Robotic lower limb exoskeletons using proportional myoelectric control

For robotic ankle exoskeletons, the choice of controller and the many associated controller parameters (e.g. magnitude or timing of assistance) directly affect the user's performance, comfort, and biomechanics. Some studies have proposed tuning such controllers for individual users using optimization techniques to minimize (or maximize) a physiological objective, such as metabolic energy expenditure. One drawback to this approach is that it necessitates having one predefined, measurable objective function that is relevant in all situations. In reality, people may prioritize many different less-quantifiable metrics, such as stability, comfort, pain, or perceived effort, in any given situation. In this paper, we present a method of exoskeleton controller customization that is based on subject preference, which likely encodes many of these subjective cost functions at once. In this pilot study, two subjects self-tuned their own controller parameters in two dimensions, by directly manipulating the magnitude and timing of bilateral ankle exoskeleton assistance using a touch screen tablet interface. Subjects exhibited high repeatability in identifying their preferred exoskeleton parameters at each of three walking speeds (coefficient of variation between 0.914.5%). Subjects' preferences differed from each other, which highlights the importance of individual customization. The results from this pilot study will inform future experiments that incorporate rigorous measurement of subject preference into the control of robotic ankle exoskeletons. This research has the potential to provide more complete insights into important elements of exoskeleton control that will be useful outside of the steady-state, laboratory environment.

Lower limb exoskeleton robots help with walking movements through mechanical force, by identifying the wearer’s walking intention. When the exoskeleton robot is lightweight and comfortable to wear, the stability of walking increases, and energy can be used efficiently. However, because it is difficult to implement the complex anatomical movements of the human body, most are designed simply. Due to this, misalignment between the human and robot movement causes the wearer to feel uncomfortable, and the stability of walking is reduced. In this paper, we developed a two degrees of freedom (2DoF) ankle exoskeleton robot with a subtalar joint and a talocrural joint, applying a four-bar linkage to realize the anatomical movement of a simple 1DoF structure mainly used for ankles. However, bidirectional tendon-driven actuators (BTDAs) do not consider the difference in a length change of both cables due to dorsiflexion (DF) and plantar flexion (PF) during walking, causing misalignment. To solve this problem, a BTDA was developed by considering the length change of both cables. Cable-driven actuators and exoskeleton robot systems create uncertainty. Accordingly, adaptive control was performed with a proportional-integral-differential neural network (PIDNN) controller to minimize system uncertainty.

Lower-limb wearable exoskeletons have been designed to assist people that have a spinal cord injury during standing and walking. However, because these people generally also have impaired balance, it is difficult, if not impossible for them to operate these exoskeletons without additional supporting aids, such as crutches. Ideally the exoskeleton supports its user’s balance, preferably in a human-like way to match the user's natural intention. Therefore, proper balance control of the exoskeleton is required. This work presents the first steps taken towards postural balance control of lower-limb wearable exoskeletons. The focus is specifically on standing balance control strategies for exoskeletons, inspired by human and humanoid standing balance. The first goal of this thesis was to explore balance control strategies for the application in a lower-limb exoskeletons, with a particular focus on human-like motion generation. In Chapter 2 the ability of the momentum-based controller to generate human-like feet-in-place balance recovery strategies was investigated. Besides feet-in-place balance recovery strategies, people also use a reactive stepping strategy to maintain balance. Therefore, in Chapter 3 it was investigated whether the occurrence of reactive stepping could be predicted using a classification-based method, and what features are most relevant for that prediction. The second goal of this thesis was to verify the effectiveness of exoskeleton balance support. Hence, the effects of an ankle exoskeleton and an ankle-knee exoskeleton on the balance of able-bodied users and (three) users with an incomplete spinal cord injury respectively were assessed in Chapters 4 and 5. By modeling human balance for the use in an exoskeleton on the one hand, and by analyzing and implementing existing balance control strategies on the other, the results presented in this thesis provide insight into how to impose standing balance on exoskeletons and their users.

