Cagatay Basdogan

Berk Guler, Pouya P. Niaz, Alireza Madani et al.

In this paper, we propose a supervised learning approach based on an Artificial Neural Network (ANN) model for real-time classification of subtasks in a physical human-robot interaction (pHRI) task involving contact with a stiff environment. In this regard, we consider three subtasks for a given pHRI task: Idle, Driving, and Contact. Based on this classification, the parameters of an admittance controller that regulates the interaction between human and robot are adjusted adaptively in real time to make the robot more transparent to the operator (i.e. less resistant) during the Driving phase and more stable during the Contact phase. The Idle phase is primarily used to detect the initiation of task. Experimental results have shown that the ANN model can learn to detect the subtasks under different admittance controller conditions with an accuracy of 98% for 12 participants. Finally, we show that the admittance adaptation based on the proposed subtask classifier leads to 20% lower human effort (i.e. higher transparency) in the Driving phase and 25% lower oscillation amplitude (i.e. higher stability) during drilling in the Contact phase compared to an admittance controller with fixed parameters.

Feras Kiki, Pouya P. Niaz, Alireza Madani et al.

Assessing human muscle fatigue is critical for optimizing performance and safety in physical human-robot interaction(pHRI). This work presents a data-driven framework to estimate fatigue in dynamic, cyclic pHRI using arm-mounted surface electromyography(sEMG). Subject-specific machine-learning regression models(Random Forest, XGBoost, and Linear Regression predict the fraction of cycles to fatigue(FCF) from three frequency-domain and one time-domain EMG features, and are benchmarked against a convolutional neural network(CNN) that ingests spectrograms of filtered EMG. Framing fatigue estimation as regression (rather than classification) captures continuous progression toward fatigue, supporting earlier detection, timely intervention, and adaptive robot control. In experiments with ten participants, a collaborative robot under admittance control guided repetitive lateral (left-right) end-effector motions until muscular fatigue. Average FCF RMSE across participants was 20.8+/-4.3% for the CNN, 23.3+/-3.8% for Random Forest, 24.8+/-4.5% for XGBoost, and 26.9+/-6.1% for Linear Regression. To probe cross-task generalization, one participant additionally performed unseen vertical (up-down) and circular repetitions; models trained only on lateral data were tested directly and largely retained accuracy, indicating robustness to changes in movement direction, arm kinematics, and muscle recruitment, while Linear Regression deteriorated. Overall, the study shows that both feature-based ML and spectrogram-based DL can estimate remaining work capacity during repetitive pHRI, with the CNN delivering the lowest error and the tree-based models close behind. The reported transfer to new motion patterns suggests potential for practical fatigue monitoring without retraining for every task, improving operator protection and enabling fatigue-aware shared autonomy, for safer fatigue-adaptive pHRI control.

Bushra Sadia, Ayberk Sadic, Mehmet Ayyildiz et al.

Advancements in surface haptics technology have given rise to the development of interactive applications displaying tactile content on touch surfaces such as images, signs, diagrams, plots, charts, graphs, maps, networks, and tables. In those applications, users manually explore the touch surface to interact with the tactile data using some intuitive strategies. The user's exploration strategy, tactile data's complexity, and tactile rendering method all affect the user's haptic perception, which plays a critical role in design and prototyping of those applications. In this study, we conducted experiments with human participants to investigate the recognition rate and time of five tactile shapes rendered by electrovibration on a touchscreen using three different methods and displayed in prototypical orientation and non-prototypical orientations. The results showed that the correct recognition rate of the shapes was higher when the haptically active area was larger. However, as the number of edges increased, the recognition time increased and the recognition rate dropped significantly, arriving to a value slightly higher than the chance rate of 20% for non-prototypical octagon. We also recorded the participants' finger movements on the touchscreen to examine their haptic exploration strategies. Our analyses revealed that the participants first used global scanning to extract the coarse features of the displayed shapes, and then they applied local scanning to identify finer details, but needed another global scan for final confirmation in the case of non-prototypical shapes, possibly due to the current limitations of electrovibration technology in displaying tactile stimuli to a user. We observed that it was highly difficult to follow the edges of shapes and recognize shapes with more than five edges under electrovibration when a single finger was used for exploration.

Omer Sirin, Allan Barrea, Philippe Lefèvre et al.

Displaying tactile feedback through a touchscreen via electrovibration has many potential applications in mobile devices, consumer electronics, home appliances, and automotive industry though our knowledge and understanding on the underlying contact mechanics is very limited. An experimental study was conducted to investigate the contact evolution between the human finger and a touchscreen under electrovibration using a robotic set-up and an imaging system. The results show that the effect of electrovibration is only present during full slip but not before slip. Hence, coefficient of friction increases under electrovibration as expected during full slip, but the apparent contact area is significantly smaller during full slip when compared to that of no electrovibration condition. It is suggested that the main cause of the increase in friction during full slip is due to an increase in real contact area and the reduction in apparent area is due to stiffening of the finger skin in tangential direction.

