Haptic feedback is known to enhance the realism of an individual’s interactions with objects in virtual environments. Wearable haptic devices, such as vibrotactile sleeves or armbands, can provide haptic feedback in a smaller and more lightweight form factor than haptic gloves that can be bulky and cumbersome to the wearer. In this article, we present tactile and squeeze bracelet interface (Tasbi), a multimodal haptic wristband that can provide radial squeeze forces around the wrist along with vibrotactile feedback at six discrete locations around the band. Tasbi implements a squeezing mechanism that minimizes tangential forces between the band’s points of contact with the skin, instead of focusing the motor actuation to predominantly normal forces. Force sensing capacitors enable closed-loop control of the squeeze force, while vibration is achieved with linear resonant actuators. A detailed description of the design and experimental results demonstrating closed-loop control of squeeze cues provided by Tasbi is presented. Additionally, we present the results of psychophysical experiments that quantify user perception of the vibration and squeeze cues, including vibrotactile identification accuracy in the presence of varying squeeze forces, discrimination thresholds for the squeeze force, and an analysis of user preferences for squeeze actuation magnitudes.

We present a learning and control strategy that enables robots to harness physical human interventions to update their trajectory and goal during autonomous tasks. Within the state of the art, the robot typically reacts to physical interactions by modifying a local segment of its trajectory, or by searching for the global trajectory offline, using either replanning or previous demonstrations. Instead, we explore a one-shot approach: here, the robot updates its entire trajectory and goal in real time without relying on multiple iterations, offline demonstrations, or replanning. Our solution is grounded in optimal control and gradient descent, and extends linear-quadratic regulator controllers to generalize across methods that locally or globally modify the robot’s underlying trajectory. In the best case, this Linear-quadratic regulator + Learning approach matches the optimal offline response to physical interactions, and—in more challenging cases—our strategy is robust to noisy and unexpected human corrections. We compare the proposed solution against other real-time strategies in a user study and demonstrate its efficacy in terms of both objective and subjective measures.

Robotic teleoperation enables humans to safely complete exploratory procedures in remote locations for applications such as deep sea exploration or building assessments following natural disasters. Successful task completion requires meaningful dual arm robotic coordination and proper understanding of the environment. While these capabilities are inherent to humans via impedance regulation and haptic interactions, they can be challenging to achieve in telerobotic systems. Teleimpedance control has allowed impedance regulation in such applications, and bilateral teleoperation systems aim to restore haptic sensation to the operator, though often at the expense of stability or workspace size. Wearable haptic devices have the potential to apprise the operator of key forces during task completion while maintaining stability and transparency. In this paper, we evaluate the impact of wearable haptics for force feedback in teleimpedance control for dual-arm robotic teleoperation. Participants completed a peg-in-hole, box placement task, aiming to seat as many boxes as possible within the trial period. Experiments were conducted both transparent and opaque boxes. With the opaque box, participants achieved a higher number of successful placements with haptic feedback, and we saw higher mean interaction forces. Results suggest that the provision of wearable haptic feedback may increase confidence when visual cues are obscured.

Objective assessment of surgical skill is gaining traction in a number of specialty fields. In robot-assisted surgery in particular, the availability of data from the operating console and patient-side robot offers the potential to derive objective metrics of performance based on tool movement kinematics. While these techniques are becoming established in the laparoscopic domain, current assessment techniques for robotic endovascular surgery are based primarily on observation, checklists, and grading scales. This work presents an objective and quantitative means of measuring technical competence based on analysis of the kinematics of endovascular tool tip motions controlled with a robotic interface. We designed an experiment that recorded catheter tip movement from 21 subjects performing fundamental endovascular robotic navigation tasks on a physical model. Motion-based measures of smoothness (spectral arc length and number of submovements) were computed and tested for correlation with subjective scores from a global rating scale assessment tool that has been validated for use when performing manual catheterization. Results show that the smoothness metrics that produced significant correlations with the global rating scale for manual catheterization show similar correlations for robotic catheterization. This finding is notable, since with the robotic interface, tool tip motion is commanded discretely via a control button interface, while in manual procedures the tools are controlled through continuous movements of the surgeon's hands. Logistic regression analysis using a single motion metric was capable of classifying subjects by expertise with better than 90% accuracy. These objective and quantitative metrics that capture movement quality could be incorporated into future training protocols to provide detailed feedback on trainee performance.

