Robotic systems assess joint dynamics objectively by perturbing the limb and estimating properties such as impedance. Position perturbations constrain the limb to a target trajectory, reducing variability in task execution but obstructing voluntary motion. Force perturbations allow voluntary movement but elicit orientation-dependent responses, increasing the number of trials needed for accurate estimates. To overcome these limitations, we combined the flexibility of admittance control with the repeatability of position perturbations. A minimum-jerk trajectory ensures smooth transitions. The experiment with six healthy participants was performed to demonstrate the reliability, accuracy and smoothness of applying such perturbations during voluntary movement. Reliability was the proportion of perturbations that reached the target velocity within one millisecond of the acceleration time window. Accuracy was measured as the RMSE between the target and measured velocity during the constant velocity. Smoothness was assessed as perceivability: the fraction of trials in which participants correctly detected a perturbation. The controller allows continuous voluntary movement, switching only during perturbations to impose a precise, specified perturbation. All perturbations reached the target velocity within one millisecond of the acceleration time window; thus, the method is reliable. Under the most demanding condition— an increase to 200 deg/s in 0.01 s—the RMSE between target and measured velocity was 1.1 deg/s (0.55%), indicating a high accuracy. Specially designed perturbations had a perceivability accuracy of 22.1%, indicating smooth transitions between control modes. Together, these results indicate a promising approach for assessing joint dynamics during voluntary elbow movement, enabling assessment during activities of daily living.

Human intention detection with hand motion prediction is critical to drive the upper-extremity assistive robots in neurorehabilitation applications. However, the traditional methods relying on physiological signal measurement are restrictive and often lack environmental context. We propose a novel approach that predicts future sequences of both hand poses and joint positions. This method integrates gaze information, historical hand motion sequences, and environmental object data, adapting dynamically to the assistive needs of the patient without prior knowledge of the intended object for grasping. Specifically, we use a vector-quantized variational autoencoder for robust hand pose encoding with an autoregressive generative transformer for effective hand motion sequence prediction. We demonstrate the usability of these novel techniques in a pilot study with healthy subjects. To train and evaluate the proposed method, we collect a dataset consisting of various types of grasp actions on different objects from multiple subjects. Through extensive experiments, we demonstrate that the proposed method can successfully predict sequential hand movement. Especially, the gaze information shows significant enhancements in prediction capabilities, particularly with fewer input frames, highlighting the potential of the proposed method for real-world applications.

To increase the quality of life of stroke patients, better diagnostics with the ability to identify the cause of motor impairment are needed. Robotic diagnostics increases the resolution of measurements, allows for tracking progress over a longer period, and can be used to evaluate new treatments. The Shoulder Elbow Perturbator (SEP) was developed to improve the diagnostics of post-stroke motor impairment. The SEP has already been tested on patients, showing promising results in identifying the cause of motor impairment, but no SEP system performance analysis has been published. To identify the joint properties of the elbow accurately, the SEP should have a bandwidth of at least 12 Hz. Furthermore, admittance and velocity control are required for various possible experimental tasks. This paper shows that the SEP performs adequately for the desired perturbations and experimental conditions for system identification of the human elbow. The SEP's performance is analysed with multisine signals to determine the bandwidth and endpoint dynamics. The velocity controller bandwidth is 50 Hz, and the admittance controller bandwidth is 65 Hz. Furthermore, the controller is stable. Thus, the SEP meets all the requirements and should be able to provide the desired perturbations and experimental conditions needed for system identification of the human elbow.

Haptic interaction is critical in physical Human-Robot Interaction (pHRI), given its wide applications in manufacturing, medical and healthcare, and various industry tasks. A stable haptic interface is always needed while the human operator interacts with the robot. Passivity-based approaches have been widely utilised in the control design as a sufficient condition for stability. However, it is a conservative approach which therefore sacrifices performance to maintain stability. This paper proposes a novel concept to characterise an ultimately passive system, which can achieve the boundedness of the energy in the steady-state. A so-called Ultimately Passive Controller (UPC) is then proposed. This algorithm switches the system between a nominal mode for keeping desired performance and a conservative mode when needed to remain stable. An experimental evaluation on two robotic systems, one admittance-based and one impedance-based, demonstrates the potential interest of the proposed framework compared to existing approaches. The results demonstrate the possibility of UPC in finding a more aggressive trade-off between haptic performance and system stability, while still providing a stability guarantee.