This study presents an innovative solution for integrating a human and a robotic knee prosthesis symbiotically for walking. Achieving this requires human-robot cross-joint coordination to provide personalized walking assistance. Our approach uses inverse reinforcement learning (IRL) to identify control objectives for reinforcement learning (RL) controller. Unlike existing methods that optimize performance of human or robot alone, our approach considers both human (thigh segmental angle) and robot (knee joint kinematics) aspects. This bilevel optimization method was evaluated on 3 non-disabled participants and 2 people with amputation. Results showed that the approach personalized the objective function and resulted in a robust policy, completing optimization within a duration of 3.5 minutes. Compared to previous approaches focusing only on robot states, this symbiotic approach increased stance time and step length on the prosthesis side for most participants. Our results highlight the potential of integrating human state into prosthesis control personalization, enhancing the functionality and health of people with amputation.

Maintaining balance during gait in the mediolateral direction requires more active motor control than in the anteroposterior direction. Step width modulation is a key strategy used by healthy individuals to achieve mediolateral walking balance, but it can be disrupted in populations with poor sensorimotor integration and weak hip abductors, such as the elderly, stroke patients, and people with lower limb amputation. Wearable hip exoskeletons have the potential to serve as assistive or rehabilitation devices for these populations, but there has been limited research on their appropriate usage. In this study, we successfully demonstrated the feasibility of controlling step width using a mediolaterally acting robotic hip exoskeleton. We were able to effectively adjust the user's step width by increasing or decreasing it to predefined targets through the regulation of admittance control parameters governing the device. The maximum average error to increase or decrease the step width was 1.2 cm. This research has the potential to facilitate the development of assistive and rehabilitation applications focused on enhancing the mediolateral gait balance of individuals with neurological impairments, elderly individuals, and amputees via the control of step width.

There is increasing evidence of the role of compromised mediolateral balance in falls and the need for rehabilitation specifically focused on mediolateral direction for various populations with motor deficits. To address this need, we have developed a neurorehabilitation platform by integrating a wearable robotic hip abduction-adduction exoskeleton with a visual interface. The platform is expected to influence and rehabilitate the underlying visuomotor mechanisms in individuals by having users perform motion tasks based on visual feedback while the robot applies various controlled resistances governed by the admittance controller implemented in the robot. A preliminary study was performed on 3 non disabled individuals to analyze the performance of the system and observe any adaptation in hip joint kinematics and kinetics as a result of the visuomotor training under 4 different admittance conditions. All three subjects exhibited increased consistency of motion during training and interlimb coordination to achieve motion tasks, demonstrating the utility of the system. Further analysis of observed human-robot torque interactions and electromyography (EMG) signals, and its implication in neurorehabilitation aimed at populations suffering from chronic stroke are discussed.

Human-in-the-loop (HIL) optimization usually optimizes assistive torque of exoskeletons to minimize the human's energetic expenditure in walking, quantified by metabolic cost. This formulation can, however, result in altered gait pattern of the human joint from the natural pattern, which is undesired. In this paper, we proposed a novel concept of HIL optimization of a hip exoskeleton. The optimization goal was to maintain the hip kinematics while providing optimal mechanical energy from the exoskeleton by modulating the admittance control. Policy iteration was used to optimize the switching time within the gait phase, at which a single parameter of the admittance controller was altered to provide assistance. The stiffness and equilibrium angle were considered as the two parameters for altering at the switching time, resulting in three possible modes of operation for the algorithm: (i) switching the equilibrium point, (ii) switching stiffness while equilibrium point is set at maximum extension and, (iii) maximum flexion. The optimization algorithm was found to converge for all three modes, with the equilibrium mode resulting in multiple solutions. Further analysis of power injected by the exoskeleton in the three modes showed that the first and third mode reduced human energetic exertion while the second mode increased human exertion. Implications of the results as well as the observed muscle activation patterns in response to assistance are discussed.

Existing robotic lower-limb prostheses use autonomous control to address cyclic, locomotive tasks, but are inadequate in adapting to variations in non-cyclic and unpredictable tasks. This study aims to address this challenge by designing a novel electromyography (EMG)-driven musculoskeletal model for volitional control of a robotic ankle-foot prosthesis. The proposed controller ensures continuous control of the device, allowing users to freely manipulate the prosthesis behavior. A Hill-type muscle model was implemented to model a dorsiflexor and a plantarflexor to function around a virtual ankle joint. The model parameters for a subject specific model was determined by fitting the model to the experimental data collected from an able-bodied subject. EMG signals recorded from antagonist muscle pairs were used to activate the virtual muscle models. This model-based approach was then validated via offline simulations and real-time prosthesis control. Additionally, the feasibility of the proposed prosthesis control on assisting the user's functional tasks was demonstrated. The present control may further improve the function of robotic prosthesis for supporting versatile activities in individuals with lower-limb amputations.

The tuning process for a robotic prosthesis is a challenging and time-consuming task both for users and clinicians. An automatic tuning approach using reinforcement learning (RL) has been developed for a knee prosthesis to address the challenges of manual tuning methods. The algorithm tunes the optimal control parameters based on the provided knee joint profile that the prosthesis is expected to replicate during gait safely. This paper presents an intuitive interface designed for the prosthesis users and clinicians to choose the preferred knee joint profile during gait and use the autotuner to replicate in the prosthesis. The interface-based approach is validated by observing the ability of the tuning algorithm to successfully converge to various alternate knee profiles by testing on two able-bodied subjects walking with a robotic knee prosthesis. The algorithm was found to converge successfully in an average duration of 1.15 min for the first subject and 2.31 min for the second subject. Further, the subjects displayed different preferences for optimal profiles reinforcing the need to tune alternate profiles. The implications of the results in the tuning of robotic prosthetic devices are discussed.

This paper presents a novel multi-axis robotic platform for the characterization of two important neuromuscular properties of the human ankle: mechanical impedance and reflex responses. The platform is capable of producing highly accurate position perturbations up to an angular speed of 200°/s and emulating a wide range of haptic environments in two degree-of-freedom (DOF) of the ankle: dorsiflexion-plantarflexion (in the sagittal plane) and inversion-eversion (in the frontal plane). This unique feature allows us to seamlessly simulate realistic mechanical environments and to transiently perturb the ankle for the characterization of its neuromuscular properties. The position controller achieved the accuracy of 0.05° even under the loading condition (a subject of 95 kg standing on the platform). The haptic controller could successfully emulate a wide range of mechanical environments, from compliant to rigid (50-1000 Nm/rad), with an error of 2% of the commanded values. We further validated that the proposed platform could reliably estimate the stiffness of a mockup (17.8-171.0 Nm/rad) that resembles the human ankle within an error of 1.6%. Finally we demonstrated that the platform could be successfully utilized to elicit medium-latency and long-latency reflex responses of the ankle muscles. Implications for future ankle studies are discussed.