We focus on learning robot objective functions from human guidance: specifically, from physical corrections provided by the person while the robot is acting. Objective functions are typically parametrized in terms of features, which capture aspects of the task that might be important. When the person intervenes to correct the robot»s behavior, the robot should update its understanding of which features matter, how much, and in what way. Unfortunately, real users do not provide optimal corrections that isolate exactly what the robot was doing wrong. Thus, when receiving a correction, it is difficult for the robot to determine which features the person meant to correct, and which features were changed unintentionally. In this paper, we propose to improve the efficiency of robot learning during physical interactions by reducing unintended learning. Our approach allows the human-robot team to focus on learning one feature at a time, unlike state-of-the-art techniques that update all features at once. We derive an online method for identifying the single feature which the human is trying to change during physical interaction, and experimentally compare this one-at-a-time approach to the all-at-once baseline in a user study. Our results suggest that users teaching one-at-a-time perform better, especially in tasks that require changing multiple features.

In our daily lives, we rely heavily on our visual and auditory channels to receive information from others. In the case of impairment, or when large amounts of information are already transmitted visually or aurally, alternative methods of communication are needed. A haptic language offers the potential to provide information to a user when visual and auditory channels are unavailable. Previously created haptic languages include deconstructing acoustic signals into features and displaying them through a haptic device, and haptic adaptations of Braille or Morse code; however, these approaches are unintuitive, slow at presenting language, or require a large surface area. We propose using a multi-sensory haptic device called MISSIVE, which can be worn on the upper arm and is capable of producing brief cues, sufficient in quantity to encode the full English phoneme set. We evaluated our approach by teaching subjects a subset of 23 phonemes, and demonstrated an 86% accuracy in a 50 word identification task after 100 minutes of training.

Robots are finding new applications where physical interaction with a human is necessary, such as manufacturing, healthcare, and social tasks. Accordingly, the field of physical human-robot interaction (pHRI) has leveraged impedance control approaches, which support compliant interactions between human and robot. However, a limitation of traditional impedance control is that-despite provisions for the human to modify the robot's current trajectory-the human cannot affect the robot's future desired trajectory through pHRI. In this paper, we present an algorithm for physically interactive trajectory deformations which, when combined with impedance control, allows the human to modulate both the actual and desired trajectories of the robot. Unlike related works, our method explicitly deforms the future desired trajectory based on forces applied during pHRI, but does not require constant human guidance. We present our approach and verify that this method is compatible with traditional impedance control. Next, we use constrained optimization to derive the deformation shape. Finally, we describe an algorithm for real-time implementation, and perform simulations to test the arbitration parameters. Experimental results demonstrate reduction in the human's effort and improvement in the movement quality when compared to pHRI with impedance control alone.

There is currently a scarcity of wearable robotic devices that can practically provide physical assistance in a range of real world activities. Soft wearable exoskeletons, or exosuits, have the potential to be more portable and less restrictive than their rigid counterparts. In this paper, we present the design of an actuation system that has been optimized for use in a soft exosuit for the human arm. The selected design comprises a DC motor and gearbox, a flexible cable conduit transmission, and a custom series elastic force sensor. Placed in series with the transmission conduit, the custom compliant force sensor consists of a translational steel compression spring with a pair of Hall effect sensors for measuring deflection. The custom sensor is validated as an accurate means of measuring cable tension, and it is shown that it can be used in feedback to control the cable tension with high bandwidth. The dynamic effect of the cable-conduit transmission on the force felt at the user interface is characterized by backdriving the system as it renders a range of virtual impedances to the user. We conclude with recommendations for the integration of such an actuation system into a full wearable exosuit.

