Development of Oral Rehabilitation Robot for Massage Therapy

Koga, H.; Usuda, Y.; Matsuno, Masanori; Ogura, Yu; Ishii, Hiroyuki; Solı́s, Jorge; Takanishi, Atsuo; Katsumata, Akitoshi

doi:10.1109/itab.2007.4407357

Cited by 28 publications

(14 citation statements)

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“…Within this paper we focus on robot-initiated touch. Various robotic systems for healthcare involve robotinitiated touch, including facial massage [20], skin care [41], patient transfer [30], surgery [18], and hygiene [19].…”

Section: Human-robot Touchmentioning

confidence: 99%

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An Investigation of Responses to Robot-Initiated Touch in a Nursing Context

Chen

King

Thomaz

et al. 2013

Int J of Soc Robotics

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Physical human-robot interaction has the potential to be useful in a number of domains, but this will depend on how people respond to the robot's actions. For some domains, such as healthcare, a robot is likely to initiate physical contact with a person's body. In order to investigate how people respond to this type of interaction, we conducted an experiment with 56 people in which a robotic nurse autonomously touched and wiped each participant's forearm. On average, participants had a favorable response to the first time the robot touched them. However, we found that the perceived intent of the robot significantly influenced people's responses. If people believed that the robot intended to clean their arms, the participants tended to respond more favorably than if they believed the robot intended to comfort them, even though the robot's manipulation behavior was the same. Our results suggest that roboticists should consider this social factor in addition to the mechanics of physical interaction. Surprisingly, we found that participants in our study responded less favorably when given a verbal warning prior to the robot's actions. In addition to these main results, we present post-hoc analyses of participants' galvanic skin responses (GSR), open-ended responses, attitudes towards robots, and responses to a second trial.

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Section: Human-robot Touchmentioning

confidence: 99%

“…Other researchers used a force magnitude threshold of 39.2 N with an oral rehabilitation robot [20]. Various factors could influence the force range that a person would find comfortable, including the contact surface over which the applied force is distributed, and the part of the person's body with which contact has been made.…”

Section: Safetymentioning

confidence: 99%

An Investigation of Responses to Robot-Initiated Touch in a Nursing Context

Chen

King

Thomaz

et al. 2013

Int J of Soc Robotics

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“…This robot was designed to provide appropriate massage therapy for maxillofacial lesions such as TMD, dry mouth, and dysphagia in patients and elderly people [18]. Introduction of the robot system has enabled us to assess the quality of treatment objectively and provide more effective treatment for more patients.…”

Section: Discussionmentioning

confidence: 99%

“…1 (WAO-1)'' was recently developed, in a joint research program involving Waseda and Asahi Universities, to ameliorate the oral and maxillofacial problems of elderly people, for example dry mouth, TMD, and dysphagia [18]. A clinical trial of this robot is scheduled to be carried out at Aichi-Gakuin and Tsurumi Universities.…”

Section: Introductionmentioning

confidence: 99%

An oral rehabilitation robot for massaging the masseter and temporal muscles: a preliminary report

et al. 2009

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Objectives This study determined suitable conditions for masseter and temporal muscle massage using a specially fabricated robot and evaluated its effects on patients with TMJ dysfunction associated with myofascial pain. Methods The robot was designed with two arms with six degrees-of-freedom, and equipped with plungers. A phase-1 trial examined 22 healthy volunteers to determine its safety and suitable massage pressure, examining three different pressures. The volunteers evaluated their comfort, warmth, and ease of mouth opening by use of a visual analogue scale (VAS). A phase-2 trial examined the safety, suitable dose regimen, and efficacy in 12 patients. Maximal mouth opening was measured, and muscle pain and massage were evaluated subjectively. Results The robot was safe in the phase-1 trial, except for two massages in which the pressure was excessive. Massages at 6-10 N were given the highest VAS scores. In phase 2, the massage pressure was arbitrary and each muscle was massaged seven times for 1 min, three times every two weeks. After evaluating the efficacy, additional treatments were performed at a greater pressure or for longer. The massage treatment was very effective for most patients. Conclusion The massage treatment was safe and effective for most patients when administered at a pressure of 6-10 N seven times for 1 min per muscle every two weeks. The robot may constitute a useful tool for treating TMJ dysfunction associated with myofascial pain.

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“…Existing massage robot's researches mainly focus on the trajectory reproduction of massagist technique and the changing force curve [1][2][3][4][5][6][7]. Robotic massage is implemented by a redundant manipulator equipped with a tactile sensor, in which the hybrid impedance control is adopted [8][9][10].…”

Section: ⃝Icros Kiee and Springer 2016mentioning

confidence: 99%

Strategies for feet massage robot to position the pelma acupoints with model predictive and real-time optimization

Wei

Gu²,

Xu-jing

et al. 2016

Int. J. Control Autom. Syst.

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It is one of critical factors for a massage robot to find and position the acupuncture point precisely in order to cure the diseases. Based on large amount of sample data offline, Chinese medical empirical knowledge is also introduced to build the prediction model. The massagist prescription and the robot mechanism devise are both considered for robot positioning. Least squares method is of simplicity, easy to use and high efficiency. Its real-time calculation is very effective, too. A modeling method for robot positioning is proposed based on least squares. Knowledge consultation is set for the calculation of acupoint position. The robot needs to get the feature points of a foot to be massaged. The foot contour sampling data are divided into piecewise curve fitting. Qlearning is adopted to optimize the robot positioning for they are model free. CMAC (Cerebellar Model Articulation Controller) cerebellum model is incorporated into the function approximation of Q learning. The learning system is rewarded by referring to the strengths of instrumental signal. By the direct representation, the model of human pelma acupoint is expressed with the vector variables and formal computer language. Through prediction model's calculation, the robot will work out the rough position of acupuncture point. Meanwhile, Q learning does the online adjustment for accurate location. These strategies provide for the robot to automatically search and position the pelma acupoint with little real-time computation and storage. The idea of this paper also prompts a research cue for the development of Chinese medical standardization.

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Development of Oral Rehabilitation Robot for Massage Therapy

Cited by 28 publications

References 1 publication

An Investigation of Responses to Robot-Initiated Touch in a Nursing Context

An Investigation of Responses to Robot-Initiated Touch in a Nursing Context

An oral rehabilitation robot for massaging the masseter and temporal muscles: a preliminary report

Strategies for feet massage robot to position the pelma acupoints with model predictive and real-time optimization

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