Closing the loop in minimally supervised human-robot interaction: formative and summative feedback.

Human instructors fluidly communicate with hand gestures, head and body movements, and facial expressions, but robots rarely leverage these complementary cues. A minimally supervised social robot with such skills could help people exercise and learn new activities. Thus, we investigated how nonverbal feedback from a humanoid robot affects human behavior. Inspired by the education literature, we evaluated formative feedback (real-time corrections) and summative feedback (post-task scores) for three distinct tasks: positioning in the room, mimicking the robot's arm pose, and contacting the robot's hands. Twenty-eight adults completed seventy-five 30-s-long trials with no explicit instructions or experimenter help. Motion-capture data analysis shows that both formative and summative feedback from the robot significantly aided user performance. Additionally, formative feedback improved task understanding. These results show the power of nonverbal cues based on human movement and the utility of viewing feedback through formative and summative lenses.

Fingertip dynamic response simulated across excitation points and frequencies.

Predicting how the fingertip will mechanically respond to different stimuli can help explain human haptic perception and enable improvements to actuation approaches such as ultrasonic mid-air haptics. This study addresses this goal using high-fidelity 3D finite element analyses. We compute the deformation profiles and amplitudes caused by harmonic forces applied in the normal direction at four locations: the center of the finger pad, the side of the finger, the tip of the finger, and the oblique midpoint of these three sites. The excitation frequency is swept from 2.5 to 260 Hz. The simulated frequency response functions (FRFs) obtained for displacement demonstrate that the relative magnitudes of the deformations elicited by stimulating at each of these four locations greatly depend on whether only the excitation point or the entire finger is considered. The point force that induces the smallest local deformation can even cause the largest overall deformation at certain frequency intervals. Above 225 Hz, oblique excitation produces larger mean displacement amplitudes than the other three forces due to excitation of multiple modes involving diagonal deformation. These simulation results give novel insights into the combined influence of excitation location and frequency on the fingertip dynamic response, potentially facilitating the design of future vibration feedback devices.

IMU-Based Kinematics Estimation Accuracy Affects Gait Retraining Using Vibrotactile Cues.

Wearable sensing using inertial measurement units (IMUs) is enabling portable and customized gait retraining for knee osteoarthritis. However, the vibrotactile feedback that users receive directly depends on the accuracy of IMU-based kinematics. This study investigated how kinematic errors impact an individual's ability to learn a therapeutic gait using vibrotactile cues. Sensor accuracy was computed by comparing the IMU-based foot progression angle to marker-based motion capture, which was used as ground truth. Thirty subjects were randomized into three groups to learn a toe-in gait: one group received vibrotactile feedback during gait retraining in the laboratory, another received feedback outdoors, and the control group received only verbal instruction and proceeded directly to the evaluation condition. All subjects were evaluated on their ability to maintain the learned gait in a new outdoor environment. We found that subjects with high tracking errors exhibited more incorrect responses to vibrotactile cues and slower learning rates than subjects with low tracking errors. Subjects with low tracking errors outperformed the control group in the evaluation condition, whereas those with higher error did not. Errors were correlated with foot size and angle magnitude, which may indicate a non-random bias. The accuracy of IMU-based kinematics has a cascading effect on feedback; ignoring this effect could lead researchers or clinicians to erroneously classify a patient as a non-responder if they did not improve after retraining. To use patient and clinician time effectively, future implementation of portable gait retraining will require assessment across a diverse range of patients.

Robust Surface Recognition With the Maximum Mean Discrepancy: Degrading Haptic-Auditory Signals Through Bandwidth and Noise.