Atakan Arasan, Cagatay Basdogan, T. Metin Sezgin

With the emergence of pen-enabled tablets and mobile devices, stylus-based interaction has been receiving increasing attention. Unfortunately, styluses available in the market today are all passive instruments that are primarily used for writing and pointing. In this paper, we describe a novel stylus capable of displaying certain vibro-tactile and inertial haptic effects to the user. Our stylus is equipped with two vibration actuators at the ends, which are used to create a tactile sensation of up and down movement along the stylus. The stylus is also embedded with a DC motor, which is used to create a sense of bidirectional rotational torque about the long axis of the pen. Through two psychophysical experiments, we show that, when driven with carefully selected timing and actuation patterns, our haptic stylus can convey movement and rotational torque information to the user. Results from a further psychophysical experiment provide insight on how the shape of the actuation patterns effects the perception of rotational torque effect. Finally, experimental results from our interactive pen-based game show that our haptic stylus is effective in practical settings

Bushra Sadia, Senem Ezgi Emgin, T. Metin Sezgin et al.

Although mobile phones incorporate low-cost vibration motors to enhance touch-based interactions, it is not possible to generate complex tactile effects on their touchscreens. It is also difficult to relate the limited vibrotactile feedback generated by these motors to different types of physical buttons. In this study, we focus on creating vibrotactile feedback on a touchscreen that simulates the feeling of physical buttons using piezo actuators attached to it. We first recorded and analyzed the force, acceleration, and voltage data from twelve participants interacting with three different physical buttons: latch, toggle, and push buttons. Then, a button-specific vibrotactile stimulus was generated for each button based on the recorded data. Finally, we conducted a threealternative forced choice (3AFC) experiment with twenty participants to explore whether the resultant stimulus is distinct and realistic. In our experiment, participants were able to match the three digital buttons with their physical counterparts with a success rate of 83%. In addition, we harvested seven adjective pairs from the participants expressing their perceptual feeling of pressing the physical buttons. All twenty participants rated the degree of their subjective feelings associated with each adjective for all the physical and digital buttons investigated in this study. Our statistical analysis showed that there exist at least three adjective pairs for which participants have rated two out of three digital buttons similar to their physical counterparts.

Senem Ezgi Emgin, Amirreza Aghakhani, T. Metin Sezgin et al.

We present HapTable; a multimodal interactive tabletop that allows users to interact with digital images and objects through natural touch gestures, and receive visual and haptic feedback accordingly. In our system, hand pose is registered by an infrared camera and hand gestures are classified using a Support Vector Machine (SVM) classifier. To display a rich set of haptic effects for both static and dynamic gestures, we integrated electromechanical and electrostatic actuation techniques effectively on tabletop surface of HapTable, which is a surface capacitive touch screen. We attached four piezo patches to the edges of tabletop to display vibrotactile feedback for static gestures. For this purpose, the vibration response of the tabletop, in the form of frequency response functions (FRFs), was obtained by a laser Doppler vibrometer for 84 grid points on its surface. Using these FRFs, it is possible to display localized vibrotactile feedback on the surface for static gestures. For dynamic gestures, we utilize the electrostatic actuation technique to modulate the frictional forces between finger skin and tabletop surface by applying voltage to its conductive layer. Here, we present two examples of such applications, one for static and one for dynamic gestures, along with detailed user studies. In the first one, user detects the direction of a virtual flow, such as that of wind or water, by putting their hand on the tabletop surface and feeling a vibrotactile stimulus traveling underneath it. In the second example, user rotates a virtual knob on the tabletop surface to select an item from a menu while feeling the knob's detents and resistance to rotation in the form of frictional haptic feedback.

Idil Ozdamar, M. Reza Alipour, Benoit P. Delhaye et al.

Rendering tactile effects on a touch screen via electrovibration has many potential applications. However, our knowledge on tactile perception of change in friction and the underlying contact mechanics are both very limited. In this study, we investigate the tactile perception and the contact mechanics for a step change in friction under electrovibration during a relative sliding between finger and the surface of a capacitive touchscreen. First, we conduct magnitude estimation experiments to investigate the role of normal force and sliding velocity on the perceived tactile intensity for a step increase and decrease in friction, called as rising friction (RF) and falling friction (FF). To investigate the contact mechanics involved in RF and FF, we then measure the frictional force, the apparent contact area, and the strains acting on the fingerpad during sliding at a constant velocity under three different normal loads using a custom-made experimental set-up. The results show that the participants perceived RF stronger than FF, and both the normal force and sliding velocity significantly influenced their perception. These results are supported by our mechanical measurements; the relative change in friction, the apparent contact area, and the strain in the sliding direction were all higher for RF than those for FF, especially for low normal forces. Taken together, our results suggest that different contact mechanics take place during RF and FF due to the viscoelastic behavior of fingerpad skin, and those differences influence our tactile perception of a step change in friction.