Rigid haptic devices enable humans to physically interact with virtual environments, and the range of impedances that can be safely rendered using these rigid devices is quantified by the Z-Width metric. Series elastic actuators (SEAs) similarly modulate the impedance felt by the human operator when interacting with a robotic device, and, in particular, the robot's perceived stiffness can be controlled by changing the elastic element's equilibrium position. In this paper, we explore the K-Width of SEAs, while specifically focusing on how discretization inherent in the computer-control architecture affects the system's passivity. We first propose a hybrid model for a single degree-of-freedom (DoF) SEA based on prior hybrid models for rigid haptic systems. Next, we derive a closed-form bound on the K-Width of SEAs that is a generalization of known constraints for both rigid haptic systems and continuous time SEA models. This bound is first derived under a continuous time approximation, and is then numerically supported with discrete time analysis. Finally, experimental results validate our finding that large pure masses are the most destabilizing operator in human-SEA interactions, and demonstrate the accuracy of our theoretical K-Width bound.

Current performance assessment techniques in endovascular surgery are subjective or limited to grading scales based solely on an expert's observation of a novice's task execution. Since most endovascular procedures involve performing fine motor control tasks that require complex dexterous movements, this paper evaluates objective and quantitative metrics of performance that capture movement quality through the computation of tool tip movement smoothness. An experiment was designed that involved recording the catheter tip movement from 20 subjects performing four fundamental endovascular tasks in each of three sessions using manual catheterization on a physical model and in a simulation environment. Several motion-based performance measures that have been shown to reliably assess skill in other domains were computed and tested for correlation with subjective data that were simultaneously obtained from the global rating scale assessment tool. Metrics that captured movement smoothness produced statistically significant correlations with the observation-based assessment metrics and were able to differentiate skill among participants. In particular, submovement analysis led to metrics that captured statistically significant differences across ability group, session, experimental platform, and task. Objective and quantitative metrics that capture movement smoothness could be incorporated into future training protocols to provide detailed feedback on trainee performance.

Robotic rehabilitation of the upper limb following neurological injury is most successful when subjects are engaged in the rehabilitation protocol. Developing assistive control strategies that maximize subject participation is accordingly an active area of research, with aims to promote neural plasticity and, in turn, increase the potential for recovery of motor coordination. Unfortunately, state-of-the-art control strategies either ignore more complex subject capabilities or assume underlying patterns govern subject behavior and may therefore intervene suboptimally. In this paper, we present a minimal assist-as-needed (mAAN) controller for upper limb rehabilitation robots. The controller employs sensorless force estimation to dynamically determine subject inputs without any underlying assumptions as to the nature of subject capabilities and computes a corresponding assistance torque with adjustable ultimate bounds on position error. Our adaptive input estimation scheme is shown to yield fast, stable, and accurate measurements regardless of subject interaction and exceeds the performance of current approaches that estimate only position-dependent force inputs from the user. Two additional algorithms are introduced in this paper to further promote active participation of subjects with varying degrees of impairment. First, a bound modification algorithm is described, which alters allowable error. Second, a decayed disturbance rejection algorithm is presented, which encourages subjects who are capable of leading the reference trajectory. The mAAN controller and accompanying algorithms are demonstrated experimentally with healthy subjects in the RiceWrist-S exoskeleton.

The use of haptic display to refer cues sensed electronically from a prosthetic terminal device promises to improve the function of myoelectrically controlled upper limb prostheses. This promise is often evaluated in experiments involving non-amputees, though the availability of additional haptic feedback from an intact hand (auxiliary feedback) may confound attempts to use non-amputees as stand-ins for amputees. In this paper we test the influence of auxiliary haptic feedback on myoelectric control performance by introducing various grasp conditions in a compensatory tracking task. We ask non-amputees to compensate for the motion of a random signal by producing myoelectric control signals with a hard object, soft object, or no object (requiring co-contraction) in their grasp. The error signal is displayed through a squeeze band worn about the upper arm or a visual display. Our results suggest that the main difference between tracking with haptic and visual feedback is low-frequency drift, and that auxiliary feedback does not substantially influence task performance. Despite the drift, our results show that participants are able to respond to cues presented through the squeeze band in the compensatory tracking task.

