Robot-mediated overground gait training for transfemoral amputees with a powered bilateral hip orthosis: a pilot study

Background Transfemoral amputation is a serious intervention that alters the locomotion pattern, leading to secondary disorders and reduced quality of life. The outcomes of current gait rehabilitation for TFAs seem to be highly dependent on factors such as the duration and intensity of the treatment and the age or etiology of the patient. Although the use of robotic assistance for prosthetic gait rehabilitation has been limited, robotic technologies have demonstrated positive rehabilitative effects for other mobility disorders and may thus offer a promising solution for the restoration of healthy gait in TFAs. This study therefore explored the feasibility of using a bilateral powered hip orthosis (APO) to train the gait of community-ambulating TFAs and the effects on their walking abilities. Methods Seven participants (46–71 years old with different mobility levels) were included in the study and assigned to one of two groups (namely Symmetry and Speed groups) according to their prosthesis type, mobility level, and prior experience with the exoskeleton. Each participant engaged in a maximum of 12 sessions, divided into one Enrollment session, one Tuning session, two Assessment sessions (conducted before and after the training program), and eight Training sessions, each consisting of 20 minutes of robotically assisted overground walking combined with additional tasks. The two groups were assisted by different torque-phase profiles, aiming at improving symmetry for the Symmetry group and at maximizing the net power transferred by the APO for the Speed group. During the Assessment sessions, participants performed two 6-min walking tests (6mWTs), one with (Exo) and one without (NoExo) the exoskeleton, at either maximal (Symmetry group) or self-selected (Speed group) speed. Spatio-temporal gait parameters were recorded by commercial measurement equipment as well as by the APO sensors, and metabolic efficiency was estimated via the Cost of Transport (CoT). Additionally, kinetic and kinematic data were recorded before and after treatment in the NoExo condition. Results The one-month training protocol was found to be a feasible strategy to train TFAs, as all participants smoothly completed the clinical protocol with no relevant mechanical failures of the APO. The walking performance of participants improved after the training. During the 6mWT in NoExo, participants in the Symmetry and Speed groups respectively walked 17.4% and 11.7% farther and increased walking speed by 13.7% and 17.9%, with improved temporal and spatial symmetry for the former group and decreased energetic expenditure for the latter. Gait analysis showed that ankle power, step width, and hip kinematics were modified towards healthy reference levels in both groups. In the Exo condition metabolic efficiency was reduced by 3% for the Symmetry group and more than 20% for the Speed group. Conclusions This study presents the first pilot study to apply a wearable robotic orthosis (APO) to assist TFAs in an overground gait rehabilitation program. The proposed APO-assisted training program was demonstrated as a feasible strategy to train TFAs in a rehabilitation setting. Subjects improved their walking abilities, although further studies are required to evaluate the effectiveness of the APO compared to other gait interventions. Future protocols will include a lighter version of the APO along with optimized assistive strategies.

SA-SVM-Based Locomotion Pattern Recognition for Exoskeleton Robot

An exoskeleton robot is a kind of wearable mechanical instrument designed according to the shape and function of the human body. The main purpose of its design and manufacture is to enhance human strength, assist human walking and to help patients recover. The walking state of the exoskeleton robot should be highly consistent with the state of the human, so the accurate locomotion pattern recognition is the premise of the flexible control of the exoskeleton robot. In this paper, a simulated annealing (SA) algorithm-based support vector machine model is proposed for the recognition of different locomotion patterns. In order to improve the overall performance of the support vector machine (SVM), the simulated annealing algorithm is adopted to obtain the optimal parameters of support vector machine. The pressure signal measured by the force sensing resistors integrated on the sole of the shoe is fused with the position and pose information measured by the inertial measurement units attached to the thigh, shank and foot, which are used as the input information of the support vector machine. The max-relevance and min-redundancy algorithm was selected for feature extraction based on the window size of 300 ms and the sampling frequency of 100 Hz. Since the signals come from different types of sensors, normalization is required to scale the input signals to the interval (0,1). In order to prevent the classifier from overfitting, five layers of cross validation are used to train the support vector machine classifier. The support vector machine model was obtained offline in MATLAB. The finite state machine is used to limit the state transition and improve the recognition accuracy. Experiments on different locomotion patterns show that the accuracy of the algorithm is 97.47% ± 1.16%. The SA-SVM method can be extended to industrial robots and rehabilitation robots.

MOTION PLANNING OF ASSISTIVE EXOSKELETON IN HUMAN’S LOCOMOTION REHABILITATION

Researches on rehabilitation exoskeleton system have bee n implementing in recent decades and have been achieving many advantages. However, most of reseaches have focused on more simplified systems such as rehabilitation exoskeleton for one or two joints or for one paralysed leg with the purpose of recovering human’s locomotion pattern. Researches on rehabilitation exoskeleton using for whole two paralysed legs are limited because of the complexity of balance issue for a combined human – exoskeleton system. Therefore, crutches are used to prevent the human from falling during human’s walking in recent researches. In order to abandon the crutches and help to recover human’s nomal walking pattern, the balance problem of combined human-exoskeleton system must be considered in control algorithm. In this paper, we build a motion path for a combined human-exoskeleton ensuring that the combined human-exoskeleton system can move in a balanced area. Our proposed paths are validated in the control algorithm for the HUALEX exoskeleton system in University of Electronic Science and Technology of China (UESTC).

