Continuous Force Feedback Research Articles

SmartSpring: a Low-Cost Wearable Haptic VR Display with Controllable Passive Feedback.

With the development of virtual reality, the practical requirements of the wearable haptic interface have been greatly emphasized. While passive haptic devices are commonly used in virtual reality, they lack generality and are difficult to precisely generate continuous force feedback to users. In this work, we present SmartSpring, a new solution for passive haptics, which is inexpensive, lightweight and capable of providing controllable force feedback in virtual reality. We propose a hybrid spring-linkage structure as the proxy and flexibly control the mechanism for adjustable system stiffness. By analyzing the structure and force model, we enable a smart transform of the structure for producing continuous force signals. We quantitatively examine the real-world performance of SmartSpring to verify our model. By asymmetrically moving or actively pressing the end-effector, we show that our design can further support rendering torque and stiffness. Finally, we demonstrate the SmartSpring in a series of scenarios with user studies and a just noticeable difference analysis. Experimental results show the potential of the developed haptic display in virtual reality.

Experiment and Study of Garlic Root Cutting Based on Continuous Force Feedback

In this study, we quantified and analyzed the root-cutting process of garlic with a test bench with pressure sensors on the basis of the comparative analysis of various information perception methods. On the basis of the output value of the pressure sensor, the force curve of garlic roots was plotted, and the double round blade cutting module is optimized on the basis of the force curve diagram. The innovative proposal of slotted round blades for garlic root cutting is presented here. The round blade diameter is 110 mm, the center distance is 100 mm, the blade thickness is 1 mm, and the blade speed is 1200 r/min. According to the analysis of the force curve, it was found that the slotted round blade with the slanted blade could generate a strong thrust to cut the roots. The cutting effect was better and the cutting surface of the roots was straight. The slotted blade meets the need for cutting garlic roots.

An ensemble deep learning approach to evaluate haptic delay from a single trial EEG data.

Haptic technologies are becoming increasingly valuable in Human-Computer interaction systems as they provide means of physical interaction with a remote or virtual environment. One of the persistent challenges in tele-haptic systems, communicating haptic information over a computer network, is the synchrony of the delivered haptic information with the rest of the sensory modalities. Delayed haptic feedback can have serious implications on the user performance and overall experience. Limited research efforts have been devoted to studying the implication of haptic delay on the human neural response and relating it to the overall haptic experience. Deep learning could offer autonomous brain activity interpretation in response to a haptic experience such as haptic delay. In this work, we propose an ensemble of 2D CNN and transformer models that is capable of detecting the presence and redseverity of haptic delay from a single-trial Electroencephalography data. Two EEG-based experiments involving visuo-haptic interaction tasks are proposed. The first experiment aims to collect data for detecting the presence of haptic delay during discrete force feedback using a bouncing ball on a racket simulation, while the second aims to collect data for detecting the severity level (none, mild, moderate, severe) of the haptic delay during continuous force feedback via grasping/releasing of an object in a bucket. The ensemble model showed a promising performance with an accuracy of 0.9142 ± 0.0157 for detecting haptic delay during discrete force feedback and 0.6625 ± 0.0067 for classifying the severity of haptic delay during continuous force feedback (4 levels). These results were obtained based on training the model with raw EEG data as well as their wavelet transform using several wavelet kernels. This study is a step forward towards developing cognitive evaluation of the user experience while interaction with haptic interfaces.

Noninvasively recorded high-gamma signals improve synchrony of force feedback in a novel neurorehabilitation brain–machine interface for brain injury

Objective. Brain injury is the leading cause of long-term disability worldwide, often resulting in impaired hand function. Brain–machine interfaces (BMIs) offer a potential way to improve hand function. BMIs often target replacing lost function, but may also be employed in neurorehabilitation (nrBMI) by facilitating neural plasticity and functional recovery. Here, we report a novel nrBMI capable of acquiring high-γ (70–115 Hz) information through a unique post-traumatic brain injury (TBI) hemicraniectomy window model, and delivering sensory feedback that is synchronized with, and proportional to, intended grasp force. Approach. We developed the nrBMI to use electroencephalogram recorded over a hemicraniectomy (hEEG) in individuals with TBI. The nrBMI empowered users to exert continuous, proportional control of applied force, and provided continuous force feedback. We report the results of an initial testing group of three human participants with TBI, along with a control group of three skull- and motor-intact volunteers. Main results. All participants controlled the nrBMI successfully, with high initial success rates (2 of 6 participants) or performance that improved over time (4 of 6 participants). We observed high-γ modulation with force intent in hEEG but not skull-intact EEG. Most significantly, we found that high-γ control significantly improved the timing synchronization between neural modulation onset and nrBMI output/haptic feedback (compared to low-frequency nrBMI control). Significance. These proof-of-concept results show that high-γ nrBMIs can be used by individuals with impaired ability to control force (without immediately resorting to invasive signals like electrocorticography). Of note, the nrBMI includes a parameter to change the fraction of control shared between decoded intent and volitional force, to adjust for recovery progress. The improved synchrony between neural modulations and force control for high-γ signals is potentially important for maximizing the ability of nrBMIs to induce plasticity in neural circuits. Inducing plasticity is critical to functional recovery after brain injury.

