Online Gait Research Articles

Adaptive Modular Neural Control for Online Gait Synchronization and Adaptation of an Assistive Lower-Limb Exoskeleton.

Gait synchronization has attracted significant attention in research on assistive lower-limb exoskeletons because it can circumvent conflicting movements and improve the assistance performance. This study proposes an adaptive modular neural control (AMNC) for online gait synchronization and the adaptation of a lower-limb exoskeleton. The AMNC comprises several distributed and interpretable neural modules that interact with each other to effectively exploit neural dynamics and adopt feedback signals to quickly reduce the tracking error, thereby smoothly synchronizing the exoskeleton movement with the user's movement on the fly. Taking state-of-the-art control as the benchmark, the proposed AMNC provides further improvements in the locomotion phase, frequency, and shape adaptation. Accordingly, under the physical interaction between the user and the exoskeleton, the control can reduce the optimized tracking error and unseen interaction torque by up to 80% and 30%, respectively. Accordingly, this study contributes to the advancement of exoskeleton and wearable robotics research in gait assistance for the next generation of personalized healthcare.

Free Gait Generation of Quadruped Robots via Impulse-Based Feasibility Analysis

Online gait adaptation is crucial for improving the dynamic performance of quadruped locomotion systems, while current approaches often adopt the predefined periodic gait pattern. This article proposes a free gait generation algorithm that only takes the robot state as input. Specifically, we introduce the feasible impulse polytope, which takes into account both linear and angular momentum impulses acting on the body in a prediction horizon. This naturally leads us to formulate a leg capability metric related to the effect of take-off and touch-down on the body motion. Then, gait sequence, take-off timing, and touch-down location can be automatically adjusted online as long as a metric threshold is given. To cope with rough terrain, a series of quadratic programming problems are established for online pose optimization under mild assumptions. Furthermore, we fully integrate the proposed algorithms with the robot control and estimation framework for real-time implementation. We first validate our approach with the quadruped robot SCIT-Dog through locomotion with speed changes, on stairs and slopes, and under lateral disturbances. Besides, a push-recovery experiment is established to show the superiority of the proposed approach over the baseline method with prespecified gait patterns. Experiment results verify the ability of the proposed algorithms to achieve autonomous gait transitions in response to various emergencies.

Online gait learning with Assist-As-Needed control strategy for post-stroke rehabilitation exoskeletons

AbstractLower limb exoskeletons (LLEs) have demonstrated their potential in delivering quantified repetitive gait training for individuals afflicted with gait impairments. A critical concern in robotic gait training pertains to fostering active patient engagement, and a viable solution entails harnessing the patient’s intrinsic effort to govern the control of LLEs. To address these challenges, this study presents an innovative online gait learning approach with an appropriate control strategy for rehabilitation exoskeletons based on dynamic movement primitives (DMP) and an Assist-As-Needed (AAN) control strategy, denoted as DMP-AAN. Specifically tailored for post-stroke patients, this approach aims to acquire the gait trajectory from the unaffected leg and subsequently generate the reference gait trajectory for the affected leg, leveraging the acquired model and the patient’s personal exertion. Compared to conventional AAN methodologies, the proposed DMP-AAN approach exhibits adaptability to diverse scenarios encompassing varying gait patterns. Experimental validation has been performed using the lower limb rehabilitation exoskeleton HemiGo. The findings highlight the ability to generate suitable control efforts for LLEs with reduced human-robot interactive force, thereby enabling highly patient-controlled gait training sessions to be achieved.

