Conference Paper

Handshakiness: Benchmarking for human-robot hand interactions

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... Pedemonte, Laliberté, and Gosselin, 2016 measured the grip force while handshaking a 3D-printed hand, in order to design a tele-operated handshake system. One of the most recent work on quantifying tactile contact during handshake was done by Knoop et al., 2017. The authors measured the contact surface during interpersonal and human-robot handshakes using colored paint. ...
... We could determine the most often touched zones, which are the most relevant locations to put the sensors on. This work was briefly presented in Orefice et al., 2016, and at the same time, Knoop et al., 2017 published another study with the same purpose. As a consequence, the results in this section are compared with this latter publication. ...
... The results reveal that XTop contains high numbers of behavior differentiation due to the participant. This means that the position of the thumb above the agent's hand is efficient in discriminating participants, and it confirms the observations on the variability of this area by Knoop et al., 2017. The feature maxBot is also important. ...
Thesis
Today, robots are more and more present in everyday life. The study and the development of strategies of social and emotional interaction constitutes a key point of their insertion in our social space. The latter years, many researches were carried out in the man-robot communication by exploiting the facial expressions, posturals or still vocal, but very few focused on the physical interaction via the touch. However, recent researches in the field of the psychology and the human-machine interfaces (HMI) showed the role of the haptic modality and more particularly tactile in the perception of the feelings and their various dimensions (for example valence, activation, dominance). The objective of this project is to exploit this sensory modality in the emotional man-robot interaction. On the basis of the robot humanoid MEKA, a set of tactile and physiological sensors will be studied and developed to make sensitive certain regions of its body (eg arm, shoulder, hand) and to detect the emotional state of the user. Afterward, a series of studies will be led to analyze the behavior of the users in situations of emotional interaction with the robot. The results of these studies will allow us to identify typical haptic emotional behavior which will be used to model the behavior of the robot in contexts of social interactions.
... In [10] the authors study human-human handshaking interactions and measure contact forces along with IMU hand motion data. In [11] contact area and contact pressure are measured in human-human and humanrobot handshaking tests, comparing the results obtained with two underactuated soft hands. The handshake grasp would appear to match well to the first postural synergy of the human hand, which the Pisa/IIT SoftHand has been designed to follow in an underactuated manner [12]. ...
... The FSR sensors have a low profile, so they can be attached to the hand without requiring design changes. We use the histogram from [11] as a guide for where to place the sensors, we indicate with F f sr,i , with i = 1 · · · 3, the measure of the generic sensor. Sensors 1 − 3 in Fig. 2 a), are used as triggers to identify the contact with the human hand, and 1 and 2 are used for estimating F R as they were found to be robust towards small variations in the grasp. ...
... To identify the relation between F H and FSR measurements, we attach the FSR sensors indicated with 1 and 2 to a sensorized palm, as shown in Fig. 2 b). The sensorized palm is a simple 3D-printed object whose shape and dimensions similar to a human hand palm, composed of two shells connected by a load cell [11]. Six calibration experiments were performed, with three different subjects. ...
Article
In this paper we investigate the role of haptic feedback in human-robot handshaking by comparing different force controllers. The basic hypothesis is that in human handshaking force control there is a balance between an intrinsic (open-loop) and extrinsic (closed-loop) contribution. We use an underactuated anthropomorphic robotic hand, the Pisa/IIT hand, instrumented with a set of pressure sensors estimating the grip force applied by humans. In a first set of experiments we ask subjects to mimic a given force profile applied by the robot hand, to understand how human perceive and are able to reproduce a handshaking force. Using the obtained results, we implement three different handshaking controllers in which we varied the intrinsic and extrinsic contributions and in a second set of experiments we ask participants to evaluate them in a user study. We show that a sensorimotor delay mimicking the reaction time of the Central Nervous System (CNS) is beneficial for making interactions more human-like. Moreover, we demonstrate that humans exploit closed-loop control for handshaking. By varying the controller we show that we can change the perceived handshake quality, and also influence personality traits attributed to the robot.
... More recently, contact pressure has been proposed as an evaluation metric for soft robotic grippers [25]. Sometimes, only contact area information is analysed, e.g. to categorise prehensile patterns [18], or to assess the realism of human-robot handshaking [26]. Most notably, in both these examples a simple technique was adopted, where either the hand or the object was painted and then painttransfer patterns were used to describe the contact occurred. ...
... Although the experimental protocol is inspired by other literature [18], [26], the exclusive acquisition of contact area information, with no information about the contact forces, is an important limitation of our study. Indeed, the peak contact pressure is an important metric for the evaluation of the quality of the interaction. ...