Stroke is a significant cause of disability worldwide, and stroke survivors often experience severe motor impairments. Lower limb rehabilitation exoskeleton robots provide support and balance for stroke survivors and assist them in performing rehabilitation training tasks, which can effectively improve their quality of life during the later stages of stroke recovery. Lower limb rehabilitation exoskeleton robots have become a hot topic in rehabilitation therapy research. This review introduces traditional rehabilitation assessment methods, explores the possibility of lower limb exoskeleton robots combining sensors and electrophysiological signals to assess stroke survivors' rehabilitation objectively, summarizes standard human-robot coupling models of lower limb rehabilitation exoskeleton robots in recent years, and critically introduces adaptive control models based on motion intent recognition for lower limb exoskeleton robots. This provides new design ideas for the future combination of lower limb rehabilitation exoskeleton robots with rehabilitation assessment, motion assistance, rehabilitation treatment, and adaptive control, making the rehabilitation assessment process more objective and addressing the shortage of rehabilitation therapists to some extent. Finally, the article discusses the current limitations of adaptive control of lower limb rehabilitation exoskeleton robots for stroke survivors and proposes new research directions.

A wearable lightweight lower-limb exoskeleton (LLEX) robot that can help paraplegic patients perform daily activities such as standing/sitting, walking, stair ascending/descending is proposed in this paper firstly. In order to ensure comfort between the exoskeleton and the wearer, a novel configuration of the exoskeleton robot that combines active and passive joints, adaptive structure size, and high compatibility with human physiological parameters is proposed. Secondly, this paper proposes a dynamic gait planning algorithm for stairs ascending and descending based on hip and ankle motion position planning, which is implemented to LLEX. It is intended to provide paraplegic patients with personalized gaits for independently ascending/descending stairs of diverse sizes with the help of LLEX. The system employs the inertial measurement unit (IMU) module located on the back to identify the wearer's movement intention while using the depth camera located on the front of the LLEX to detect heights and depths of different stairs for a dynamic gait. Experiments have been carried out to prove the feasibility and pervasiveness of the gait planning algorithm. The application of LLEX can help patients with various physiological characteristics to complete the task of ascending and descending stairs.

Robotic lower limb exoskeletons allow paraplegics to stand up, walk, and overcome obstacles like stairs and ramps. The selection of the required movement pattern is generally done manually by the user, which reduces the ease of use and prohibits dynamic transitions. The proposed solution for this problem is a machine vision based obstacle recognition system, which allows the exoskeleton to adapt its movements to the environment. To achieve this, the system needs to categorize the obstacle, determine its distance, and measure physical characteristics like step height and ramp slope.The developed system uses an infrared stereo camera paired with an IMU to capture a 3D point cloud of the path of the exoskeleton. Using the RANSAC algorithm, planes are segmented from the point cloud and stairs and ramps are identified based on the geometric constellation of the extracted planes. These surfaces are then used to calculate the quantitative characteristics of the obstacle, that would be needed for adaptive trajectory planning of an exoskeleton. To minimize the probability of wrong classifications or measurements, a higher level evaluation was integrated, which rates the quality of the gathered data based on redundant analyses and suppresses wrong results.The system was implemented on low power hardware intended for mobile applications to match the use case of a mobile exoskeleton. In first experiments, the system showed good results in terms of its detection rate and measurement accuracy, but is currently held back by long process run times due to hardware limitations.

This paper presents experimental results of a myoelectric impedance controller designed for reciprocal stair ascent with an active-knee powered transfemoral prosthesis. The controller is modeled from non-amputee (normal) motion capture data, estimating knee torque with a linear two-state (stance/swing) impedance control form that includes proportional myoelectric torque control. The normal gait model is characterized by small stiffness and damping in both stance and swing, a low angle set-point in stance, a high angle set-point in swing, and proportional myoelectric control in stance but not swing. Clinical tests with a single unilateral transfemoral amputee indicate good performance of the controller; however, subject feedback suggests a reduction in the extensive myoelectric torque parameter and the need for constant, balanced myoelectric torque parameters in both stance and swing. Average prosthesis knee joint kinetics from a stairwell test using the amputee-tuned controller compare favorably with non-amputee gait data.