The flexible and distributed pressure sensor with 64 units for on-line gait recognition analysis

With the rapid development of flexible electronic technology, human gait recognition device is also developing in the direction of flexibility, refinement, and intelligence. In this paper, a gait recognition system with the advantages of being non-invasive and flexible is proposed and prepared to collect complex pressure information of human plantar and identify different gaits accurately. A flexible and distributed pressure sensor with a structure of eight rows and eight columns is designed and fabricated by the screen printing process and micro-nano manufacturing technology. The insole shape can be placed directly into a sneaker for easy wearing, while 64 pressure sensing units allow for more accurate detection of complex plantar pressure information. In addition, based on the “voltage mirror” principle and the feature threshold analysis method, a distributed pressure acquisition circuit and an online gait recognition software are developed respectively to realize time-sharing decoupling of complex output signals from multiple pressure sensing units and online real-time display of different gaits. Finally, Different human gait recognition experiments have also been carried out. The results show that the test error of the pressure sensing unit is better than 8.28%. The gait recognition system can realize the reliable and accurate online real-time identification of slow walking, fast walking, and different leaning directions, which shows great potential and value in the fields of medical diagnosis, health monitoring, gait correction and artificial intelligence.

Walking-Speed-Adaptive Gait Phase Estimation for Wearable Robots.

This paper introduces a Gait Phase Estimation Module (GPEM) and its real-time algorithm designed to estimate gait phases continuously and monotonically across a range of walking speeds and accelerations/decelerations. To address the challenges of real-world applications, we propose a speed-adaptive online gait phase estimation algorithm, which enables precise estimation of gait phases during both constant speed locomotion and dynamic speed changes. Experimental verification demonstrates that the proposed method offers smooth, continuous, and repetitive gait phase estimation when compared to conventional approaches such as the phase portrait method and time-based estimation. The proposed method achieved a 48% reduction in gait phase deviation compared to time-based estimation and a 48.29% reduction compared to the phase portrait method. The proposed algorithm is integrated within the GPEM, allowing for its versatile application in controlling gait assistive robots without incurring additional computational burden. The results of this study contribute to the development of robust and efficient gait phase estimation techniques for various robotic applications.

Gait analysis algorithm for lower limb rehabilitation robot applications

Abstract. When patients with lower limb dyskinesia use robots for rehabilitation training, gait parameters are of great significance for disease diagnosis and rehabilitation evaluation. Gait measurement is usually carried out by using optical motion capture systems, pressure plates and so on. However, it is difficult to apply these systems to lower limb rehabilitation robots due to their high price, limited scope and wearing requirements. At the same time, most of the current applications in robots focus on the basic gait parameters (such as step length and step speed) for robot control or user intention recognition. Therefore, this paper proposes an online gait analysis algorithm for lower limb rehabilitation robots, which uses a lidar sensor as the gait data acquisition sensor. The device is installed on the lower limb rehabilitation robot, which not only avoids the problems of decline in the detection accuracy and failure of leg tracking caused by lidar placement on the ground, but it also calculates seven gait parameters, such as step length, stride length, gait cycle and stance time, with high precision in real time. At the same time, the walking track of the patient may not be straight, and the lidar coordinate system is also changed due to the movement of the lower limb rehabilitation robot when the patient moves forward. In order to overcome this situation, a spatial parameter-splicing algorithm based on a time series is proposed to effectively reduce the error impact on gait spatiotemporal parameters. The experimental results show that the gait analysis algorithm proposed in this paper can measure the gait parameters effectively and accurately. Except for the swing time and double support time, which are calculated with large relative errors due to their small values, the relative errors of the remaining gait parameters are kept below 8 %, meeting the requirements of clinical applications.

Adaptive Symmetry Reference Trajectory Generation in Shared Autonomy for Active Knee Orthosis

Gait symmetry training plays an essential role in the rehabilitation of hemiplegic patients. Robotics-based gait training has been widely accepted by patients and clinicians. Reference trajectory generation for the affected side using the motion data of the unaffected side is an important way to achieve this. However, online generation of gait reference trajectory requires the algorithm to provide correct gait phase delay and could reduce the impact of measurement noise from sensors and input uncertainty from users. Based on an active knee orthosis (AKO) prototype, this work presents an adaptive symmetric gait trajectory generation framework for the gait rehabilitation of hemiplegic patients. Using the adaptive nonlinear frequency oscillators (ANFO) and movement primitives, we implement online gait pattern encoding and adaptive phase delay according to the real-time user input. A shared autonomy (SA) module with online input validation and arbitration has been designed to prevent undesired movements from being transmitted to the actuator on the affected side. The experimental results demonstrate the feasibility of the framework. Overall, this work suggests that the proposed method has the potential to perform gait symmetry rehabilitation in an unstructured environment and provide a kinematic reference for torque-assistance AKO.