Robotic devices have been shown to be efficacious in the delivery of therapy to treat upper limb motor impairment following stroke. However, the application of this technology to other types of neurological injury has been limited to case studies. In this paper, we present a multi degree of freedom robotic exoskeleton, the MAHI Exo II, intended for rehabilitation of the upper limb following incomplete spinal cord injury (SCI). We present details about the MAHI Exo II and initial findings from a clinical evaluation of the device with eight subjects with incomplete SCI who completed a multi-session training protocol. Clinical assessments show significant gains when comparing pre- and post-training performance in functional tasks. This paper explores a range of robotic measures capturing movement quality and smoothness that may be useful in tracking performance, providing as feedback to the subject, or incorporating into an adaptive training protocol. Advantages and disadvantages of the various investigated measures are discussed with regard to the type of movement segmentation that can be applied to the data collected during unassisted movements where the robot is backdriven and encoder data is recorded for post-processing.

The fidelity with which series elastic actuators (SEAs) render desired impedances is important. Numerous approaches to SEA impedance control have been developed under the premise that high-precision actuator torque control is a prerequisite. Indeed, the design of an inner torque compensator has a significant impact on actuator impedance rendering. The disturbance observer (DOB) based torque control implemented in NASA's Valkyrie robot is considered here and a mathematical model of this torque control, cascaded with an outer impedance compensator, is constructed. While previous work has examined the impact a disturbance observer has on torque control performance, little has been done regarding DOBs and impedance rendering accuracy. Both simulation and a series of experiments are used to demonstrate the significant improvements possible in an SEA's ability to render desired dynamic behaviors when utilizing a DOB. Actuator transparency at low impedances is improved, closed loop hysteresis is reduced, and the actuator's dynamic response to both commands and interaction torques more faithfully matches that of the desired model. All of this is achieved by leveraging DOB based control rather than increasing compensator gains, thus making improved SEA impedance control easier to achieve in practice.

A key competency requirement in endovascular surgery is to optimally visualize and utilize pre-shaped catheters to navigate complex vascular anatomy, yet current performance assessment techniques are limited to grading scales based solely on observation. Since most endovascular procedures involve performing fine motor control tasks that require complex, dexterous movements, this paper explores the potential for a standardized, objective, and quantitative means of measuring technical competence based on analysis of the kinematics of endovascular tool tip motions. To accomplish this goal, we recorded catheter tip movement from twenty subjects performing fundamental endovascular tasks in an inanimate model and in a simulation environment with a virtual representation of the same inanimate model. Several motion-based performance measures that have been shown to reliably assess skill in other domains were computed and tested for correlation with data that were obtained from the global rating scale assessment tool. The metrics that quantified movement quality by assessing movement smoothness produced reliable correlations with the observation-based assessment metrics. These objective and quantitative metrics that capture movement quality could be incorporated into future training protocols to provide detailed feedback on trainee performance.

Objective assessment of skill is important in understanding the human performance of precision motor control tasks; however, outcome-based performance measures are most commonly used to provide feedback after the task is completed. In this paper, we propose the use of motion-based performance metrics in order to determine successful task completion strategies for a complex and unconstrained dynamic task. We developed a novel motor control task in a virtual environment and identified two motion-based metrics (mean absolute jerk and average frequency) that correlate with the successful performance of the task measured as a function of completion time and acquired targets. Our methodologies provide insight into the movement strategies used during successful trials of the motor control task, and could be used to provide specific feedback and coaching aimed at altering the task completion strategy and boosting the participants’ performance.