Article
Full-text available
Robotic hand engineers usually focus on finger capabilities, often disregarding the palm contribution. Inspired by human anatomy, this paper explores the advantages of including a flexible concave palm into the design of a robotic hand actuated by soft synergies. We analyse how the inclusion of an articulated palm improves finger workspace and manipulability. We propose a mechanical design of a modular palm with two elastic rolling-contact palmar joints, that can be integrated on the Pisa/IIT SoftHand, without introducing additional motors. With this prototype, we evaluate experimentally the grasping capabilities of a robotic palm. We compare its performance to that of the same robotic hand with the palm fixed, and to that of a human hand. To assess the effective grasp quality achieved by the three systems, we measure the contact area using paint-transfer patterns in different grasping actions. Preliminary grasping experiments show a closer resemblance of the soft-palm robotic hand to the human hand. Results evidence a higher adaptive capability and a larger involvement of all fingers in grasping.
... First, five human subjects were asked to grasp five food items barehanded, recording the contact area between the users' hand and the objects' surface using paint transfer and machine vision techniques (see Sec. III-A and Fig. 1b). Paint-transfer approaches have already been used to detect contact areas when humans grasp objects [29]. However, to the best of our knowledge, this is the first time that this approach is used to learn an LfD policy. ...
... Our objective was to understand how humans grasp objects when they are endowed with maximum dexterity and feedback (i.e. during direct hand interaction). Inspired by the work of Knopp et al. [29] and Kamakura et al. [34], we used a paint-transfer approach to identify the parts of the hand that are employed the most during this grasping. ...
Article
Research on robotic manipulation of fragile, compliant objects, such as food items, is gaining traction due to its game-changing potential within the food production and retailing sectors, currently characterized by manually-intensive and highly repetitive tasks. Food products exhibit high levels of frailness, biological variation, and complex 3D shapes and textures. For these reasons, introducing greater levels of robotic automation in the food and agricultural sectors remains an important challenge. This paper addresses this challenge by developing a human-centred, haptic-based, Learning from Demonstration (LfD) policy that enables pre-trained autonomous grasping of food items using an anthropomorphic robotic system. The policy combines data from teleoperation and direct human manipulation of objects, embodying human intent and interaction areas of significance. We evaluated the proposed solution against a recent state-of-the-art LfD policy as well as against two standard impedance controller techniques. Results show that the proposed policy performs significantly better than the other considered techniques, leading to high grasping success rates while guaranteeing the integrity of the food at hand.
... Just like in human interactions, it is probable that humans will make split-second judgements on whether a humanoid robot possesses humanlike traits through a handshake (e.g., Is this robot sociable or competent enough to interact with me?). Thus, a humanoid robot should be able to know what to do when a person extends his or her hand (Cabibihan et al., 2009;Knoop et al., 2017;Avelino et al., 2018). ...
... In the classical experiments of Harlow and Zimmermann (1958) on affection, they identified softness, warmth, and comfort to be among the basic physiological need of humans. Soft and warm artificial skins have been developed that can be incorporated into compliant robotic hands for handshaking [e.g., Cabibihan et al. (2006b);Cabibihan et al. (2015); Knoop et al. (2017)]. ...
Article
Full-text available
The handshake is the most acceptable gesture of greeting in many cultures throughout many centuries. To date, robotic arms are not capable of fully replicating this typical human gesture. Using multiple sensors that detect contact forces and displacements, we characterized the movements that occured during handshakes. A typical human-to-human handshake took around 3.63 s (SD = 0.45 s) to perform. It can be divided into three phases: reaching (M = 0.92 s, SD = 0.45 s), contact (M = 1.96 s, SD = 0.46 s), and return (M = 0.75 s, SD = 0.12 s). The handshake was further investigated to understand its subtle movements. Using a multiphase jerk minimization model, a smooth human-to-human handshake can be modelled with fifth or fourth degree polynomials at the reaching and return phases, and a sinusoidal function with exponential decay at the contact phase. We show that the contact phase (1.96 s) can be further divided according to the following subphases: preshake (0.06 s), main shake (1.31 s), postshake (0.06 s), and a period of no movement (0.52 s) just before both hands are retracted. We compared these to the existing handshake models that were proposed for physical human-robot interaction (pHRI). From our findings in human-to-human handshakes, we proposed guidelines for a more natural handshake movement between humanoid robots and their human partners.
... However, precision grasps were not the focus of either of these studies. Other recent studies of soft finger design focused entirely on power grasps, using simulation and experimentation (Knoop et al., 2017). Furthermore, one recent study has been presented by Vogt et al. (2018) where adding a passive extension to soft fingers enabled them to perform pinch grasps. ...