Reliability and Validity of a Wearable Sensing System and Online Gait Analysis Report in Persons after Stroke.

The restoration of gait and mobility after stroke is an important and challenging therapy goal due to the complexity of the potentially impaired functions. As a result, precise and clinically feasible assessment methods are required for personalized gait rehabilitation after stroke. The aim of this study is to investigate the reliability and validity of a sensor-based gait analysis system in stroke survivors with different severities of gait deficits. For this purpose, 28 chronic stroke survivors (9 women, ages: 62.04 ± 11.68 years) with mild to moderate walking impairments performed a set of ambulatory assessments (3× 10MWT, 1× 6MWT per session) twice while being equipped with a sensor suit. The derived gait reports provided information about speed, step length, step width, swing and stance phases, as well as joint angles of the hip, knee, and ankle, which we analyzed for test-retest reliability and hypothesis testing. Further, test-retest reliability resulted in a mean ICC of 0.78 (range: 0.46-0.88) for walking 10 m and a mean ICC of 0.90 (range: 0.63-0.99) for walking 6 min. Additionally, all gait parameters showed moderate-to-strong correlations with clinical scales reflecting lower limb function. These results support the applicability of this sensor-based gait analysis system for individuals with stroke-related walking impairments.

Online gait generator for lower limb exoskeleton robots: Suitable for level ground, slopes, stairs, and obstacle avoidance

The development of lower limb exoskeletons has seen significant interest in recent times. Two types of them are more used that cover two types of needs: gait rehabilitation and human locomotion assistance. An essential subject in controlling the latter kind is trajectory generation, in which there are still challenges. For online controlling of the exoskeleton, gait parameters must have the ability to change at any moment during walking, taking into account human intention and particular conditions. In this paper, an online gait generation method is provided that is suitable for different walking modes. For this purpose, three trajectory generator blocks are proposed. The first block is for the center of mass (CoM) in the double support phase, where the trajectory is generated to make the patient feel more comfortable. The second block is for the support leg in the single support phase. The trajectory is generated using the center of pressure (CoP) criterion to secure backward balance and reduce the forces applied to the arms. The last block is for the swing leg in the single support phase, where a cost function is proposed to minimize the torques of the motors. The performance analysis of the proposed trajectory generator blocks was evaluated, and walking patterns were examined via simulations. Finally, three experimental tests were implemented with a healthy subject wearing Exoped® exoskeleton on level-ground with an obstacle, and stairs.

Online whole-stage gait planning method for biped robots based on improved Variable Spring-Loaded Inverted Pendulum with Finite-sized Foot (VSLIP-FF) model

Environmental adaptability and real-time control are significant to the actual application of biped robots. The current Spring-Loaded Inverted Pendulum (SLIP) walking exhibits the compliant interaction with environments. However, the movability and controllability of this model is limited owing to the lack of ankles. Moreover, complicated nonlinear optimization problems in gait generation bring difficulties to real-time control. To overcome these problems, this study proposes an online whole-stage gait planning method to enhance the bipedal walking performance. Firstly, considering the role of ankles, this study applies the proposed template model called Variable Spring-Loaded Inverted Pendulum with Finite-sized Foot (VSLIP-FF) model. Then a Finite State Machine (FSM)-based gait pattern including the corresponding bio-inspired gait strategies is established, which extends the single cyclic gait to the whole-stage gait. Secondly, to realize real-time gait planning, an online gait generator based on a neural network is applied to reduce the calculational burden. Finally, the method is applied on the simulation prototype and real robot platform for verification. Experimental results validate that the proposed method can achieve an autonomous gait with the online planning time of 0.01s, and the step length range is expanded by 37.52% compared with the traditional SLIP model.