Achieving dexterous volitional control of an upper-limb prosthetic device will require multimodal sensory feedback that goes beyond vision. Haptic display is well-positioned to provide this additional sensory information. Haptic display, however, includes a diverse set of modalities that encode information differently. We have begun to make a comparison between two of these modalities, force feedback spanning the elbow, and amplitude-modulated vibrotactile feedback, based on performance in a functional grasp and lift task. In randomly ordered trials, we assessed the performance of N=11 participants (8 able-bodied, 3 amputee) attempting to grasp and lift an object using an EMG controlled gripper under three feedback conditions (no feedback, vibrotactile feedback, and force feed-back), and two object weights that were undetectable by vision. Preliminary results indicate differences between able-bodied and amputee participants in coordination of grasp and lift forces. In addition, both force feedback and vibrotactile feedback contribute to significantly better task performance (fewer slips) and better adaptation following an unpredicted weight change. This suggests that the development and utilization of internal models for predictive control is more intuitive in the presence of haptic feedback.

Advanced prosthetic hands offer the promise of great dexterity; however, myoelectric control techniques, successful with low degree-of-freedom prosthetics, are often set aside by amputees due to the lack of important sensations of touch and effort experienced in the interaction between prosthetic hand and task. In this paper, we explore the efficacy of various modalities of feedback (visual, tactile, visual and tactile, and none) conveying proprioceptive information, specifically the error in joint angles between a desired and actual pose of a virtual prosthetic hand. Our analysis of performance in achieving and maintaining a desired prosthetic hand pose indicates a significant effect of feedback condition, with visual and visual+tactile outperforming tactile alone and a no-feedback condition. Further, the combination of tactile and visual feedback does not seem to have significant drawbacks over visual feedback alone. For tasks that rely on proprioception in the absence of visual feedback, or when attention must be focused elsewhere, we see a performance benefit to the inclusion of tactile cueing, with no lags in reaction times or requirements for increased effort measured by muscle activation.

Smart buildings promise to revolutionize the way we live. Applications ranging from climate control to fire management can have significant impact on the quality and cost of these services. However, smart buildings and any technology with direct effect on human safety and life must undergo extensive testing. Virtual testing by means of computer simulation can significantly reduce the cost of testing and, as a result, accelerate the development of novel applications. Unfortunately, building physically-accurate simulation codes can be labor intensive. To address this problem, we propose a framework for rapid, physically-accurate virtual testing. The proposed framework supports analytical modeling of both a discrete distributed system as well as the physical environment that hosts it. The discrete models supported are accurate enough to allow the automatic generation of a dedicated programming framework that will help the developer in the implementation of these systems. The physical environment models supported are equational specifications that are accurate enough to produce running simulation codes. Combined, these two frameworks enable simulating both active systems and physical environments. These simulations can be used to monitor the behavior and gather statistics about the performance of an application in the context of precise virtual experiments. To illustrate the approach, we present models of Heating, Ventilating and Air-Conditioning (HVAC) systems. Using these models, we construct virtual experiments that illustrate how the approach can be used to optimize energy and cost of climate control for a building.

Young engineers entering an industry job are likely to be immediately placed into a division or team that is comprised of people from a wide variety of educational backgrounds. This will include other engineering disciplines and, depending on the industry, individuals with business, marketing, public relations, policy or science backgrounds. This work in progress describes our initial efforts to implement and evaluate outcomes from collaboratively teaching capstone design and assigning interdisciplinary teams to solve real-world design challenges as part of the course. Initial data demonstrate that these teams are more successful than their single discipline counterparts in achieving outstanding success in their projects as indicated by a larger proportion of interdisciplinary teams earning design awards, both local and national.

In this paper, we present results from implementation of Levant's differentiator for velocity estimation from optical encoder readings. Levant's differentiator is a sliding mode control theory-based real-time differentiation algorithm proposed as a velocity estimator. The application of the technique allows stable implementation of higher stiffness virtual walls as compared to using the common finite difference method (FDM) cascaded with low-pass filters for velocity estimation. A single degree-of-freedom (DOF) linear haptic device is used as a test bed and an automated virtual wall hitting task is implemented to experimentally demonstrate that it is possible to extend the impedance-width (or Z-width) of a haptic interface via Levant's differentiator.