A novel force feedback model for virtual robot teaching of belt lapping

Virtual offline robot teaching provides a flexible and more economic robot programming solution to belt lapping. Due to the elasticity of the components, the applied force directly influences the polishing result, and the corresponding reaction force sensed by the user affects his or her decision on next operation. However, most of the simulation works reported so far focus mainly on the machining effect of belt lapping but not the contacting force. Therefore, this paper proposes a novel force feedback model for virtual robot teaching of belt lapping. By analyzing the force conditions at different process stages, three kinds of forces, namely natural force, colliding force, and resistance force, are defined to facilitate users with continuous force feedback during a virtual lapping process. To validate the model, a comparative study is done between the actual force measured during the belt lapping process and that simulated by the proposed force model. Moreover, evaluators are involved to conduct the virtual lapping of a mechanical part using the prototype developed. Both the quantitative and the evaluation experiments have validated that the force model is able to provide users with realistic force feedback and better ergonomic feelings of immersion. Moreover, they are also positive on the flexibility and efficiency of using the prototype for virtual robot teaching of belt lapping processes.

A novel force rendering approach for virtual assembly of mechanical parts

Virtual assembly (VA) is a typical virtual reality (VR)-based application in engineering. However, common interaction devices, such as keyboard and mouse, are less realistic due to lack of force sensation. Therefore, realistic force feedback in the VA environment provides a more natural interaction to simulate the assembly operation and result in improved task efficiency. This paper presents a novel force rendering approach, which focuses on mechanical part assembly based on three basic mechanical fit types, namely clearance fit, interference fit, and transition fit. The algorithm to calculate the assembly force is formulated by analyzing the tolerance variation along the assembly length between two mating parts. And then the force is rendered continuously at real-time during the VA operation to provide a fast, stable, and more realistic assembly force feedback to the users. Several comparative case studies are conducted to investigate the approach with the users’ performance of VA with the other three common approaches, namely conducting assembly task using a WIMP-based CAD software, with a standard physically based approach and the one with both collision detection and geometric constraints, respectively. The proposed approach is more efficient than other approaches by providing continuous force feedback to the users so as to greatly enhance their force sensation of the assembly operation. Moreover, case studies on users’ identification capability of different fit types has shown that with the continuous force rendering, users can easily tell the clearance fit from the other two fit types, hence the proposed approach equips users with the ability to possibly evaluate the assembly performance at the early stage of product development process.

A Tactile Display System for Hand Prostheses to Discriminate Pressure and Individual Finger Localization

No current commercially available myoelectrically controlled prosthetic hands provide conscious sensory feedback to the user. A system aiming at relocation of sensory input from a prosthetic hand equipped with force sensors to the forearm skin of amputees, a tactile display, has been developed and constructed. The system consists of five piezoresistive force sensors or, alternatively, a prosthetic hand equipped with three sensors, five digital servomotors with a lever and a circular plastic disk pushing on the skin, control electronics based on an MSP430 microcontroller and a test application implemented in LabVIEW running on a PC. The tactile display system is intended to be integrated into the socket of a hand prosthesis and used as a conscious sensory feedback system for hand amputees using a myoelectrically controlled hand prosthesis. The system will provide continuous force feedback from sensors in the fingertips of each prosthetic finger and will likely improve the users' controllability and perception of the prosthetic hand. Here we report on tests made on ”a five site” localization discrimination task and three pressure level discrimination tasks on the forearm of live healthy participants (non-amputees) using the LabView application to generate the stimulations. A mean live-linger discrimination accuracy of 86% and a mean three-level pressure discrimination accuracy of 93% were achieved, indicating the system to he a viable method of producing sensory feedback on the level of individual fingers.

Use of Force-Feedback Joysticks to Promote Teamwork in Virtual Teleoperation

Force feedback about fellow team-member control actions was evaluated as a means to improve team coordination and performance in virtual teleoperation. Each member of a two-person team received continuous force feedback about the other team- member's joystick control movements. In Study 1 (N=22 teams), participants worked in parallel to control vertical and horizontal forces exerted on a simulated inertial object. Although force feedback improved joystick coordination between participants, teleoperation performance worsened. In Study 2 (N=24 teams) where participants instead had direct control over horizontal and vertical position of a cursor, force feedback improved both joystick coordination and teleoperation performance. Performance impairment in Study 1 may be explained by the visual feedback of inertial movements being asynchronous to the social force feedback. These findings suggest force feedback about team member control actions can benefit team coordination and performance during virtual teleoperation only if it is synchronous with feedback about system response.

Skin contact force information in sensory nerve signals recorded by implanted cuff electrodes

When functional neuromuscular stimulation (FNS) is used to restore the use of paralyzed limbs after a spinal cord injury or stroke, it may be possible to control the stimulation using feedback information relayed by natural sensors in the skin. In this study the authors tested the hypothesis that the force applied on glabrous skin can be extracted from the electroneurographic (ENG) signal recorded from the sensory nerve. They used the central footpad of the cat hindlimb as a model of the human fingertip and recorded sensory activity with a cuff electrode chronically implanted around the tibial nerve. Their results showed that the tibial ENG signal, suitably filtered, rectified, and smoothed carries detailed static and dynamic information related to the force applied on the footpad. The authors derived a mathematical model of the force-ENG relation that provided accurate estimates of the ENG signal for a wide range of force profiles, amplitudes, and frequencies. Once fitted to data obtained in one recording session, the model could be made to fit data obtained in other sessions from the same cat, as well as from other cats, by simply adjusting its overall gain and offset. However, the model was noninvertible; i.e., the force could not be similarly predicted from the ENG signal, unless additional assumptions or restrictions were introduced. The authors discuss the reasons for these findings and their implications on the potential use of nerve signals as a source of continuous force feedback information suitable for closed-loop control of FNS. >