assist device for manual material handlers and workers performing repetitive lifting.

Switching Assistance for Exoskeletons During Cyclic Motions.

This paper proposes a novel control algorithm for torque-controlled exoskeletons assisting cyclic movements. The control strategy is based on the injection of energy parcels into the human-robot system with a timing that minimizes perturbations, i.e., when the angular momentum is maximum. Electromyographic activity of main flexor-extensor knee muscles showed that the proposed controller mostly favors extensor muscles during extension, with a statistically significant reduction in muscular activity in the range of 10-20% in 60 out of 72 trials (i.e., 83%), while no effect related to swinging speed was recorded (speed variation was lower than 10% in 92% of the trials). In the remaining cases muscular activity increment, when statistically significant, was less than 10%. These results showed that the proposed algorithm reduced muscular effort during the most energetically demanding part of the movement (the extension of the knee against gravity) without perturbing the spatio-temporal characteristics of the task and making it particularly suitable for application in exoskeleton-assisted cyclic motions.

Decoding the ERD/ERS: influence of afferent input induced by a leg assistive robot.

This paper investigates the influence of the leg afferent input, induced by a leg assistive robot, on the decoding performance of a BMI system. Specifically, it focuses on a decoder based on the event-related (de)synchronization (ERD/ERS) of the sensorimotor area. The EEG experiment, performed with healthy subjects, is structured as a 3 × 2 factorial design, consisting of two factors: "finger tapping task" and "leg condition." The former is divided into three levels (BMI classes), being left hand finger tapping, right hand finger tapping and no movement (Idle); while the latter is composed by two levels: leg perturbed (Pert) and leg not perturbed (NoPert). Specifically, the subjects' leg was periodically perturbed by an assistive robot in 5 out of 10 sessions of the experiment and not moved in the remaining sessions. The aim of this study is to verify that the decoding performance of the finger tapping task is comparable between the two conditions NoPert and Pert. Accordingly, a classifier is trained to output the class of the finger tapping, given as input the features associated with the ERD/ERS. Individually for each subject, the decoding performance is statistically compared between the NoPert and Pert conditions. Results show that the decoding performance is notably above chance, for all the subjects, under both conditions. Moreover, the statistical comparison do not highlight a significant difference between NoPert and Pert in any subject, which is confirmed by feature visualization.