We explore the effects of parameters constituting a second order dynamic system on the rate of human motor adaptation while performing a rhythmic dynamic task. In our experiments, participants excite virtual second-order systems at resonance via a haptic interface. After overtraining subjects with a nominal system, we unexpectedly change the system parameters and study the resulting motor adaptation in catch trials. Through four experiment seatings, we demonstrate the effects of dynamic system parameters on human motor adaptation. Results indicate that gain and damping parameters significantly affect the rate of adaptation. In particular, as the effort required to complete the task increases, the rate of adaptation decreases, indicating a trade-off between task performance and the effort required to perform the task.

Robot-mediated training has seen a plethora of implementations of shared-control haptic guidance, intended to teach novices to perform dynamic tasks by providing them with real-time visual and haptic feedback from real or virtual experts. The efficacies of these paradigms are difficult to quantify and compare, as the paradigms have typically been developed in an ad-hoc manner to suit specific devices and tasks. This work proposes a novel guidance paradigm taxonomy intended to help classify and compare the multitude of implementations in the literature, as well as a revised proxy rendering model to allow for the implementation of two relatively novel guidance paradigms (in addition to existing paradigms). The efficacies of these two paradigms, plus two more paradigms representing the vast majority of implementations in the literature, are compared in a controlled study with 50 healthy subjects. The results show that none of these paradigms are superior to visual-only guidance (the control condition), and that the two newer paradigms are actually detrimental to training for a target-hitting task. These results contradict many intuitions about how haptic guidance should be implemented and raise doubts about the efficacies of the most commonly-implemented paradigms.

We describe a proof-of-concept design for a low-cost two-degree-of-freedom robotic arm that incorporates series elastic actuators (SEAs) with force sensing. The cost effectiveness of the design will enable the construction of compliant manipulators for multi-robot systems with large populations. The arm assembly attaches to a commercially available mobile robot chassis to perform multi-robot coordination. In this work, we present the design of a robot arm and data from experiments to characterize the accuracy and resolution of the force sensing. We describe a force-following manipulation experiment using two robots. The experiment measures strain on a rigid bar between two robots. The data shows the feasibility of using SEAs for force sensing to reduce the strain in the bar. This is the first step towards a distributed force controller for multi-robot object coordination with large numbers of robots.

Background: Upper limb weakness is the primary concern of tetraplegic individuals who have sustained incomplete spinal cord injury (SCI), to an extent that it is considered more important than standing abilities. Recent evidence of the plasticity of the brain and the spinal cord that can be enhanced by repeated practice- such as that available with robotic devices- suggest that robotic training of upper limbs can be beneficial to persons with SCI. The goal of this pilot study was to evaluate an innovative rehabilitation technique using the RiceWrist, a newly developed robotic device, for a person with tetraplegia. A 24-year-old male with incomplete SCI at the C4 level, 6.5 months post-injury participated in 10 sessions of robotic training over 2 weeks. Variability of movement trajectory (spatial) and the time to complete (temporal) simple point-to-point wrist and forearm movements were collected before and after training completion to determine skill acquisition. The participant successfully completed 10 sessions of robotic training. While there were minimal changes in variability of movement trajectory, great improvements were observed for the average movement time for the majority of wrist and forearm movements. Overall, results suggest that the RiceWrist robotic device could be used for upper-limb rehabilitation and can potentially serve as an assessment tool for the SCI population.

When analyzed in the tangential speed domain, human movements exhibit a multi-peaked speed profile which is commonly interpreted as evidence for submovements. At slow speeds, the number of the peaks increases and the peaks also become more distinct, corresponding to non-smoothness or intermittency in the movement. In this study, we evaluate two potential sources proposed in the literature for the origins of movement intermittency and conclude that intermittency is more likely due to noise in the neuromuscular system as opposed to a central movement planner that generates intermittent plans. This conclusion is based on the assumption that the central planner would be expected to introduce similar levels of intermittency for different joints, while accumulating noise in the neuromuscular circuitry would be expected to exhibit itself as increase in noise in distal direction. We have used a 3D motion capture system to record trajectories of fingertip, wrist, elbow and shoulder as five participants completed a simple manual circular tracking task at various constant speed levels. Statistical analyses indicated that movement intermittency, quantified by a number of peaks metric, increased in distal direction, supporting the noise model for origins of intermittency. Movement speed was determined to have a significant effect on intermittency, while orientation of the task plane showed no significance.

