Simulation training with haptic feedback of instrument vibrations reduces resident workload during live robot-assisted sleeve gastrectomy.

Background: New surgeons experience heavy workload during robot-assisted surgery partially because they must use vision to compensate for the lack of haptic feedback. We hypothesize that providing realistic haptic feedback during dry-lab simulation training may accelerate learning and reduce workload during subsequent surgery on patients.

Methods: We conducted a single-blinded study with 12 general surgery residents (third and seventh post-graduate year, PGY) randomized into haptic and control groups.

Cutaneous Electrohydraulic (CUTE) Wearable Devices for Pleasant Broad-Bandwidth Haptic Cues.

By focusing on vibrations, current wearable haptic devices underutilize the skin's perceptual capabilities. Devices that provide richer haptic stimuli, including contact feedback and/or variable pressure, are typically heavy and bulky due to the underlying actuator technology and the low sensitivity of hairy skin, which covers most of the body. This article presents a system architecture for compact wearable devices that deliver salient and pleasant broad-bandwidth haptic cues: Cutaneous Electrohydraulic (CUTE) devices combine a custom materials design for soft haptic electrohydraulic actuators that feature high stroke, high force, and electrical safety with a comfortable mounting strategy that places the actuator in a non-contact resting position.

AiroTouch: enhancing telerobotic assembly through naturalistic haptic feedback of tool vibrations.

Teleoperation allows workers to safely control powerful construction machines; however, its primary reliance on visual feedback limits the operator's efficiency in situations with stiff contact or poor visibility, hindering its use for assembly of pre-fabricated building components. Reliable, economical, and easy-to-implement haptic feedback could fill this perception gap and facilitate the broader use of robots in construction and other application areas. Thus, we adapted widely available commercial audio equipment to create AiroTouch, a naturalistic haptic feedback system that measures the vibration experienced by each robot tool and enables the operator to feel a scaled version of this vibration in real time.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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! ), followed by 10-12 sessions of targeted lower-limb VR treatment consisting of custom games requiring leg movement.

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.

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.

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.

Physical Variables Underlying Tactile Stickiness During Fingerpad Detachment.

One may notice a relatively wide range of tactile sensations even when touching the same hard, flat surface in similar ways. Little is known about the reasons for this variability, so we decided to investigate how the perceptual intensity of light stickiness relates to the physical interaction between the skin and the surface. We conducted a psychophysical experiment in which nine participants actively pressed their finger on a flat glass plate with a normal force close to 1.

Learning to Predict Perceptual Distributions of Haptic Adjectives.

When humans touch an object with their fingertips, they can immediately describe its tactile properties using haptic adjectives, such as hardness and roughness; however, human perception is subjective and noisy, with significant variation across individuals and interactions. Recent research has worked to provide robots with similar haptic intelligence but was focused on identifying binary haptic adjectives, ignoring both attribute intensity and perceptual variability. Combining ordinal haptic adjective labels gathered from human subjects for a set of 60 objects with features automatically extracted from raw multi-modal tactile data collected by a robot repeatedly touching the same objects, we designed a machine-learning method that incorporates partial knowledge of the distribution of object labels into training; then, from a single interaction, it predicts a probability distribution over the set of ordinal labels.