In rehabilitation robotics, control of energy flow is not merely a stability requirement but a therapeutic tool that shapes the quality and safety of human-robot interaction (HRI). The precise modulation of potential and kinetic energy within the coupled human–robot system governs how assistance is provided, how disturbances are rejected, and how patient effort is encouraged. Energy shaping approaches enable the controller to sculpt an artificial energy landscape anchored at a prescribed reference posture, so that restorative torques emerge naturally from the gradient of the shaped potential and disturbance-rich interactions are regulated within bounded, passive operating limits. This study presents a novel deep learning-based energy shaping framework for torque control in a three-degree-of-freedom (DOF) ankle rehabilitation robot. The proposed method is rooted in port-Hamiltonian mechanics. It employs interconnection and damping assignment-passivity-based control (IDA-PBC) to shape the energy landscape of the system, promoting practical stability and safe patient interaction. To address the limitations of static or heuristic energy shaping, we introduce a physics-informed data-driven approach in which the potential energy function is dynamically constructed through an Attention-Augmented Fourier Neural Operator (AFNO). This architecture learns mappings from spatiotemporal sensor data, including joint kinematics and interaction torques, to optimal shaping parameters that define the control energy field. The control strategy was experimentally validated on an ankle rehabilitation robot with ten healthy subjects (eight male, two female, aged 25–43), performing controlled movements across dorsiflexion/plantarflexion, inversion/eversion, and abduction/adduction. Experimental data confirmed that the shaped potential energy fields successfully guided joint trajectories toward the prescribed reference posture under disturbance-rich interaction conditions, while maintaining passivity and minimizing unnecessary energy expenditure.

The global elderly population is increasing rapidly, and more and more countries are stepping into the aging society. Because of deterioration in gait-related functions, such as muscle activity, most elderly people have difficulty walking, which is one of the most basic activities of daily living. A wearable lower limb robot exoskeleton offers a viable solution to this issue. Rigid exoskeletons usually have fewer degrees of freedom and require a quite big power supply for actuation, resulting in difficulties during wearing and motion. Soft exoskeletons could be an alternative choice for elderly assistance, due to their advantages of lighter weight, simpler mechanism, and compliance. Therefore, this research proposed a soft array actuator for a lower limb assistive exoskeleton for elderly people. The fully compliant actuation mechanism was designed using flexible Thermoplastic Polyurethane fabric to power the soft exoskeleton, making it compact, light, and comfortable to wear. The proposed array actuator was modeled on kinematics, contacting force, output torque, and dynamics. Mechanical performance of the compliant actuation mechanism during inflation/deflation at different air pressures and during static force testing has been evaluated. The internal air pressure of the soft array actuator reaches 100 kPa in 0.2 s and fully deflates in 0.48 s. It can generate 53.7–55.7 N to output 8.1–11.2 N·m of torque at the endpoint, which is 13.5% –18.7% of the maximum human hip joint torque during natural walking. The proposed soft actuation mechanism, with good wearability and performance, could be a good fit for a robot exoskeleton that improves natural gait and reduces human metabolic consumption during walking among the elderly. Its lightweight design, good invisibility, and high power-to-weight ratio enable the proposed soft array actuator to provide compliant, continuous, and consistent gait assistance without imposing a psychological burden on the elderly.

Abstract The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there are no systematic approaches for solving the inverse kinematics problem. Furthermore, for some morphology of the upper limb rehabilitation robots, the inverse kinematics problem is particularly challenging to solve. Conventional methods to solve the inverse kinematics problem reported in the literature are computationally expensive. In the present work, we propose a deep learning-based model to acquire the joint angles for a given end-effector position. The proposed approach exhibits high efficacy in determining the joint angles for various target positions and can accurately predict the end-effector positions once trained, improving the ability of the upper limb rehabilitation robot to adapt to varying patient needs. Due to its improved capability and effectiveness to track positions, the proposed algorithm lays the foundation for the development of efficient controllers in future.

Optimizing energy transfer during physical human–robot interactions is important for enhancing neurotherapeutic outcomes and ensuring patient safety. Energy transfer dynamics are particularly complex, involving a delicate balance between kinetic and potential energies as the robot assists or resists movement, adapting to the patient’s needs in real time. Traditional methods, which often rely on predefined robot control strategies, often struggle in dynamic environments where the interplay of forces and motions becomes unpredictable. Therefore, this work integrates the computational intelligence of quantum computing with transformer models to estimate the dynamics of energy transfer between human and gait rehabilitation robot, specifically designed based on the Stephenson III six-bar linkage mechanism. The principles of quantum computing, such as superposition and entanglement, combined with the attention mechanisms of transformer models, explore a much larger solution space. It provides accurate predictions of the complex, nonlinear interactions of energy flows between the robot and the human lower limb. The quantum transformer network was trained on the experimental data obtained from the interaction of seven male and one female healthy human subjects with the gait rehabilitation robot operated at low and high impedance control modes.