Virtual reality with haptic feedback provides a safe and versatile practice medium for many manual control tasks. Haptic guidance has been shown to improve performance of manual control tasks in virtual environments; however, the efficacy of haptic guidance for training in virtual environments has not been studied conclusively. This article presents experimental results that show negative efficacy of haptic guidance during training in virtual environments. The haptic guidance in this study is a fixed-gain error-reducing shared controller, with the control effort overlaid on the dynamics of the manual control task during training. Performance of the target-hitting manual control task in the absence of guidance is compared for three training protocols. One protocol contained no haptic guidance and represented virtual practice. Two protocols utilized haptic guidance, varying the duration of exposure to guidance during the training sessions. Exposure to the fixed-gain error-reducing shared controller had a detrimental effect on performance of the target-hitting task at the conclusion of a month-long training protocol, regardless of duration of exposure. While the shared controller was designed with knowledge of the task and an intuitive sense of the motions required to achieve good performance, the results indicate that the acquisition of motor skill is a complex phenomenon that is not aided with haptic guidance during training as implemented in this experiment.

In this paper, preliminary results in motor function improvement for four sub-acute stroke patients that underwent a hybrid robotic and traditional rehabilitation program are presented. The therapy program was scheduled for three days a week, four hours per day (approximately 60% traditional constraint induced therapy activities and 40% robotic therapy). A haptic joystick was used to implement four different operating modes for robotic therapy: unassisted (U), constrained (C), assisted (A), and resisted (R) modes. A target hitting task involving the positioning of a pointer on twelve targets was completed by the patients. Two different robotic measures were utilized to quantify the motor function improvement through the sessions: trajectory error (TE) and smoothness of movement (SM). Fugl-Meyer (FM) and motor activity log (MAL) scales were used as clinical measures. Analysis of results showed that the group demonstrates a significant motor function improvement with respect to both clinical and robotic measures. Regression analyses were carried out on corresponding clinical and robotic measure result pairs. A significant relation between FM scale and robotic measures was found for both of the analyzed modes. Regression of robotic measures on MAL scores resulted in no significance. A regression analysis that compared the two clinical measures revealed a very low agreement. Our findings suggest that it might be possible to obtain objective robotic measures that are significantly correlated to widely-used and reliable clinical measures in considerably different operating modes and control schemes.

This paper presents a two-part study of the effects of virtual surface stiffness on haptic perception. First, size identification experiments were performed to determine the effects of system quality, in terms of surface stiffness, on the ability of a human to identify square cross-section ridges by size in a simulated environment. Then, discrimination experiments were performed to determine relationships between virtual surface stiffness and simulation quality in terms of perceived surface hardness. Results of experiments to test human haptic perception for varying virtual surface stiffnesses indicate that haptic interface hardware may be able to convey sufficient perceptual information to the user at relatively low levels of virtual surface stiffness. Subjects, however, can perceive improvements in perceived simulated surface hardness as stiffness levels are increased in the range of achievable parameters for this hardware. The authors draw several conclusions about the allowable time delays in a haptic interface system based on the results of the surface stiffness experiments.

This paper presents a two-part study of the effects of maximum endpoint force and system bandwidth on haptic perception. First, size identification experiments were performed to determine the effects of system quality, in terms of these two system parameters, on the ability of a human to identify square cross-section ridges by size in a simulated environment. Then, discrimination experiments were performed to determine the relationships between haptic interface machine parameters and simulation quality in terms of perceived surface hardness. Results indicate that haptic interface hardware may be able to convey sufficient perceptual information to the user with relatively low levels of force feedback and system bandwidth, yet subjects can perceive improvements in simulated surface quality as levels are further increased.