Sliding a tool across a surface generates rich sensations that can be analyzed to recognize what is being touched. However, the optimal configuration for capturing these signals is yet unclear. To bridge this gap, we consider haptic-auditory data as a human explores surfaces with different steel tools, including accelerations of the tool and finger, force and torque applied to the surface, and contact sounds. Our classification pipeline uses the maximum mean discrepancy (MMD) to quantify differences in data distributions in a high-dimensional space for inference. With recordings from three hemispherical tool diameters and ten diverse surfaces, we conducted two degradation studies by decreasing sensing bandwidth and increasing added noise. We evaluate the haptic-auditory recognition performance achieved with the MMD to compare newly gathered data to each surface in our known library. The results indicate that acceleration signals alone have great potential for high-accuracy surface recognition and are robust against noise contamination. The optimal accelerometer bandwidth exceeds 1000 Hz, suggesting that useful vibrotactile information extends beyond human perception range. Finally, smaller tool tips generate contact vibrations with better noise robustness. The provided sensing guidelines may enable superhuman performance in portable surface recognition, which could benefit quality control, material documentation, and robotics.

Effects of automated skill assessment on robotic surgery training.

BACKGROUND: Several automated skill-assessment approaches have been proposed for robotic surgery, but their utility is not well understood. This article investigates the effects of one machine-learning-based skill-assessment approach on psychomotor skill development in robotic surgery training. METHODS: N = 29 trainees (medical students and residents) with no robotic surgery experience performed five trials of inanimate peg transfer with an Intuitive Surgical da Vinci Standard robot. Half of the participants received no post-trial feedback. The other half received automatically calculated scores from five Global Evaluative Assessment of Robotic Skill domains post-trial. RESULTS: There were no significant differences between the groups regarding overall improvement or skill improvement rate. However, participants who received post-trial feedback rated their overall performance improvement significantly lower than participants who did not receive feedback. CONCLUSIONS: These findings indicate that automated skill evaluation systems might improve trainee self-awareness but not accelerate early stage psychomotor skill development in robotic surgery training.

Generating Clear Vibrotactile Cues with a Magnet Embedded in a Soft Finger Sheath.

Haptic displays act on the user's body to stimulate the sense of touch and enrich applications from gaming and computer-aided design to rehabilitation and remote surgery. However, when crafted from typical rigid robotic components, they tend to be heavy, bulky, and expensive, while sleeker designs often struggle to create clear haptic cues. This article introduces a lightweight wearable silicone finger sheath that can deliver salient and rich vibrotactile cues using electromagnetic actuation. We fabricate the sheath on a ferromagnetic mandrel with a process based on dip molding, a robust fabrication method that is rarely used in soft robotics but is suitable for commercial production. A miniature rare-earth magnet embedded within the silicone layers at the center of the finger pad is driven to vibrate by the application of alternating current to a nearby air-coil. Experiments are conducted to determine the amplitude of the magnetic force and the frequency response function for the displacement amplitude of the magnet perpendicular to the skin. In addition, high-fidelity finite element analyses of the finger wearing the device are performed to investigate the trends observed in the measurements. The experimental and simulated results show consistent dynamic behavior from 10 to 1000 Hz, with the displacement decreasing after about 300 Hz. These results match the detection threshold profile obtained in a psychophysical study performed by 17 users, where more current was needed only at the highest frequency. A cue identification experiment and a demonstration in virtual reality validate the feasibility of this approach to fingertip haptics.

The Utility of Synthetic Reflexes and Haptic Feedback for Upper-Limb Prostheses in a Dexterous Task Without Direct Vision.

Individuals who use myoelectric upper-limb prostheses often rely heavily on vision to complete their daily activities. They thus struggle in situations where vision is overloaded, such as multitasking, or unavailable, such as poor lighting conditions. Non-disabled individuals can easily accomplish such tasks due to tactile reflexes and haptic sensation guiding their upper-limb motor coordination. Based on these principles, we developed and tested two novel prosthesis systems that incorporate autonomous controllers and provide the user with touch-location feedback through either vibration or distributed pressure. These capabilities were made possible by installing a custom contact-location sensor on the fingers of a commercial prosthetic hand, along with a custom pressure sensor on the thumb. We compared the performance of the two systems against a standard myoelectric prosthesis and a myoelectric prosthesis with only autonomous controllers in a difficult reach-to-pick-and-place task conducted without direct vision. Results from 40 non-disabled participants in this between-subjects study indicated that vibrotactile feedback combined with synthetic reflexes proved significantly more advantageous than the standard prosthesis in several of the task milestones. In addition, vibrotactile feedback and synthetic reflexes improved grasp placement compared to only synthetic reflexes or pressure feedback combined with synthetic reflexes. These results indicate that autonomous controllers and haptic feedback together facilitate success in dexterous tasks without vision, and that the type of haptic display matters.