Disability from injuries and diseases is a global problem affecting a large population; however, due to a lack of therapists and labor-intensive procedures, only a few benefits from rehabilitation. Robots can assist therapists in treating many patients simultaneously, but the existing solutions need improvements in their mechanism, actuation, and control. This article presents a four-link parallel end-effector robot for wrist joint rehabilitation. The proposed robot employs biomimetic muscle actuators (BMA) that provide intrinsic compliance to the robotic system. A fuzzy-based model is developed to identify the nonlinear nature of BMAs. The stiffness-observer learns subject-specific stiffness, which is used to modify the robot reference trajectories. An adaptive controller uses the fuzzy model and stiffness-observer and simultaneously controls the four BMAs to provide three degrees of rotational freedom to the robot end-effector. The feasibility of the robot mechanism and the controller was evaluated through proof of concept experiments conducted with three unimpaired human subjects. It was found that the controller was able to guide the robot–human system on the commanded trajectories in the presence of parallel actuation of compliant and nonlinear BMAs. Furthermore, the controller was also able to modify the commanded trajectories in the higher stiffness regions of the wrist workspace.

Robot-assisted physical therapy of the upper limb is becoming popular among the rehabilitation community. The wrist is the second most complicated joint in the upper limb after shoulder in terms of degrees of freedom. Several robotic devices have been developed during the past three decades for wrist joint rehabilitation. Intensive physical therapy and repetitive self-practice, with objective measurement of performance, could be provided by using these wrist rehabilitation robots at a low cost. There has been an increasing trend in the development of wrist rehabilitation robots to provide safe and customized therapy according to the disability level of patients. The mechanical design and control paradigms are two active fields of research undergoing rapid developments in the field of robot-assisted wrist rehabilitation. The mechanical design of these robots could be divided into the categories of end-effector based robots and wearable robotic orthoses. The control for these wrist rehabilitation robots could also be divided into the conventional trajectory tracking control mode and the assist-as-needed control mode for providing customized robotic assistance. This article presents a review of the mechanical design and control aspects of wrist rehabilitation robots. Experimental evaluations of these robots with healthy and neurologically impaired are also discussed along with the future directions of research in the design and control domains of wrist rehabilitation robots.

While newer designs and control approaches are being proposed for rehabilitation robots, vital information from the human musculoskeletal system should also be considered. Incorporating knowledge about joint biomechanics during the development of robot controllers can enhance the safety and performance of robot-aided treatments. In this article, the optimal path or trajectories of a parallel ankle rehabilitation robot were generated by minimizing joint reaction moments and the tension along ligaments and muscle-tendon units. The simulations showed that using optimized robot paths, user efforts could be reduced to 80%, thereby ensuring less strain on weaker or stiffer ligaments, etc. Additionally, to limit the moments applied by the robot in stiff or constrained directions, the intended robot path was modified to move the commanded position in the direction opposite to that of the position error. Such online modification of the robot path can lead to a reduction in forces applied by a robot to the subject. Simulation results and experimental findings with healthy subjects using an ankle rehabilitation robot prototype and subsequent statistical analysis further validated that path modification based on ankle joint biomechanics results in a reduction in undesired forces experienced by human users during treatment.

Robotic rehabilitation devices are more frequently used for the physical therapy of people with upper limb weakness, which is the most common type of stroke-induced disability. Rehabilitation robots can provide customized, prolonged, intensive, and repetitive training sessions for patients with neurological impairments. In most cases, the robotic exoskeletons have to be aligned with the human joints and provide natural arm movements. This is a challenging task to achieve for one of the most biomechanically complex joints of human body, i.e., the shoulder. Therefore, specific considerations have been made in the development of various existing robotic shoulder rehabilitation orthoses. Different types of actuation, degrees of freedom (DOFs), and control strategies have been utilized for the development of these shoulder rehabilitation orthoses. This paper presents a comprehensive review of these shoulder rehabilitation orthoses. Recent advancements in the mechanism design, their advantages and disadvantages, overview of hardware, actuation system, and power transmission are discussed in detail with the emphasis on the assisted DOFs for shoulder motion. A brief overview of control techniques and clinical studies conducted with the developed robotic shoulder orthoses is also presented. Finally, current challenges and directions of future development for robotic shoulder rehabilitation orthoses are provided at the end of this paper.

Parallel robots, owing to their increased stiffness, accuracy, and compactness, are preferred over their serial counterparts in applications involving higher torques and precision such as robot-assisted physical therapy. However, their design is complex and calls for obtaining a tradeoff between several conflicting objectives such as the minimization of actuator forces versus the maximization of workspace while maintaining a close to unity condition number, etc. While evolutionary algorithms have been proposed in the literature for simultaneous optimization of many objectives, they have been found to be inefficient in dealing with a large number of objectives. We propose a fuzzy logic-based sorting approach in this paper which effectively replaces the concept of nondominated sorting and provides a better discrimination between solutions and clear termination logic. The proposed sorting algorithm has been evaluated against the existing nondominated sorting genetic algorithm II in the pretext of design optimization of a parallel ankle rehabilitation robot. The proposed fuzzy-based approach is able to provide a better discrimination among solutions and, thereby an improved parallel ankle robot design.

