Development of a mechanotherapy unit for examining the possibility of an intelligent massage robot

Kume, Minoru; Morita, Yoshifumi; Yamauchi, Yukiharu; Aoki, Hiroyuki; Yamada, Moyuru; Tsukamoto, Kazuyoshi

doi:10.1109/iros.1996.570698

Cited by 30 publications

(8 citation statements)

References 4 publications

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“…Thus, in recent years, robot massaging has become a research hotspot in the field of robots. In 1996, the Mechatronics Research Center of Japan's Sanyo Electric Co., Ltd. designed a mechanical therapy unit and verified the feasibility of developing an intelligent massage robot (Kume et al, 1996 ). In 2000s, Toyohashi University of Technology and Japan's Gifu Institute of Technology carried out research on humanoid multi-finger massage robots with four fingers and 13 joints (Zhang and Zhang, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive

Fahmy

et al. 2020

Front. Neurorobot.

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With requirements to improve life quality, smart homes, and healthcare have gradually become a future lifestyle. In particular, service robots with human behavioral sensing for private or personal use in the home have attracted a lot of research attention thanks to their advantages in relieving high labor costs and the fatigue of human assistance. In this paper, a novel force-sensing-and robotic learning algorithm-based teaching interface for robot massaging has been proposed. For the teaching purposes, a human operator physically holds the end-effector of the robot to perform the demonstration. At this stage, the end position data are outputted and sent to be segmented via the Finite Difference (FD) method. A Dynamic Movement Primitive (DMP) is utilized to model and generalize the human-like movements. In order to learn from multiple demonstrations, Dynamic Time Warping (DTW) is used for the preprocessing of the data recorded on the robot platform, and a Gaussian Mixture Model (GMM) is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. After that, a Gaussian Mixture Regression (GMR) algorithm is applied to generate a synthesized trajectory to minimize position errors. Then a hybrid position/force controller is integrated to track the desired trajectory in the task space while considering the safety of human-robot interaction. The validation of our proposed method has been performed and proved by conducting massage tasks on a KUKA LBR iiwa robot platform.

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Section: Introductionmentioning

confidence: 99%

An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive

Fahmy

et al. 2020

Front. Neurorobot.

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“…Therefore, it comes as no surprise that robotic systems have been developed to try to recreate these benefits by mimicking human massage patterns [2]. While there exist several automated massage platforms on the market [3][4][5][6][7][8], they are mainly limited to massaging the back and provide very simple mechanical and repetitive movements. Massage is a challenging task as it requires reactive and coordinated arm-finger motion planning, safe and compliant interaction as the robot is in direct contact with the human.…”

Section: Introductionmentioning

confidence: 99%

Arm-hand motion-force coordination for physical interactions with non-flat surfaces using dynamical systems: Toward compliant robotic massage

Khoramshahi

Henriks

Naef

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

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Many manipulation tasks require coordinated motions for arm and fingers. Complexity increases when the task requires to control for the force at contact against a non-flat surface; This becomes even more challenging when this contact is done on a human. All these challenges are regrouped when one, for instance, massages a human limb. When massaging, the robotic arm is required to continuously adapt its orientation and distance to the limb while the robot fingers exert desired patterns of forces and motion on the skin surface. To address these challenges, we adopt a Dynamical System (DS) approach that offers a unified motion-force control approach and enables to easily coordinate multiple degrees of freedom. As each human limb may slightly differ, we learn a model of the surface using support vector regression (SVR) which enable us to obtain a distance-to-surface mapping. The gradient of this mapping, along with the DS, generates the desired motions for the interaction with the surface. A DS-based impedance control for the robotic fingers allows to control separately for force along the normal direction of the surface while moving in the tangential plane. We validate our approach using the KUKA IIWA robotic arm and Allegro robotic hand for massaging a mannequin arm covered with a skin-like material. We show that our approach allows for 1) reactive motion planning to reach for an unknown surface, 2) following desired motion patterns on the surface, and 3) exerting desired interaction forces profiles. Our results show the effectiveness of our approach; especially the robustness toward uncertainties for shape and the given location of the surface.

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“…Kume et al 1 developed a massage robot with two end-effectors to implement the repetitive action of grasping on soft tissues. Massage is, however, laborious, time-consuming, and monotonous.…”

Section: Introductionmentioning

confidence: 99%

Anthropomorphic robotic arm with integrated elastic joints for TCM remedial massage

Huang

Li²,

Huang³

et al. 2014

Robotica

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For reproducing the manipulation of Traditional Chinese Medicine (TCM) remedial massage and meanwhile guaranteeing safety, a 4-degree-of-freedom anthropomorphic robotic arm with integrated elastic joints is developed, and a passivity-based impedance control is used. Due to the series elasticity, integrated joints may minimize large forces that occur during accidental impacts, and, further, may offer more accurate and stable force control and a capacity for energy storage. Human expert's fingertip force curve in the process of massage therapy is acquired in vivo by a dedicated measurement device. Then three massage techniques, pressing, kneading, and plucking, are implemented by the soft arm, respectively, on torso model in vitro and on human body in vivo. Experimental results show that the developed robotic arm can effectively replicate the TCM remedial massage techniques.

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Development of a mechanotherapy unit for examining the possibility of an intelligent massage robot

Cited by 30 publications

References 4 publications

An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive

An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive

Arm-hand motion-force coordination for physical interactions with non-flat surfaces using dynamical systems: Toward compliant robotic massage

Anthropomorphic robotic arm with integrated elastic joints for TCM remedial massage

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