Tailoring Upper-Limb Robot-Aided Orthopedic Rehabilitation on Patients' Psychophysiological State

Characterizing eye gaze and mental workload for assistive device control

Eye gaze tracking is increasingly popular due to improved technology and availability. In the domain of assistive device control, however, eye gaze tracking is often used in discrete ways (e.g., activating buttons on a screen), and does not harness the full potential of the gaze signal. In this article, we present a method for collecting both reactionary and controlled eye gaze signals, via screen-based tasks designed to isolate various types of eye movements. The resulting data allows us to build an individualized characterization for eye gaze interface use. Results from a study conducted with participants with motor impairments are presented, offering insights into maximizing the potential of eye gaze for assistive device control. Importantly, we demonstrate the potential for incorporating direct continuous eye gaze inputs into gaze-based interface designs; generally seen as intractable due to the 'Midas touch' problem of differentiating between gaze movements for perception versus for interface operation. Our key insight is to make use of an individualized measure of smooth pursuit characteristics to differentiate between gaze for control and gaze for environment scanning. We also present results relating to gaze-based metrics for mental workload and show the potential for the concurrent use of eye gaze for control input as well as assessing a user's mental workload both offline and in real-time. These findings might inform the development of continuous control paradigms using eye gaze, as well as the use of eye tracking as the sole input modality to systems that share control between human-generated and autonomy-generated inputs.

A fuzzy-logic approach for longitudinal assessment of patients' psychophysiological state: an application to upper-limb orthopedic robot-aided rehabilitation

Understanding the psychophysiological state during robot-aided rehabilitation is crucial for assessing the patient experience during treatments. This paper introduces a psychophysiological estimation approach using a Fuzzy Logic inference model to assess patients' perception of robots during upper-limb robot-aided rehabilitation sessions. The patients were asked to perform nine cycles of 3D point-to-point trajectories toward different targets at varying heights with the assistance of an anthropomorphic robotic arm (i.e. KUKA LWR 4+). Physiological parameters, including galvanic skin response, heart rate, and respiration rate, were monitored across ten out of forty daily sessions. This data enabled the construction of an inference model to estimate patients' perception states. Results expressed in terms of correlation coefficients between the patient state and the increasing number of sessions. Correlation coefficients showed statistically significant strong associations: a state of heightened engagement (formerly described as "Excited") had ρ = - 0.73 (p-value=0.01), and a more calm and resting state (namely "Relaxed" state) had ρ = 0.70 (p-value=0.02) with the number of sessions completed. All patients had positive interaction with the robot, initially expressing curiosity and interest that gradually shifted to a more "Relaxed" state over time.

A greedy assist-as-needed controller for end-effect upper limb rehabilitation robot based on 3-DOF potential field constraints

It has been proven that robot-assisted rehabilitation training can effectively promote the recovery of upper-limb motor function in post-stroke patients. Increasing patients' active participation by providing assist-as-needed (AAN) control strategies is key to the effectiveness of robot-assisted rehabilitation training. In this paper, a greedy assist-as-needed (GAAN) controller based on radial basis function (RBF) network combined with 3 degrees of freedom (3-DOF) potential constraints was proposed to provide AAN interactive forces of an end-effect upper limb rehabilitation robot. The proposed 3-DOF potential fields were adopted to constrain the tangential motions of three kinds of typical target trajectories (one-dimensional (1D) lines, two-dimensional (2D) curves and three-dimensional (3D) spirals) while the GAAN controller was designed to estimate the motor capability of a subject and provide appropriate robot-assisted forces. The co-simulation (Adams-Matlab/Simulink) experiments and behavioral experiments on 10 healthy volunteers were conducted to validate the utility of the GAAN controller. The experimental results demonstrated that the GAAN controller combined with 3-DOF potential field constraints enabled the subjects to actively participate in kinds of tracking tasks while keeping acceptable tracking accuracies. 3D spirals could be better in stimulating subjects' active participation when compared to 1D and 2D target trajectories. The current GAAN controller has the potential to be applied to existing commercial upper limb rehabilitation robots.