How should robots exercise with people? Robot-mediated exergames win with music, social analogues, and gameplay clarity.

Introduction: The modern worldwide trend toward sedentary behavior comes with significant health risks. An accompanying wave of health technologies has tried to encourage physical activity, but these approaches often yield limited use and retention. Due to their unique ability to serve as both a health-promoting technology and a social peer, we propose robots as a game-changing solution for encouraging physical activity. Methods: This article analyzes the eight exergames we previously created for the Rethink Baxter Research Robot in terms of four key components that are grounded in the video-game literature: repetition, pattern matching, music, and social design. We use these four game facets to assess gameplay data from 40 adult users who each experienced the games in balanced random order. Results: In agreement with prior research, our results show that relevant musical cultural references, recognizable social analogues, and gameplay clarity are good strategies for taking an otherwise highly repetitive physical activity and making it engaging and popular among users. Discussion: Others who study socially assistive robots and rehabilitation robotics can benefit from this work by considering the presented design attributes to generate future hypotheses and by using our eight open-source games to pursue follow-up work on social-physical exercise with robots.

Learning to Feel Textures: Predicting Perceptual Similarities From Unconstrained Finger-Surface Interactions.

Whenever we touch a surface with our fingers, we perceive distinct tactile properties that are based on the underlying dynamics of the interaction. However, little is known about how the brain aggregates the sensory information from these dynamics to form abstract representations of textures. Earlier studies in surface perception all used general surface descriptors measured in controlled conditions instead of considering the unique dynamics of specific interactions, reducing the comprehensiveness and interpretability of the results. Here, we present an interpretable modeling method that predicts the perceptual similarity of surfaces by comparing probability distributions of features calculated from short time windows of specific physical signals (finger motion, contact force, fingernail acceleration) elicited during unconstrained finger-surface interactions. The results show that our method can predict the similarity judgments of individual participants with a maximum Spearman's correlation of 0.7. Furthermore, we found evidence that different participants weight interaction features differently when judging surface similarity. Our findings provide new perspectives on human texture perception during active touch, and our approach could benefit haptic surface assessment, robotic tactile perception, and haptic rendering.

Contact evolution of dry and hydrated fingertips at initial touch.

Pressing the fingertips into surfaces causes skin deformations that enable humans to grip objects and sense their physical properties. This process involves intricate finger geometry, non-uniform tissue properties, and moisture, complicating the underlying contact mechanics. Here we explore the initial contact evolution of dry and hydrated fingers to isolate the roles of governing physical factors. Two participants gradually pressed an index finger on a glass surface under three moisture conditions: dry, water-hydrated, and glycerin-hydrated. Gross and real contact area were optically measured over time, revealing that glycerin hydration produced strikingly higher real contact area, while gross contact area was similar for all conditions. To elucidate the causes for this phenomenon, we investigated the combined effects of tissue elasticity, skin-surface friction, and fingerprint ridges on contact area using simulation. Our analyses show the dominant influence of elastic modulus over friction and an unusual contact phenomenon, which we call friction-induced hinging.

Normal and tangential forces combine to convey contact pressure during dynamic tactile stimulation.