... While it may be possible to enable the desired placement of contact points with only one bending segment of non-uniform stiffness, we restrict the focus of this work to uniform bending segments for simplicity. Prior work in this area from Knoop et al. (2017) shows that non-uniform stiffness can be used to tune the contact pressure a soft finger applies at each point along its contact surface. However, the impact of these tuned contact-pressure profiles on grasping performance has not been evaluated in detail. ...
Article
In this work, we discuss the design of soft robotic fingers for robust precision grasping. Through a conceptual analysis of the finger shape and compliance during grasping, we confirm that antipodal grasps are more stable when contact with the object occurs on the side of the fingers (i.e., pinch grasps) instead of the fingertips. In addition, we show that achieving such pinch grasps with soft fingers for a wide variety of objects requires at least two independent bending segments each, but only requires actuation in the proximal segment. Using a physical prototype hand, we evaluate the improvement in pinch-grasping performance of this two-segment proximally actuated finger design compared to more typical, uniformly actuated fingers. Through an exploration of the relative lengths of the two finger segments, we show the tradeoff between power grasping strength and precision grasping capabilities for fingers with passive distal segments. We characterize grasping on the basis of the acquisition region, object sizes, rotational stability, and robustness to external forces. Based on these metrics, we confirm that higher-quality precision grasping is achieved through pinch grasping via fingers with the proximally actuated finger design compared to uniformly actuated fingers. However, power grasping is still best performed with uniformly actuated fingers. Accordingly, soft continuum fingers should be designed to have at least two independently actuated serial segments, since such fingers can maximize grasping performance during both power and precision grasps through controlled adaptation between uniform and proximally actuated finger structures.
... Even though the psychological mechanism of how handshake conveys emotions remain unclear, this conclusion has brought out another large area of human-robot handshake research, that is the physical interaction during a handshake. From a haptics perspective, physical interactions have a kinesthetic element (joint torques) and a cutaneous element of contact forces on the skin [8]. Several researches have been done on measuring the contact area and contact pressure in human handshake interactions [9] [10]. ...
Article
Human-robot handshake is a deeply explored topic in human-robot physical interaction. Lots of researches have focused on the pressure distribution during the handshake movements. But the grasping force and joint torque are usually intertwined, and the current approach measuring the contact force cannot separate the two. In this research we proposed a new device for the purpose of measuring the interaction force generated by joint movement and the grasping force generated by forearm muscles separately. And we used the proposed measuring device to investigate two different types of handshakes and verified its effectiveness in doing research on analyzing the interaction forces of handshakes.
... Evaluating the human-likeness of a handshake on a more complex android robot yields different challenges. All other aspects of a handshake mentioned above must be considered for a Handshake Turing test, some of which have been evaluated in [6]. ...
Preprint
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Handshakes are fundamental and common greeting and parting gestures among humans. They are important in shaping first impressions as people tend to associate character traits with a person's handshake. To widen the social acceptability of robots and make a lasting first impression, a good handshaking ability is an important skill for social robots. Therefore, to test the human-likeness of a robot handshake, we propose an initial Turing-like test, primarily for the hardware interface to future AI agents. We evaluate the test on an android robot's hand to determine if it can pass for a human hand. This is an important aspect of Turing tests for motor intelligence where humans have to interact with a physical device rather than a virtual one. We also propose some modifications to the definition of a Turing test for such scenarios taking into account that a human needs to interact with a physical medium.
... Proprioceptive sensing of contact forces is a useful tool for soft robotics because the complex deformation during interactions makes it difficult to use other force sensors. And it is especially useful in applications that require a soft touch, e.g. for human-robot interaction (Knoop et al. 2017) or pick-and-place of delicate fruits and vegetables (Mnyusiwalla et al. 2020). ...
Preprint
We propose a sensorization method for soft pneumatic actuators that uses an embedded microphone and speaker to measure different actuator properties. The physical state of the actuator determines the specific modulation of sound as it travels through the structure. Using simple machine learning, we create a computational sensor that infers the corresponding state from sound recordings. We demonstrate the acoustic sensor on a soft pneumatic continuum actuator and use it to measure contact locations, contact forces, object materials, actuator inflation, and actuator temperature. We show that the sensor is reliable (average classification rate for six contact locations of 93%), precise (mean spatial accuracy of 3.7 mm), and robust against common disturbances like background noise. Finally, we compare different sounds and learning methods and achieve best results with 20 ms of white noise and a support vector classifier as the sensor model.