Eliciting Force and Slippage in Upper Limb Amputees Through Transcutaneous Electrical Nerve Stimulation (TENS)

Upper limb amputation severely affects the quality of life of individuals. Therefore, developing closed-loop upper-limb prostheses would enhance the sensory-motor capabilities of the prosthetic user. Considering design priorities based on user needs, the restoration of sensory feedback is one of the most desired features. This study focuses on employing Transcutaneous Electrical Nerve Stimulation (TENS) as a non-invasive somatotopic stimulation technique for restoring somatic sensations in upper-limb amputees. The aim of this study is to propose two encoding strategies to elicit force and slippage sensations in transradial amputees. The former aims at restoring three different levels of force through a Linear Pulse Amplitude Modulation (LPAM); the latter is devoted to elicit slippage sensations through Apparent Moving Sensation (AMS) by means of three different algorithms, i.e. the Pulse Amplitude Variation (PAV), the Pulse Width Variation (PWV) and Inter-Stimulus Delay Modulation (ISDM). Amputees had to characterize perceived sensations and to perform force and slippage recognition tasks. Results demonstrates that amputees were able to correctly identify low, medium and high levels of force, with an accuracy above the 80% and similarly, to also discriminate the slippage moving direction with a high accuracy above 90%, also highlighting that ISDM would be the most suitable method, among the three AMS strategies to deliver slippage sensations. It was demonstrated for the first time that the developed encoding strategies are effective methods to somatotopically reintroduce in the amputees, by means of TENS, force and slippage sensations.

Direct Comparisons of Upper-Limb Motor Learning Performance Among Three Types of Haptic Guidance With Non-Assisted Condition in Spiral Drawing Task

In robot-assisted rehabilitation, it is unclear which type of haptic guidance is effective for regaining motor function because of the lack of direct comparisons among multiple types of haptic guidance. The objective of this study was to investigate the effects of different types of haptic guidance on upper limb motor learning in a spiral drawing task. Healthy young participants performed two experiments in which they practiced the drawing movement using a robotic manipulandum with a virtual wall (Path guidance), running direction pushing and virtual wall (Path & Push guidance), restriction to the target movement (Target guidance), or without haptic guidance (Free guidance). Experiment 1 compared the learning effects of the four types of guidance. Experiment 2 investigated the effects of pre-learning with Path, Path & Push, or Target guidance on post-learning with Free guidance. In Experiment 1, Free guidance demonstrated the greatest learning effect, followed by Path guidance, which showed a significantly greater improvement in task performance than the other two types of guidance. In Experiment 2, the type of pre-learning did not influence post-learning with Free guidance. The results suggested that learning with Path guidance showed a slightly slower but comparable effect to Free guidance and was the most effective among the three types of haptic guidance. The superiority of Path guidance over other haptic guidance was interpreted within the framework of error-based learning, in which the intensity of sensory feedback and voluntary motor control play important roles.

AI-based methodologies for exoskeleton-assisted rehabilitation of the lower limb: a review

Over the past few years, there has been a noticeable surge in efforts to design novel tools and approaches that incorporate Artificial Intelligence (AI) into rehabilitation of persons with lower-limb impairments, using robotic exoskeletons. The potential benefits include the ability to implement personalized rehabilitation therapies by leveraging AI for robot control and data analysis, facilitating personalized feedback and guidance. Despite this, there is a current lack of literature review specifically focusing on AI applications in lower-limb rehabilitative robotics. To address this gap, our work aims at performing a review of 37 peer-reviewed papers. This review categorizes selected papers based on robotic application scenarios or AI methodologies. Additionally, it uniquely contributes by providing a detailed summary of input features, AI model performance, enrolled populations, exoskeletal systems used in the validation process, and specific tasks for each paper. The innovative aspect lies in offering a clear understanding of the suitability of different algorithms for specific tasks, intending to guide future developments and support informed decision-making in the realm of lower-limb exoskeleton and AI applications.

Modeling multi-contact point physical interaction between the anthropomorphic finger and soft robotic exo-digit for wearable rehabilitation robotics applications

Introduction: Effective control of rehabilitation robots requires considering the distributed and multi-contact point physical human-robot interaction and users' biomechanical variation. This paper presents a quasi-static model for the motion of a soft robotic exo-digit while physically interacting with an anthropomorphic finger model for physical therapy. Methods: Quasi-static analytical models were developed for modeling the motion of the soft robot, the anthropomorphic finger, and their coupled physical interaction. An intertwining of kinematics and quasi-static motion was studied to model the distributed (multiple contact points) interaction between the robot and a human finger model. The anthropomorphic finger was modeled as an articulated multi-rigid body structure with multi-contact point interaction. The soft robot was modeled as an articulated hybrid soft-and-rigid model with a constant bending curvature and a constant length for each soft segment. A hyperelastic constitute model based on Yeoh's 3rdorder material model was used for modeling the soft elastomer. The developed models were experimentally evaluated for 1) free motion of individual soft actuators and 2) constrained motion of the soft robotic exo-digit and anthropomorphic finger model. Results and Discussion: Simulation and experimental results were compared for performance evaluations. The theoretical and experimental results were in agreement for free motion, and the deviation from the constrained motion was in the range of the experimental errors. The outcomes also provided an insight into the importance of considering lengthening for the soft actuators.