Online Gait Phase Detection in Complex Environment Based on Distance and Multi-Sensors Information Fusion Using Inertial Measurement Units

Online Gait Phase Detection in Complex Environment Based on Distance and Multi-Sensors Information Fusion Using Inertial Measurement Units

Omnidirectional Walking Pattern Generator Combining Virtual Constraints and Preview Control for Humanoid Robots.

This paper presents a novel omnidirectional walking pattern generator for bipedal locomotion combining two structurally different approaches based on the virtual constraints and the preview control theories to generate a flexible gait that can be modified on-line. The proposed strategy synchronizes the displacement of the robot along the two planes of walking: the zero moment point based preview control is responsible for the lateral component of the gait, while the sagittal motion is generated by a more dynamical approach based on virtual constraints. The resulting algorithm is characterized by a low computational complexity and high flexibility, requisite for a successful deployment to humanoid robots operating in real world scenarios. This solution is motivated by observations in biomechanics showing how during a nominal gait the dynamic motion of the human walk is mainly generated along the sagittal plane. We describe the implementation of the algorithm and we detail the strategy chosen to enable omnidirectionality and on-line gait tuning. Finally, we validate our strategy through simulation experiments using the COMAN + platform, an adult size humanoid robot developed at Istituto Italiano di Tecnologia. Finally, the hybrid walking pattern generator is implemented on real hardware, demonstrating promising results: the WPG trajectories results in open-loop stable walking in the absence of external disturbances.

Straight Gait Research of a Small Electric Hexapod Robot

Gait is an important research content of hexapod robots. To better improve the motion performance of hexapod robots, many researchers have adopted some high-cost sensors or complex gait control algorithms. This paper studies the straight gait of a small electric hexapod robot with a low cost, which can be used in daily life. The strategy of “increasing duty factor” is put forward in the gait planning, which aims to reduce foot sliding and attitude fluctuation in robot motion. The straight gaits of the robot include tripod gait, quadrangular gait, and pentagonal gait, which can be described conveniently by discretization and a time sequence diagram. In order to facilitate the user to control the robot to achieve all kinds of motion, an online gait transformation algorithm based on the adjustment of foot positions is proposed. In addition, according to the feedback of the actual attitude information, a yaw angle correction algorithm based on kinematics analysis and PD controller is designed to reduce the motion error of the robot. The experiments show that the designed gait planning scheme and control algorithm are effective, and the robot can achieve the expected motion. The RMSE of the row, pitch, and yaw angle was reduced by 35%, 25%, and 12%, respectively, using the “increasing duty factor” strategy, and the yaw angle was limited in the range −3°~3° using the yaw angle correction algorithm. Finally, the comparison with related works and the limitations are discussed.

A comparison of neural networks algorithms for EEG and sEMG features based gait phases recognition

Surface electromyography (sEMG) and electroencephalogram (EEG) can be utilized to discriminate gait phases. However, the classification performance of various combination methods of the features extracted from sEMG and EEG channels for seven gait phase recognition has yet to be discussed. This study investigates the effectiveness of various dimensions of feature sets with different neural network algorithms in multiclass discrimination of gait phases. There are thirty-seven feature sets (slope sign change (SSC) of eight sEMG and twenty-one EEG channels, mean absolute value (MAV) of eight sEMG channels) and three classifiers (Linear Discriminant Analysis (LDA), K-nearest neighbor (KNN), Kernel Support Vector Machine (KSVM)) were utilized. The thirty-seven one-dimensional and six two-dimensional feature sets were applied to LDA and KNN, twenty-one-dimensional and thirty-seven-dimensional feature sets were applied to three optimized KSVM for gait phase recognition. We found that thirty-seven-dimensional feature sets with grid search KSVM achieved the highest classification accuracy (98.56 ± 1.34 %) and the time consumption was 26.37 s. The average time consumption of two-dimensional feature sets with KNN was the shortest (0.33 s). The SSC of sEMG with wider values distributions than others obtained a high performance. This indicates the wider the value distribution of features, the better accuracy of gait recognition. The findings suggest that a multi-dimensional feature set composed of EEG and sEMG features with KSVM achieved good performance. Considering execution time and recognition rate, two-dimensional feature sets with KNN are suitable for online gait recognition, thirty-seven-dimensional feature sets with KSVM are more likely to be used for off-line gait analysis.