... Participants were also informed that they should feel free to refrain from squeezing if they felt like it. The two force standards that were presented to participants were 50 N (easy condition) and 150 N (difficult condition)-the lower standard corresponding to a weak handshake and the higher standard to a strong handshake (Knoop et al. 2017). The first task included ten trials of each force standard presented in a randomized order and served as a practice period that allowed participants to learn about the difficulty of exerting 50 N and 150 N. ...
Article
Full-text available
According to motivational intensity theory, individuals are motivated to conserve energy when pursuing goals. They should invest only the energy required for success and disengage if success is not important enough to justify the required energy. We tested this hypothesis in five experiments assessing exerted muscle force in isometric hand grip tasks as indicator of energy investment. Our results provided mixed evidence for motivational intensity theory. Corroborating its predictions, energy investment was a function of task demand. However, we did not find evidence for the predicted disengagement, and we observed that participants exerted in most conditions more force than required. Furthermore, the data could be better explained by a model that predicted an additive effect of task demand and success importance than by models drawing on motivational intensity theory’s predictions. These results illustrate the strong link between energy investment and task demand but challenge motivational intensity theory’s primacy of energy conservation.
... However, works looking into how well robotic interfaces are suited for such hapticheavy interactions are limited in number. In this regard, Knoop et al. [42] perform experiments to understand the contact area, contact pressure and grasping forces exerted by participants during handshaking and test out how a few robotic hands and custom finger designs comply with their observations from humanhuman handshaking interactions. Participants were asked to perform 3 handshakes of different strengths, namely, weak, normal and strong. ...
Preprint
Full-text available
For some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.
... However, works looking into how well robotic interfaces are suited for such haptic-heavy interactions are limited in number. In this regard, Knoop et al. [42] perform experiments to understand the contact area, contact pressure and grasping forces exerted by participants during handshaking and test out how a few robotic hands and custom finger designs comply with their observations from humanhuman handshaking interactions. Participants were asked to perform 3 handshakes of different strengths, namely, weak, normal and strong. ...
Article
Full-text available
For some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.
... For the handshake, the actuators are triggered with two different signals: the bubble and the kinesthetic impedance actuators receive the black squared signal shown in Table 2, mimicking the grip force between hands (Knoop et al., 2017;Orefice et al., 2018). The multichannel actuators alternatively inflate and deflate as shown with the dark and light pink signals, mimicking the up and down movement of the handshake. ...
Article
Full-text available
Social touch is essential for creating and maintaining strong interpersonal bonds amongst humans. However, when distance separates users, they often rely on voice and video communication technologies to stay connected with each other, and the lack of tactile interactions between users lowers the quality of the social interactions. In this research, we investigated haptic patterns to communicate five tactile messages comprising of four types of social touch (high five, handshake, caress, and asking for attention) and one physiological signal (the pulse of a heartbeat), delivered on the hand through a haptic glove. Since social interactions are highly dependent on their context, we conceived two interaction scenarios for each of the five tactile messages, conveying distinct emotions being spread across the circumplex model of emotions. We conducted two user studies: in the first one participants tuned the parameters of haptic patterns to convey tactile messages in each scenario, and a follow up study tested naïve participants to assess the validity of these patterns. Our results show that all haptic patterns were recognized above chance level, and the well-defined parameter clusters had a higher recognition rate, reinforcing the hypothesis that some social touches have more universal patterns than others. We also observed parallels between the parameters' levels and the type of emotions they conveyed based on their mapping in the circumplex model of emotions.
... Currently, bionic hands have been widely applied in many fields [1], such as grasping tasks [2,3], industrial applications [4], human-robot-interaction [5], and conducting delicate operations in dangerous situations [6]. As multi-degree-of-freedom (DoF) end-effectors, bionic hands have excellent flexibility and versatility. ...
Article
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Bionic hands have been employed in a wide range of applications, including prosthetics, robotic grasping, and human–robot interaction. However, considering the underactuated and nonlinear characteristics, as well as the mechanical structure’s backlash, achieving natural and intuitive teleoperation control of an underactuated bionic hand remains a critical issue. In this paper, the teleoperation control of an underactuated bionic hand using wearable and vision-tracking system-based methods is investigated. Firstly, the nonlinear behaviour of the bionic hand is observed and the kinematics model is formulated. Then, the wearable-glove-based and the vision-tracking-based teleoperation control frameworks are implemented, respectively. Furthermore, experiments are conducted to demonstrate the feasibility and performance of these two methods in terms of accuracy in both static and dynamic scenarios. Finally, a user study and demonstration experiments are conducted to verify the performance of these two approaches in grasp tasks. Both developed systems proved to be exploitable in both powered and precise grasp tasks using the underactuated bionic hand, with a success rate of 98.6% and 96.5%, respectively. The glove-based method turned out to be more accurate and better performing than the vision-based one, but also less comfortable, requiring greater effort by the user. By further incorporating a robot manipulator, the system can be utilised to perform grasp, delivery, or handover tasks in daily, risky, and infectious scenarios.