Humans need to accurately process the contact forces that arise as they perform everyday haptic interactions such as sliding the fingers along a surface to feel for bumps, sticky regions, or other irregularities. Several different mechanisms are possible for how the forces on the skin could be represented and integrated in such interactions. In this study, we used a force-controlled robotic platform and simultaneous ultrasonic modulation of the finger-surface friction to independently manipulate the normal and tangential forces during passive haptic stimulation by a flat surface. To assess whether the contact pressure on their finger had briefly increased or decreased during individual trials in this broad stimulus set, participants did not rely solely on either the normal force or the tangential force. Instead, they integrated tactile cues induced by both components. Support-vector-machine analysis classified physical trial data with up to 75% accuracy and suggested a linear perceptual mechanism. In addition, the change in the amplitude of the force vector predicted participants' responses better than the change of the coefficient of dynamic friction, suggesting that intensive tactile cues are meaningful in this task. These results provide novel insights about how normal and tangential forces shape the perception of tactile contact.

Perceptual Space of Algorithms for Three-to-One Dimensional Reduction of Realistic Vibrations.

Haptics researchers often endeavor to deliver realistic vibrotactile feedback through broad-bandwidth actuators; however, these actuators typically generate only single-axis vibrations, not 3D vibrations like those that occur in natural tool-mediated interactions. Several three-to-one (321) dimensional reduction algorithms have thus been developed to combine 3D vibrations into 1D vibrations. Surprisingly, the perceptual quality of 321-converted vibrations has never been comprehensively compared to rendering of the original 3D signals. In this study, we develop a multi-dimensional vibration rendering system using a magnetic levitation haptic interface. We verify the system's ability to generate realistic 3D vibrations recorded in both tapping and dragging interactions with four surfaces. We then conduct a study with 15 participants to measure the perceived dissimilarities between five 321 algorithms (SAZ, SUM, VM, DFT, PCA) and the original recordings. The resulting perceptual space is investigated with multiple regression and Procrustes analysis to unveil the relationship between the physical and perceptual properties of 321-converted vibrations. Surprisingly, we found that participants perceptually discriminated the original 3D vibrations from all tested 1D versions. Overall, our results indicate that spectral, temporal, and directional attributes may all contribute to the perceived similarities of vibration signals.

Predicting knee adduction moment response to gait retraining with minimal clinical data.

Knee osteoarthritis is a progressive disease mediated by high joint loads. Foot progression angle modifications that reduce the knee adduction moment (KAM), a surrogate of knee loading, have demonstrated efficacy in alleviating pain and improving function. Although changes to the foot progression angle are overall beneficial, KAM reductions are not consistent across patients. Moreover, customized interventions are time-consuming and require instrumentation not commonly available in the clinic. We present a regression model that uses minimal clinical data-a set of six features easily obtained in the clinic-to predict the extent of first peak KAM reduction after toe-in gait retraining. For such a model to generalize, the training data must be large and variable. Given the lack of large public datasets that contain different gaits for the same patient, we generated this dataset synthetically. Insights learned from a ground-truth dataset with both baseline and toe-in gait trials (N = 12) enabled the creation of a large (N = 138) synthetic dataset for training the predictive model. On a test set of data collected by a separate research group (N = 15), the first peak KAM reduction was predicted with a mean absolute error of 0.134% body weight * height (%BW*HT). This error is smaller than the standard deviation of the first peak KAM during baseline walking averaged across test subjects (0.306%BW*HT). This work demonstrates the feasibility of training predictive models with synthetic data and provides clinicians with a new tool to predict the outcome of patient-specific gait retraining without requiring gait lab instrumentation.

Endowing a NAO Robot With Practical Social-Touch Perception.