The lower limb exoskeleton robot is a new technique of rehabilitation training for lower limb dysfunction, which is paid more and more attention by people. Because the lower limb exoskeleton robot is relatively heavy all over the world, this article designs a new lower limb exoskeleton robot whose hip joint is driven by motor and other joints are unpowered. It effectively reduces the lower limb exoskeleton robot’s weight. The kinematics equation of exoskeleton robot is established by D-H coordinate system, and the correctness of kinematics equation is verified with combining simulation by MATLAB and SolidWorks software. The end coordinate data calculated by kinematics equation is used as a contrast to test, and a good walking effect is achieved.

Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarm optimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual’s judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control.

Research on robotic exoskeletons has rapidly expanded over the previous decade. Advances in robotic hardware and energy supplies have enabled viable prototypes for human testing. This review paper describes current lower limb robotic exoskeletons, with specific regard to common trends in the field. The preponderance of published literature lacks rigorous quantitative evaluations of exoskeleton performance, making it difficult to determine the disadvantages and drawbacks of many of the devices. We analyzed common approaches in exoskeleton design and the convergence, or lack thereof, with certain technologies. We focused on actuators, sensors, energy sources, materials, and control strategies. One of the largest hurdles to be overcome in exoskeleton research is the user interface and control. More intuitive and flexible user interfaces are needed to increase the success of robotic exoskeletons. In the last section, we discuss promising future solutions to the major hurdles in exoskeleton control. A number of emerging technologies could deliver substantial advantages to existing and future exoskeleton designs. We conclude with a listing of the advantages and disadvantages of the emerging technologies and discuss possible futures for the field.

Context: Robot-assisted gait rehabilitation is a rapidly evolving field that aims to enhance therapeutic outcomes through the integration of advanced robotic systems. These systems reduce the physical burden on therapists while enabling intensive and repetitive patient training. However, current methods often require manual adjustments tailored to individual gait patterns, limiting their generalizability and efficiency. Objective: This paper provides a comprehensive review of control approaches for robot-based gait rehabilitation and locomotion assistance, focusing on improving adaptability and human-robot synchronization. The goal is to identify effective techniques and methods that optimize exoskeleton performance and improve patient outcomes. Methodology: A state-of-the-art review was conducted through a structured literature search across multiple scientific databases to identify research on lower-limb exoskeletons, centering on control strategies for robot-assisted gait rehabilitation. The search initially employed broad keywords such as “lower limb exoskeletons,” “extremity lower exoskeletons,” and “lower limb robotic exoskeletons,” among others. It was then refined using more specific terms including “lower limb exoskeleton control,” “assisted gait control,” “assistance control strategy,” “wearable robots control,” and “robotic rehabilitation,” among others. Well-defined inclusion criteria ensured thematic relevance, methodological rigor, and comprehensive coverage of recent advancements. A total of 77 studies meeting these criteria were analyzed to identify and categorize control approaches, and to discuss their scope, advantages, and limitations rather than directly assessing their clinical effectiveness or patient outcome improvements. Future research should focus on data-driven control methods to address individual variability and improve exoskeleton adaptability. Results: The review highlights a taxonomy of essential control strategies for robot-assisted gait, such as trajectory tracking based on position, force-impedance control, control driven by bio-signals, and adaptive control techniques. Real-time adaptive strategies, such as Velocity Field Control and Assist-As-Needed approaches, demonstrate strong potential for improving synchronization and reducing manual tuning requirements. Conclusions: Adaptive control strategies, particularly Assist-As-Needed controllers, are effective in encouraging active patient participation and optimizing rehabilitation outcomes. Financing: This research work received funding from Universidad del Valle under the project "Plataforma tecnológica modular para la valoración objetiva de la marcha humana" (C.I. 21259) and from the Ministry of Science, Technology, and Innovation (Minciencias - Open call 647).

