Effective Haptic Rendering Method for Complex Interactions

Hernantes, Josune; Díaz, Iñaki; Borro, Diego; Gil, Jorge Juan

doi:10.5772/25910

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Proprioceptors (i.e., proprioceptive receptors), on the other hand, are located in the joints, muscles, and tendons of the body. A haptic sensation is directly delivered to a user through a haptic interface (HI), which is a device that reproduces the required or desired sensation [ 2 ]. Besides the use of visual and auditory cues as common human–computer interfaces (HCIs), there is an increasing demand for other means of haptics, such as touch with a combination of force, motion, and vibrations, leading to creating artificial haptic sensations for more effective HCIs.…”

Section: Introductionmentioning

confidence: 99%

A 3D-Printed Soft Haptic Device with Built-in Force Sensing Delivering Bio-Mimicked Feedback

et al. 2023

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Haptics plays a significant role not only in the rehabilitation of neurological disorders, such as stroke, by substituting necessary cognitive information but also in human–computer interfaces (HCIs), which are now an integral part of the recently launched metaverse. This study proposes a unique, soft, monolithic haptic feedback device (SoHapS) that was directly manufactured using a low-cost and open-source fused deposition modeling (FDM) 3D printer by employing a combination of soft conductive and nonconductive thermoplastic polyurethane (TPU) materials (NinjaTek, USA). SoHapS consists of a soft bellow actuator and a soft resistive force sensor, which are optimized using finite element modeling (FEM). SoHapS was characterized both mechanically and electrically to assess its performance, and a dynamic model was developed to predict its force output with given pressure inputs. We demonstrated the efficacy of SoHapS in substituting biofeedback with tactile feedback, such as gripping force, and proprioceptive feedback, such as finger flexion–extension positions, in the context of teleoperation. With its intrinsic properties, SoHapS can be integrated into rehabilitation robots and robotic prostheses, as well as augmented, virtual, and mixed reality (AR/VR/MR) systems, to induce various types of bio-mimicked feedback.

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Section: Introductionmentioning

confidence: 99%

A 3D-Printed Soft Haptic Device with Built-in Force Sensing Delivering Bio-Mimicked Feedback

et al. 2023

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“…Here, a haptic interface (Hernantes et al, 2012 ) is a device that includes a robotic mechanism along with sensors to determine position of humans in the virtual environment and actuators to apply forces to the operator and is used to manipulate an object within a virtual environment. The usage of robotic haptic interfaces generating force-field paved the way for understanding the human mechanism while learning skills through dyadic haptic interaction.…”

Section: Introductionmentioning

confidence: 99%

Mutual Skill Learning and Adaptability to Others via Haptic Interaction

et al. 2021

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When learning a new skill through an unknown environment, should we practice alone, or together with another beginner, or learn from the expert? It is normally helpful to have an expert guiding through unknown environmental dynamics. The guidance from the expert is fundamentally based on mutual interactions. From the perspective of the beginner, one needs to face dual unknown dynamics of the environment and motor coordination of the expert. In a cooperative visuo-haptic motor task, we asked novice participants to bring a virtual mass onto the specified target location under an unknown external force field. The task was completed by an individual or with an expert or another novice. In addition to evaluation of the motor performance, we evaluated the adaptability of the novice participants to a new partner while attempting to achieve a common goal together. The experiment was set in five phases; baseline for skill transfer and adaptability, learning and evaluation for adaptability and skill transfer respectively. The performance of the participants was characterized by using the time to target, effort index, and length of the trajectory. Experimental results suggested that (1) peer-to-peer interactions among paired beginners enhanced the motor learning most, (2) individuals practicing on their own (learning as a single) showed better motor learning than practicing under the expert's guidance, and (3) regarding the adaptability, peer-to-peer interactions induced higher adaptability to a new partner than the novice-to-expert interactions while attempting to achieve a common goal together. Thus, we conclude that the peer-to-peer interactions under a collaborative task can realize the best motor learning of the motor skills through the new environmental dynamics, and adaptability to others in order to achieve a goal together. We suggest that the peer-to-peer learning can induce both adaptability to others and learning of motor skills through the unknown environmental dynamics under mutual interactions. On the other hand, during the peer-to-peer interactions, the novice can learn how to coordinate motion with his/her partner (even though one is a new partner), and thus, is able to learn the motor skills through new environmental dynamics.

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“…En consecuencia, una interfaz háptica (IH) es aquel dispositivo que se encarga de reproducir en el usuario lo captado por la sensación de contacto y manipulación de un objeto que se encuentra dentro de un ambiente virtual o en un entorno remoto, simulando las características esenciales al tacto y sujeción de un objeto real, tales como temperatura, textura, peso, contorno, entre otros (Golledge et al, 2006;Hernantes et al, 2012).…”

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