Social touch is essential to everyday interactions, but current socially assistive robots have limited touch-perception capabilities. Rather than build entirely new robotic systems, we propose to augment existing rigid-bodied robots with an external touch-perception system. This practical approach can enable researchers and caregivers to continue to use robotic technology they have already purchased and learned about, but with a myriad of new social-touch interactions possible. This paper presents a low-cost, easy-to-build, soft tactile-perception system that we created for the NAO robot, as well as participants' feedback on touching this system. We installed four of our fabric-and-foam-based resistive sensors on the curved surfaces of a NAO's left arm, including its hand, lower arm, upper arm, and shoulder. Fifteen adults then performed five types of affective touch-communication gestures (hitting, poking, squeezing, stroking, and tickling) at two force intensities (gentle and energetic) on the four sensor locations; we share this dataset of four time-varying resistances, our sensor patterns, and a characterization of the sensors' physical performance. After training, a gesture-classification algorithm based on a random forest identified the correct combined touch gesture and force intensity on windows of held-out test data with an average accuracy of 74.1%, which is more than eight times better than chance. Participants rated the sensor-equipped arm as pleasant to touch and liked the robot's presence significantly more after touch interactions. Our promising results show that this type of tactile-perception system can detect necessary social-touch communication cues from users, can be tailored to a variety of robot body parts, and can provide HRI researchers with the tools needed to implement social touch in their own systems.

Evaluation of Vibrotactile Output From a Rotating Motor Actuator.

Specialized vibrotactile actuators are widely used to output haptic sensations due to their portability and robustness; some models are expensive and capable, while others are economical but weaker and less expressive. To increase the accessibility of high-quality haptics, we designed a cost-effective actuation approach called the rotating motor actuator (RMA): it uses a small DC motor to generate vibrotactile cues on a rigid stylus. We conducted a psychophysical experiment where eighteen volunteers matched the RMA's vibration amplitudes with those from a high-quality reference actuator (Haptuator Mark II) at twelve frequencies from 50 Hz to 450 Hz. The average error in matching acceleration magnitudes was 10.2%. More current was required for the RMA than the reference actuator; a stronger DC motor would require less current. Participants also watched a video of a real tool-mediated interaction with playback of recorded vibrotactile cues from each actuator. 94.4% of the participants agreed that the RMA delivered realistic vibrations and audio cues during this replay. 83.3% reported that the RMA vibrations were pleasant, compared to 66.7% for the reference. A possible cause for this significant difference may be that the reference actuator (which has a mechanical resonance) distorts low-frequency vibrations more than the RMA does.

Design of interactive augmented reality functions for robotic surgery and evaluation in dry-lab lymphadenectomy.

BACKGROUND: Augmented reality (AR) has been widely researched for use in healthcare. Prior AR for robot-assisted minimally invasive surgery has mainly focussed on superimposing preoperative three-dimensional (3D) images onto patient anatomy. This article presents alternative interactive AR tools for robotic surgery. METHODS: We designed, built and evaluated four voice-controlled functions: viewing a live video of the operating room, viewing two-dimensional preoperative images, measuring 3D distances and warning about out-of-view instruments. This low-cost system was developed on a da Vinci Si, and it can be integrated into surgical robots equipped with a stereo camera and a stereo viewer. RESULTS: Eight experienced surgeons performed dry-lab lymphadenectomies and reported that the functions improved the procedure. They particularly appreciated the possibility of accessing the patient's medical records on demand, measuring distances intraoperatively and interacting with the functions using voice commands. CONCLUSIONS: The positive evaluations garnered by these alternative AR functions and interaction methods provide support for further exploration.

A Brake-Based Overground Gait Rehabilitation Device for Altering Propulsion Impulse Symmetry.

This paper introduces a new device for gait rehabilitation, the gait propulsion trainer (GPT). It consists of two main components (a stationary device and a wearable system) that work together to apply periodic stance-phase resistance as the user walks overground. The stationary device provides the resistance forces via a cable that tethers the user's pelvis to a magnetic-particle brake. The wearable system detects gait events via foot switches to control the timing of the resistance forces. A hardware verification test confirmed that the GPT functions as intended. We conducted a pilot study in which one healthy adult and one stroke survivor walked with the GPT with increasing resistance levels. As hypothesized, the periodic stance-phase resistance caused the healthy participant to walk asymmetrically, with greatly reduced propulsion impulse symmetry; as GPT resistance increased, the walking speed also decreased, and the propulsion impulse appeared to increase for both legs. In contrast, the stroke participant responded to GPT resistance by walking faster and more symmetrically in terms of both propulsion impulse and step length. Thus, this paper shows promising results of short-term training with the GPT, and more studies will follow to explore its long-term effects on hemiparetic gait.