Robotic exoskeletons have emerged as effective rehabilitation and ability-enhancement tools, by mimicking or supporting natural body movements. The control schemes of exoskeletons are conventionally developed based on fixed torque-ankle state relationship or various human models, which are often lack of flexibility and adaptability to accurately address personalized movement assistance needs. This paper presents an adaptive control strategy for personalized robotic ankle exoskeleton in an effort to address this limitation. The adaptation was implemented by applying the experience-based fuzzy rule interpolation approach with the support of a muscle-tendon complex model. In particular, this control system is initialized based on the most common requirements of a “standard human model,” which is then evolved during its performance by effectively using the feedback collected from the wearer to support different body shapes and assistance needs. The experimental results based on different human models with various support demands demonstrate the power of the proposed control system in improving the adaptability, and thus applicability, of robotic ankle exoskeletons.

Lower limb exoskeleton robots have become a research hotspot among various enterprises, universities, and institutions. This paper proposes a deep learning network based on the recognition of five movement patterns of lower limb exoskeletons, including walking on level ground, going upstairs, going downstairs, uphill, and downhill. The network uses BiLSTM to capture the forward and backward relationships of time series data, which has better resolution than the original features. In addition, the network uses the KNN algorithm based on shortest-distance voting to improve the recognition accuracy. The network achieved a recognition rate of 99.18% for five motion patterns. In summary, the proposed network has excellent performance in terms of accuracy and generalization.

BackgroundLower-limb exoskeleton robots are being widely used in gait rehabilitation training for patients with stroke. However, most of the current rehabilitation robots are guided by predestined gait trajectories, which are often different from the actual gait trajectories of specific patients. One solution is to train patients using individualized gait trajectories generated from the physical parameters of patients. Hence, we aimed to explore the effect of individual gaits on energy consumption situations during gait rehabilitation training for hemiplegic patients with lower-limb exoskeleton robots.MethodsA total of 9 unilateral-hemiplegic patients were recruited for a 2-day experiment. On the first day of the experiment, the 9 patients were guided by a lower-limb exoskeleton robot, walking on flat ground for 15 min in general gait trajectory, which was gained by clinical gait analysis (CGA) method. On the other day, the same 9 patients wore the identical robot and walked on the same flat ground for 15 min in an individualized gait trajectory. The main physiological parameters including heart rate (HR) and peripheral capillary oxygen saturation (SpO2) were acquired via cardio tachometer and oximeter before and after the walking training. The energy consumption situation was indicated by the variation of the value of HR and SpO2 after walking training compared to before.ResultsBetween-group comparison showed that the individualized gait trajectory training resulted in an increase in HR levels and a decrease in SpO2 levels compared to the general gait trajectory training. The resulting difference had a statistical significance of p < 0.05.ConclusionUsing individualized gait guidance in rehabilitation walking training can significantly improve energy efficiency for hemiplegic patients with stroke.

The clinical efficacy of robotic rehabilitation interventions hinges on appropriate neuromuscular recruitment from the patient. The first purpose of this study was to evaluate the use of supervised machine learning techniques to predict neuromuscular recruitment of the ankle plantar flexors during walking with ankle exoskeleton resistance in individuals with cerebral palsy (CP). The second goal of this study was to utilize the predictive models of plantar flexor recruitment in the design of a personalized biofeedback framework intended to improve (i.e., increase) user engagement when walking with resistance. First, we developed and trained multilayer perceptrons (MLPs), a type of artificial neural network (ANN), utilizing features extracted exclusively from the exoskeleton's onboard sensors, and demonstrated 85-87% accuracy, on average, in predicting muscle recruitment from electromyography measurements. Next, our participants completed a gait training session while receiving audio-visual biofeedback of their personalized real-time planar flexor recruitment predictions from the online MLP. We found that adding biofeedback to resistance elevated plantar flexor recruitment by 24 16% compared to resistance alone. This study highlights the potential for online machine learning frameworks to improve the effectiveness and delivery of robotic rehabilitation systems in clinical populations.