Chapter
The use of social, anthropomorphic robots to support humans in various industries has been on the rise. During Human-Robot Interaction (HRI), physically interactive non-verbal behaviour is key for more natural interactions. Handshaking is one such natural interaction used commonly in many social contexts. It is one of the first non-verbal interactions which takes place and should, therefore, be part of the repertoire of a social robot. In this paper, we explore the existing state of Human-Robot Handshaking and discuss possible ways forward for such physically interactive behaviours.
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This article presents a system for soft human–robot handshaking, using a soft robot hand in conjunction with a lightweight and impedance-controlled robot arm. Using this system, we study how different factors influence the perceived naturalness, and give the robot different personality traits. Capitalizing on recent findings regarding handshake grasp force regulation, and on studies of the impedance control of the human arm, we investigate the role of arm stiffness as well as the kinesthetic synchronization of human and robot arm motions during the handshake. The system is implemented using a lightweight anthropomorphic arm, with a Pisa/IIT Softhand wearing a sensorized silicone glove as the end-effector. The robotic arm is impedance-controlled, and its stiffness changes according to different laws under investigation. An internal observer is employed to synchronize the human and robot arm motions. Thus, we simulate both active and passive behavior of the robotic arm during the interaction. Using the system, studies are conducted where 20 participants are asked to interact with the robot, and then rate the perceived quality of the interaction using Likert scales. Our results show that the control of the robotic arm kinesthetic behavior does have an effect on the interaction with the robot, in term of its perceived personality traits, responsiveness, and human-likeness. Our results pave the way towards robotic systems that are capable of performing human–robot interactions in a more human-like manner, and with personality.
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In this paper we present a method for evaluating a haptic device which simulates human handshakes interfaced via a metal rod. We provide an overview of the haptic demonstrator and the control algorithm used for delivering realistic handshakes. For the evaluation of this handshake demonstrator we introduce a 'ground truth' approach, where we compare the robot handshakes with handshakes operated by a human via the same metal rod. For this, an experiment was carried out where the participants entered a virtual environment, i.e. a virtual cocktail party, and were asked to perform a number of handshakes, either with the robot operating with one of two control algorithms operating the metal rod - a basic one for comparison or the proposed new advanced one, or with a human operating the metal rod. The virtual environment was represented only through audio and haptics, without any visual representation, i.e. the subjects participated blindfolded. The evaluation of each handshake was achieved through the subjective scoring of each of the handshakes. The results of the study show that the demonstrator operating with the proposed new control scheme was evaluated significantly more human-like than with the demonstrator operating with the basic algorithm, and also that the real human handshake was evaluated more like a real human handshake than both types of robot handshakes. Although the difference between the advanced robot and human handshake was significant, the effect sizes are not very different, indicating substantial confusion of participants between the advanced robot and human operated handshakes.
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For the purpose of classifying the patterns of static prehension in normal hands, the finger positions were photographed from 5 directions, while each of 7 normal adults held each of the 98 objects. The contact areas were also photographed from three to four directions. All sets of photographs were compared. Any two were considered identical in pattern when both the finger positions and the contact areas were alike. Fourteen identical patterns were identified: five patterns of power grip, four of intermediate grip, four of precision grip, and one other. Eighty-six percent of the grips shown by the subjects could be fitted into one of these prehension patterns, and the remaining 14 percent into intermediate or combined patterns. For all 7 subjects, 31 of 98 objects were grasped in an identical pattern and the rest of the objects, 67, in 2 or more patterns.
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In the analysis of human grasping motions, classifications of grasping patterns depend largely on personal definitions; no unified view has been reached. The measured quantities in grasping include the posture of the hand, and grasping force and its distribution. Grasping force and its distribution have been little considered. This paper first describes a Sensor Glove MKIII, which measures grasping force and its distribution in human grasping motion. Then the grasping force measured when same objects were grasped using this sensor is shown together with the distribution of the force. Furthermore the usefulness of this sensor was verified by applying the “Contact Web” technique to the grasping force and its distribution pattern and taking an example of the classification of grasping reported by Cutkosky (1989). It follows from these facts that the Sensor Glove MKIII may be useful for the analysis of grasping patterns
Human engineering design data digest
  • A Poston
Contact pressure distribution as an evaluation metric for human-robot hand interactions
  • E Knoop
  • M Bächer
  • P Beardsley