A Gait Phase Detection Method in Complex Environment Based on DTW-Mean Templates

Gait phase detection is essential for the wearable powered exoskeletons. This paper proposes an online gait phase detection method based on dynamic time warping mean (DTW-MEAN) templates using ground contact forces (GCFs). There are two important methods of gait phase detection, which are the template matching and statistical method. Template matching methods such as DTW are robust and have been widely used in the field of gait phase detection. However, the template matching is a kind of method based on distance measure, and the gait phase detection will ignore the coupling connections between the gait sequences; on the contrary, the statistical method can fuse these connections well. In this paper, a statistical mean method based on DTW is proposed to solve the problem of recognizing phases between gait sequences that are not correctly detected by single template methods. We tested our approach in complex environment (such as different terrains, different payloads, and different speeds) and gained over 95% accuracy and time difference below 25ms. Our proposed approach obtained better detection results with the advantage of no need to retrain templates.

Lower Limb Exoskeleton Gait Planning Based on Crutch and Human-Machine Foot Combined Center of Pressure.

With the help of wearable robotics, the lower limb exoskeleton becomes a promising solution for spinal cord injury (SCI) patients to recover lower body locomotion ability. However, fewer exoskeleton gait planning methods can meet the needs of patient in real time, e.g., stride length or step width, etc., which may lead to human-machine incoordination, limit comfort, and increase the risk of falling. This work presents a human-exoskeleton-crutch system with the center of pressure (CoP)-based gait planning method to enable the balance control during the exoskeleton-assisted walking with crutches. The CoP generated by crutches and human-machine feet makes it possible to obtain the overall stability conditions of the system in the process of exoskeleton-assisted quasi-static walking, and therefore, to determine the next stride length and ensure the balance of the next step. Thus, the exoskeleton gait is planned with the guidance of stride length. It is worth emphasizing that the nominal reference gait is adopted as a reference to ensure that the trajectory of the swing ankle mimics the reference one well. This gait planning method enables the patient to adaptively interact with the exoskeleton gait. The online gait planning walking tests with five healthy volunteers proved the method’s feasibility. Experimental results indicate that the algorithm can deal with the sensed signals and plan the landing point of the swing leg to ensure balanced and smooth walking. The results suggest that the method is an effective means to improve human–machine interaction. Additionally, it is meaningful for the further training of independent walking stability control in exoskeletons for SCI patients with less assistance of crutches.

Online Gait Detection with an Automatic Mobile Trainer Inspired by Neuro-Developmental Treatment.

This paper demonstrates the development of an automatic mobile trainer employing inertial movement units (IMUs). The device is inspired by Neuro-Developmental Treatment (NDT), which is an effective rehabilitation method for stroke patients that promotes the relearning of motor skills by repeated training. However, traditional NDT training is very labor intensive and time consuming for therapists, thus, stroke patients usually cannot receive sufficient rehabilitation training. Therefore, we developed a mobile assisted device that can automatically repeat the therapists’ intervention and help increase patient training time. The proposed mobile trainer, which allows the users to move at their preferred speeds, consists of three systems: the gait detection system, the motor control system, and the movable mechanism. The gait detection system applies IMUs to detect the user’s gait events and triggers the motor control system accordingly. The motor control system receives the triggering signals and imitates the therapist’s intervention patterns by robust control. The movable mechanism integrates these first two systems to form a mobile gait-training device. Finally, we conducted preliminary tests and defined two performance indexes to evaluate the effectiveness of the proposed trainer. Based on the results, the mobile trainer is deemed successful at improving the testing subjects’ walking ability.