Yusuf Aydin, Doganay Sirintuna, Cagatay Basdogan

In the near future, collaborative robots (cobots) are expected to play a vital role in the manufacturing and automation sectors. It is predicted that workers will work side by side in collaboration with cobots to surpass fully automated factories. In this regard, physical human-robot interaction (pHRI) aims to develop natural communication between the partners to bring speed, flexibility, and ergonomics to the execution of complex manufacturing tasks. One challenge in pHRI is to design an optimal interaction controller to balance the limitations introduced by the contradicting nature of transparency and stability requirements. In this paper, a general methodology to design an admittance controller for a pHRI system is developed by considering the stability and transparency objectives. In our approach, collaborative robot constrains the movement of human operator to help with a pHRI task while an augmented reality (AR) interface informs the operator about its phases. To this end, dynamical characterization of the collaborative robot (LBR IIWA 7 R800, KUKA Inc.) is presented first. Then, the stability and transparency analyses for our pHRI task involving collaborative drilling with this robot are reported. A range of allowable parameters for the admittance controller is determined by superimposing the stability and transparency graphs. Finally, three different sets of parameters are selected from the allowable range and the effect of admittance controllers utilizing these parameter sets on the task performance is investigated.

Yusuf Aydin, Ozan Tokatli, Volkan Patoglu et al.

Physical human-robot interaction (pHRI) integrates the benefits of human operator and a collaborative robot in tasks involving physical interaction, with the aim of increasing the task performance. However, the design of interaction controllers that achieve safe and transparent operations is challenging, mainly due to the contradicting nature of these objectives. Knowing that attaining perfect transparency is practically unachievable, controllers that allow better compromise between these objectives are desirable. In this paper, we propose a multi-criteria optimization framework, which jointly optimizes the stability robustness and transparency of a closed-loop pHRI system for a given interaction controller. In particular, we propose a Pareto optimization framework that allows the designer to make informed decisions by thoroughly studying the trade-off between stability robustness and transparency. The proposed framework involves a search over the discretized controller parameter space to compute the Pareto front curve and a selection of controller parameters that yield maximum attainable transparency and stability robustness by studying this trade-off curve. The proposed framework not only leads to the design of an optimal controller, but also enables a fair comparison among different interaction controllers. In order to demonstrate the practical use of the proposed approach, integer and fractional order admittance controllers are studied as a case study and compared both analytically and experimentally. The experimental results validate the proposed design framework and show that the achievable transparency under fractional order admittance controller is higher than that of integer order one, when both controllers are designed to ensure the same level of stability robustness.

Aykut Isleyen, Yasemin Vardar, Cagatay Basdogan

Realistic display of tactile textures on touch screens is a big step forward for haptic technology to reach a wide range of consumers utilizing electronic devices on a daily basis. Since the texture topography cannot be rendered explicitly by electrovibration on touch screens, it is important to understand how we perceive the virtual textures displayed by friction modulation via electrovibration. We investigated the roughness perception of real gratings made of plexiglass and virtual gratings displayed by electrovibration through a touch screen for comparison. In particular, we conducted two psychophysical experiments with 10 participants to investigate the effect of spatial period and the normal force applied by finger on roughness perception of real and virtual gratings in macro size. We also recorded the contact forces acting on the participants' finger during the experiments. The results showed that the roughness perception of real and virtual gratings are different. We argue that this difference can be explained by the amount of fingerpad penetration into the gratings. For real gratings, penetration increased tangential forces acting on the finger, whereas for virtual ones where skin penetration is absent, tangential forces decreased with spatial period. Supporting our claim, we also found that increasing normal force increases the perceived roughness of real gratings while it causes an opposite effect for the virtual gratings. These results are consistent with the tangential force profiles recorded for both real and virtual gratings. In particular, the rate of change in tangential force ($dF_t/dt$) as a function of spatial period and normal force followed trends similar to those obtained for the roughness estimates of real and virtual gratings, suggesting that it is a better indicator of the perceived roughness than the tangential force magnitude.

Cagatay Basdogan, Frederic Giraud, Vincent Levesque et al.

We review the current technology underlying surface haptics that converts passive touch surfaces to active ones (machine haptics), our perception of tactile stimuli displayed through active touch surfaces (human haptics), their potential applications (human-machine interaction), and finally the challenges ahead of us in making them available through commercial systems. This review primarily covers the tactile interactions of human fingers or hands with surface-haptics displays by focusing on the three most popular actuation methods: vibrotactile, electrostatic, and ultrasonic.