Virtual Reality Treatment Displaying the Missing Leg Improves Phantom Limb Pain: A Small Clinical Trial.

BACKGROUND: Phantom limb pain (PLP) is a common and in some cases debilitating consequence of upper- or lower-limb amputation for which current treatments are inadequate. OBJECTIVE: This small clinical trial tested whether game-like interactions with immersive VR activities can reduce PLP in subjects with transtibial lower-limb amputation. METHODS: Seven participants attended 5-7 sessions in which they engaged in a visually immersive virtual reality experience that did not require leg movements (Cool! TM), followed by 10-12 sessions of targeted lower-limb VR treatment consisting of custom games requiring leg movement. In the latter condition, they controlled an avatar with 2 intact legs viewed in a head-mounted display (HTC Vive TM). A motion-tracking system mounted on the intact and residual limbs controlled the movements of both virtual extremities independently. RESULTS: All participants except one experienced a reduction of pain immediately after VR sessions, and their pre session pain levels also decreased over the course of the study. At a group level, PLP decreased by 28% after the treatment that did not include leg movements and 39.6% after the games requiring leg motions. Both treatments were successful in reducing PLP. CONCLUSIONS: This VR intervention appears to be an efficacious treatment for PLP in subjects with lower-limb amputation.

Optimizing a Viscoelastic Finite Element Model to Represent the Dry, Natural, and Moist Human Finger Pressing on Glass.

When a fingerpad presses into a hard surface, the development of the contact area depends on the pressing force and speed. Importantly, it also varies with the finger's moisture, presumably because hydration changes the tissue's material properties. Therefore, we collected data from one finger repeatedly pressing a glass plate under three moisture conditions, and we constructed a finite element model that we optimized to simulate the same three scenarios. We controlled the moisture of the subject's finger to be dry, natural, or moist and recorded 15 pressing trials in each condition. The measurements include normal force over time plus finger-contact images that are processed to yield gross contact area. We defined the axially symmetric 3D model's lumped parameters to include an SLS-Kelvin model (spring in series with parallel spring and damper) for the bulk tissue, plus an elastic epidermal layer. Particle swarm optimization was used to find the parameter values that cause the simulation to best match the trials recorded in each moisture condition. The results show that the softness of the bulk tissue reduces as the finger becomes more hydrated. The epidermis of the moist finger model is softest, while the natural finger model has the highest viscosity.

Finger motion and contact by a second finger influence the tactile perception of electrovibration.

Electrovibration holds great potential for creating vivid and realistic haptic sensations on touchscreens. Ideally, a designer should be able to control what users feel independent of the number of fingers they use, the movements they make, and how hard they press. We sought to understand the perception and physics of such interactions by determining the smallest 125 Hz electrovibration voltage that 15 participants could reliably feel when performing four different touch interactions at two normal forces. The results proved for the first time that both finger motion and contact by a second finger significantly affect what the user feels. At a given voltage, a single moving finger experiences much larger fluctuating electrovibration forces than a single stationary finger, making electrovibration much easier to feel during interactions involving finger movement. Indeed, only about 30% of participants could detect the stimulus without motion. Part of this difference comes from the fact that relative motion greatly increases the electrical impedance between a finger and the screen, as shown via detailed measurements from one individual. By contrast, threshold-level electrovibration did not significantly affect the coefficient of kinetic friction in any conditions. These findings help lay the groundwork for delivering consistent haptic feedback via electrovibration.