Online Gait Planning of Lower-Limb Exoskeleton Robot for Paraplegic Rehabilitation Considering Weight Transfer Process

People who suffer from paraplegia completely lose sensory and locomotor functions; there are no known treatment methods for their recovery at this time. Exoskeleton robots have the potential to dramatically improve the locomotor ability of these individuals. Although some exoskeleton robots for paraplegic patients have been commercialized and are able to restore walking motion at present, the pilot must acquire the ability to maintain their balance and shift their weight using forearm crutches, which is very challenging for paraplegics. To make this easier, we propose a new automated intelligent gait planning method that integrates a finite-state machine (FSM) model as an underlying foundation and a gait generation model in addition to the exoskeleton system. The underlying FSM model is defined using an inverted pendulum model and a minimum jerk algorithm. To compare the planning gait, 33 volunteers provide normal walking gaits; there are two more volunteers (paraplegic and nonparaplegic) wearing the Shenzhen Institute of Advanced Technology (SIAT) exoskeleton robot to validate the effects of the proposed gait and offer the groups of surface electromyogram (sEMG) data for analysis. As a result, the input of the proposed gait planning method is simplified to two parameters. The proposed walking gait significantly reduces the arm muscle output. Note to Practitioners-This article was motivated by the problem that the four-degree of freedom (DoF) underactuated paraplegic rehabilitation lower limb exoskeleton robot lacks of the center of gravity (COG) transfer process when coordinating with paraplegia patients during the training process for beginner. The existing approach to deal with this problem generally is to train the pilot for obtaining the COG transfer ability by using crutches. This article suggests a gait planning method for the four-DOF underactuated rehabilitation lower limb exoskeleton robot considering the COG transfer process to make the exoskeleton robot coordinate with a pilot and ensure safety. The gait planning method is based on the inverted pendulum model and simplified to several parameters. By adjusting these parameters, the step length, step height, walking speed, and the shape of gait can be adjusted according to the requirements of the exoskeleton robot and pilot. In this article, we mathematically characterize a gait planning method for the exoskeleton control strategy. Preliminary online experiments suggest that this approach is feasible and can significantly reduce the arm muscle output of pilot. In future research, we will adjust the gait by estimating the velocity of center of mass (COM) of the pilot to make the exoskeleton robot coordinate with pilot actively.

Online Gait Transitions and Disturbance Recovery for Legged Robots via the Feasible Impulse Set

Gaits in legged robots are often hand tuned and time based, either explicitly or through an internal clock, for instance, in the form of central pattern generators. This strategy requires trial and error to identify leg timings, which may not be suitable in challenging terrains. In this letter, we introduce new concepts to quantify leg capabilities for online gait emergence and adaptation, without fixed timings or predefined foothold sequences. Specifically, we introduce the Feasible Impulse Set, a notion that extends aspects of the classical wrench cone to include a prediction horizon into the future. By considering the impulses that can be delivered by the legs, quantified notions of leg utility are proposed for coordinating adaptive lift-off and touch-down of stance legs. The proposed methods provide push recovery and emergent gait transitions with speed. These advances are validated in experiments with the MIT Cheetah 3 robot, where the framework is shown to automatically coordinate aperiodic behaviors on a partially moving walkway.

A novel online gait optimization approach for biped robots with point-feet

Designing a stable walking gait for biped robots with point-feet is stated as a constrained nonlinear optimization problem which is normally solved by an offline numerical optimization method. On the result of an unknown modeling error or environment change, the designed gait may be ineffective and an online gait improvement is impossible after the optimization. In this paper, we apply Generalized Path Integral Stochastic Optimal Control to closed-loop model of planar biped robots with point-feet which leads to an online Reinforcement Learning algorithm to design the walking gait. We study the ability of the proposed method to adapt the controller of RABBIT, which is a planar biped robot with point-feet, for stable walking. The simulation results show that the method, starting a dynamically unstable initial gait, quickly compensates the modeling error and reaches to a walking with exponential stability and desired features in a new situation which was impossible by the past methods.