This paper describes the design analysis and optimization of a novel 3-degrees of freedom (DOF) wearable parallel robot developed for ankle rehabilitation treatments. To address the challenges arising from the use of a parallel mechanism, flexible actuators, and the constraints imposed by the ankle rehabilitation treatment, a complete robot design analysis is performed. Three design stages of the robot, namely, kinematic design, actuation design, and structural design are identified and investigated, and, in the process, six important performance objectives are identified which are vital to achieve design goals. Initially, the optimization is performed by considering only a single objective. Further analysis revealed that some of these objectives are conflicting, and hence these are required to be simultaneously optimized. To investigate a further improvement in the optimal values of design objectives, a preference-based approach and evolutionary-algorithm-based nondominated sorting algorithm (NSGA II) are adapted to the present design optimization problem. Results from NSGA II are compared with the results obtained from the single objective optimization and preference-based optimization approaches. It is found that NSGA II is able to provide better design solutions and is adequate to optimize all of the objective functions concurrently. Finally, a fuzzy-based ranking method has been devised and implemented in order to select the final design solution from the set of nondominated solutions obtained through NSGA II. The proposed design analysis of parallel robots together with the multiobjective optimization and subsequent fuzzy-based ranking can be generalized with modest efforts for the development of all of the classes of parallel robots.

Intervention of robotic devices in the field of physical gait therapy can help in providing repetitive, systematic, and economically viable training sessions. Interactive or assist-as-needed (AAN) gait training encourages patient voluntary participation in the robotic gait training process which may aid in rapid motor function recovery. In this paper, a lightweight robotic gait training orthosis with two actuated and four passive degrees of freedom (DOFs) is proposed. The actuated DOFs were powered by pneumatic muscle actuators. An AAN gait training paradigm based on adaptive impedance control was developed to provide interactive robotic gait training. The proposed adaptive impedance control scheme adapts the robotic assistance according to the disability level and voluntary participation of human subjects. The robotic orthosis was operated in two gait training modes, namely, inactive mode and active mode, to evaluate the performance of the proposed control scheme. The adaptive impedance control scheme was evaluated on ten neurologically intact subjects. The experimental results demonstrate that an increase in voluntary participation of human subjects resulted in a decrease of the robotic assistance and vice versa. Further clinical evaluations with neurologically impaired subjects are required to establish the therapeutic efficacy of the adaptive-impedance-control-based AAN gait training strategy.

Cadence or stride frequency is an important parameter being controlled in gait training of neurologically impaired subjects. The aim of this study was to examine the effects of cadence variation on muscle activation patterns during robot assisted unimpaired gait using dynamic simulations. A twodimensional (2-D) musculoskeletal model of human gait was developed considering eight major muscle groups along with existing ground contact force (GCF) model. A 2-D model of a robotic orthosis was also developed which provides actuation to the hip, knee and ankle joints in the sagittal plane to guide subjects limbs on reference trajectories. A custom inverse dynamics algorithm was used along with a quadratic minimization algorithm to obtain a feasible set of muscle activation patterns. Predicted patterns of muscle activations during slow, natural and fast cadence were compared and the mean muscle activations were found to be increasing with an increase in cadence. The proposed dynamic simulation provide important insight into the muscle activation variations with change in cadence during robot assisted gait and provide the basis for investigating the influence of cadence regulation on neuromuscular parameters of interest during robot assisted gait.

Robot-assisted gait therapy is an emerging rehabilitation practice. This paper presents new experimental results with an intrinsically compliant robotic gait training orthosis and a trajectory tracking controller. The intrinsically compliant robotic orthosis has six degrees of freedom. Sagittal plane hip and knee joints were powered by the actuation of pneumatic muscle actuators in opposing pair configuration. The orthosis has passive hip abduction/adduction joint and passive mechanisms to allow vertical and lateral translations of the trunk. A passive foot lifter having a spring mechanism was used to ensure sufficient dorsiflexion during swing phase. A trajectory tracking controller based on a chattering-free robust variable structure control law was implemented in joint space to guide the subject's limbs on physiological gait trajectories. The performance of the robotic orthosis was evaluated during two gait training modes, namely, “trajectory tracking mode with maximum compliance” and “trajectory tracking mode with minimum compliance.” The experimental evaluations were carried out with ten neurologically intact subjects. The results show that the robotic orthosis is able to perform the gait training task during the two gait training modes. All the subjects tend to deviate from the reference joint angle trajectories with an increase in robotic compliance as the subjects have more freedom to voluntarily drive the robotic orthosis.