Development of Subcarangiform Bionic Robotic Fish Propelled by Shape Memory Alloy Actuators

In this paper, a shape memory alloy (SMA) actuated subcarangiform robotic fish has been demonstrated using a spring based propulsion mechanism. The bionic robotic fish developed using SMA spring actuators and light weight 3D printed components can be employed for under water applications. The proposed SMA spring-based design without conventional motor and other rotary actuators was able to achieve two-way shape memory effect and has reproduced the subcarangiform locomotion pattern. The positional kinematic model has been developed and the dynamics of the proposed mechanism were analysed and simulated using Automated Dynamic Analysis of Mechanical Systems (ADAMS). An open loop Arduino-relay based switching control has been adopted to control the periodic actuation of the SMA spring mechanism. The undulation of caudal fin in air and water medium has been analysed. The caudal fin and posterior body of the developed fish prototype have taken part in undulation resembling subcarangiform locomotion pattern and steady swimming was achieved in water with a forward velocity of 24.5 mm/s. The proposed design is scalable, light weight and cost effective which may be suitable for underwater surveillance application.

Adaptive Locomotion Control of a Hexapod Robot via Bio-Inspired Learning

In this paper, an adaptive locomotion control approach for a hexapod robot is proposed. Inspired from biological neuro control systems, a 3D two-layer artificial center pattern generator (CPG) network is adopted to generate the locomotion of the robot. The first layer of the CPG is responsible for generating several basic locomotion patterns and the functional configuration of this layer is determined through kinematics analysis. The second layer of the CPG controls the limb behavior of the robot to adapt to environment change in a specific locomotion pattern. To enable the adaptability of the limb behavior controller, a reinforcement learning (RL)-based approach is employed to tune the CPG parameters. Owing to symmetrical structure of the robot, only two parameters need to be learned iteratively. Thus, the proposed approach can be used in practice. Finally, both simulations and experiments are conducted to verify the effectiveness of the proposed control approach.

Target Constraints Influence Locomotion Pattern to the First Hurdle

The study examined to what extent the manipulation of hurdle height (0.76-m hurdle, low hurdle 0.50 m, and white stripe) would affect visual regulation strategies and kinematic reorganization when approaching the first hurdle. In addition, the impact of constraints as a training tool in terms of creating movement patterns functional for and representative of competitive movement models was assessed. The approach phase to the first hurdle of 13 physical education students with no previous experience in hurdling was video recorded and analyzed. Emergence of different footfall variability curves and movement coordination patterns suggests that participants interact differently with features of the performance context. Contrary to the white stripe, the hurdle height required participants to initiate regulation and distribute adjustments over a larger number of steps, and afforded the preparation for takeoff in order to clear the hurdle. In task design, manipulation of task constraints should offer valuable information regarding the dynamics of movement.

Increases in Load Carriage Magnitude and Forced Marching Change Lower-Extremity Coordination in Physically Active, Recruit-Aged Women

The objective was to examine the interactive effects of load magnitude and locomotion pattern on lower-extremity joint angles and intralimb coordination in recruit-aged women. Twelve women walked, ran, and forced marched at body weight and with loads of +25%, and +45% of body weight on an instrumented treadmill with infrared cameras. Joint angles were assessed in the sagittal plane. Intralimb coordination of the thigh–shank and shank–foot couple was assessed with continuous relative phase. Mean absolute relative phase (entire stride) and deviation phase (stance phase) were calculated from continuous relative phase. At heel strike, forced marching exhibited greater (P < .001) hip flexion, knee extension, and ankle plantar flexion compared with running. At mid-stance, knee flexion (P = .007) and ankle dorsiflexion (P = .04) increased with increased load magnitude for all locomotion patterns. Forced marching (P = .009) demonstrated a “stiff-legged” locomotion pattern compared with running, evidenced by the more in-phase mean absolute relative phase values. Running (P = .03) and walking (P = .003) had greater deviation phase than forced marching. Deviation phase increased for running (P = .03) and walking (P < .001) with increased load magnitude but not for forced marching. With loads of >25% of body weight, forced marching may increase risk of injury due to inhibited energy attenuation up the kinetic chain and lack of variability to disperse force across different supportive structures.

Novel Method for Analyzing Flexible Locomotion Patterns of Animals by Using Polar Histogram

In general, legged robots are designed to walk with a fixed rhythmic pattern. However, most animals can adapt their limb movements while walking. It is necessary to understand the mechanism of adaptability during locomotion when designing bio-inspired legged robots. In this paper, we propose an approach to analyze the flexible locomotion pattern of animals using a polar histogram. Field crickets were used to investigate variations in leg movement of insects depending on the environment. Crickets have a tripod gait; however, their leg movement changes depending on the texture of the ground. There was a significant difference between the leg movement when walking and when swimming. Our approach can explain how animals move their legs during locomotion. This study is useful for evaluating the movements of legged robots.

Locomotion generation for a mobile manipulator by global minimization of the weighted generalized momentum

This article presents a method for generating the locomotion of a mobile manipulator that globally minimizes the weighted generalized momentum. The method utilizes the calculus of variation setting to address the problem for which the optimal trajectory may be computed by solving the initial value problem of the system of ordinary differential equations rather than the two-point boundary value problem. Online optimal trajectory may then be input to a suitable tracking controller for controlling the robot in real time. Effectively, the robot closed-loop dynamics is shaped to the optimal system such that the locomotion minimizes the difference of the weighted generalized momentum and the assigned potential energy under the constraints imposed on by the tracking task, joint angle, and actuator torque limits. Desired locomotion behaviors may be achieved by properly adjusting the weighting, spring, and damping matrices. Exploiting the induced dynamical force from the cooperative motion of the constituent linkages through the momentum minimization basis, the robot is able to outperform conventional locomotion pattern actuated by the platform solely.