Development of Finger Motion Skill Learning Support System based on Data Gloves

Iwasako, Kazuma; Soga, Masato; Taki, Hirokazu

doi:10.1016/j.procs.2014.08.167

Cited by 9 publications

(20 citation statements)

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“…The advantage of the contextual information approach over other punctual approaches is that users receive information not only about the current time step (i.e. the level of the target/applied force at a specific time), but they also receive information both about the evolution of their performance in the past and about the future (how the target force will change), which also provides quantitative information about the magnitudes of the forces, as suggested by Swinnen (1996), Iwasako et al (2014), and Santos (2016). All this information could reinforce the user's mental model about the target force pattern and improve user performance.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, the term "mental model" refers to the force profile that learners have to create in their mind. The objective of this work is not to measure how users create this model in their mind; nevertheless, according to Swinnen (1996) and Iwasako et al (2014) feedback is important in motor skills learning in order to help trainees create a correct mental model of the task (they can recognize errors and improve their performance), while Santos (2016) concludes that multimodal systems should have to provide information about the whole task and the progress of the trainee throughout the training session. According to Williams and Carnahan (2014), training procedures that allow trainees to understand the whole task should be more helpful for learning.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Learning force patterns with a multimodal system using contextual cues

Rodríguez

Gutiérrez

Portillo

et al. 2018

International Journal of Human-Computer Studies

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Highlights  A new interaction technique based on contextual information using a multimodal system for force pattern learning is proposed. Using the contextual information approach, the trainees obtain information about the evolution of their performance along the time (helping to correct the errors). The findings suggest that the contextual information provides a better trainee's performance than the punctual information. One drawback of the training strategies based on punctual feedback is the difficulty that users have in interpreting the information. The contextual approach could be used to extend the capabilities of haptic training procedures based on haptic guidance to provide information about the level of force to be applied during the execution of a specific task.Abstract -Previous studies on learning force patterns (fine motor skills) have focused on providing "punctual information", which means users only receive information about their performance at the current time step. This work proposes a new approach based on "contextual information", in which users receive information not only about the current time step, but also about the past (how the target force has changed over time) and the future (how the target force will change). A test was run to compare the performance of the contextual approach in relation to the punctual information, in which each participant had to memorize and then reproduce a pattern of force after training with a multimodal system. The findings suggest that the contextual approach is a useful strategy for force pattern learning. The advantage of the contextual information approach over the punctual information approach is that users receive information about the evolution of their performance (helping to correct the errors), and they also receive information about the next forces to be exerted (providing them with a better understanding of the target force profile). Finally, the contextual approach could be implemented in medical training platforms or surgical robots to extend the capabilities of these systems.

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Section: Discussionmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Learning force patterns with a multimodal system using contextual cues

Rodríguez

Gutiérrez

Portillo

et al. 2018

International Journal of Human-Computer Studies

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“…When user studies are carried out (in 12 of the 17 systems analysed), they are usually done so with a limited number of participants, and with the goal to show that the system functions as expected. Some aspects of the users experience are also analysed, such as whether users have positive views of the system (Takahata et al 2004;Iwasako et al 2014;Kikukawaa et al 2014), whether they improve their performance while using the system (Kwon and Gross 2005;van der Linden et al 2011;Ghasemzadeh et al 2009;Portillo-Rodriguez et al 2008), or how users respond to different feedback alternatives (Dancu 2012;Lieberman and Breazeal 2007). In another vein, most of the learning environments reviewed here address novice users.…”

Section: Motor Skills Learning Ouside the Aied Communitymentioning

confidence: 99%

“…The sensing of the movement is done i) through wearable devices on the body or the instrument used -when it exists (i.e., golf club, musical instrument, oar, etc. )-such as in (Oakes et al 2015;Amma et al 2014;James et al 2011;Kikukawaa et al 2014;Ghasemzadeh et al 2009;Iwasako et al 2014;Baca and Kornfeind 2006;van der Linden et al 2011;Takahata et al 2004;Spelmezan and Borchers 2008), ii) though optical motion capture devices placed in the environment (which usually require markers on the participants body, except for the Kinect sensor) such as in (Bloomfield and Badler 2008;Lieberman and Breazeal 2007;Dancu 2012; Miaw and Raskar 2010), or iii) combining both, that is within the user and instruments and from outside such as in (Rauter et al 2011;Kwon and Gross 2005;Portillo-Rodriguez et al 2008).…”

Section: Sensing Movement In Motor Skills Learning Systemsmentioning

confidence: 99%

“…Feedback is crucial in motor skills learning (Swinnen 1996). For example, learners do not often recognize motion errors, and experts need to provide objective advice to learners (Iwasako et al 2014). Traditionally, a human instructor or coach is used to support motor skills learning (Wulf et al 2010) following the apprenticeship model (i.e., learning by observation of an expert) (Christensson 2005), which can also be done using multimedia content (Kwon and Gross 2005).…”

Section: Introductionmentioning

confidence: 99%

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Training the Body: The Potential of AIED to Support Personalized Motor Skills Learning

Santos

2016

Int J Artif Intell Educ

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This paper argues that the research field of Artificial Intelligence in Education (AIED) can benefit from integrating recent technological advances (e.g., wearable devices, big data processing, 3D modelling, 3D printing, ambient intelligence) and design methodologies, such as TORMES, when developing systems that address the psychomotor learning domain. In particular, the acquisition of motor skills could benefit from individualized instruction and support just as cognitive skills learning has over the last decades. To this point, procedural learning has been considered since the earliest days of AIED (dating back to the 1980's). However, AIED developments in motor skills learning have lagged significantly behind. As technology has evolved, and supported by the do-ityourself and quantified-self movements, it is now possible to integrate emerging interactive technologies in order to provide personal awareness and reflection for behavioural change at low cost and with low intrusion. Many activities exist that would benefit from personalizing motor skills learning, such as playing a musical instrument, handwriting, drawing, training for surgery, improving the technique in sports and martial arts, learning sign language, dancing, etc. In this context, my suggestions for AIED research in the coming 25 years focus on addressing challenges regarding 1) modelling the psychomotor interaction, and 2) providing appropriate personalized psychomotor support.

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Human–Machine Interaction Method of Virtual Assembly

Wang

Xie

2021

Virtual Reality Technology in Mining Machinery

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Virtual assembly human-computer interaction is a kind of interactive action matching with the characteristics of Three-dimensional environment itself, which enables users to get a direct immersive feeling in the virtual scene. In the virtual reality assembly system for coal machine equipment, various human-computer interaction devices are used to enable designers and researchers of coal machine equipment to interact with the system with more abundant means, so as to find the problems existing in the equipment and provide strong technical support for equipment improvement and new product research and development.Three-dimensional interaction technology is a "control-display" mapping relationship, user devices (such as mouse, keyboard, data gloves, force feedback device, etc.) input control information to the system, and then the system output the execution results to the user. There are many tasks involved in 3D interaction, and the main human-machine interaction in this system is assembly interaction. The assembly interaction is achieved through three methods: one is based on the traditional mouse and keyboard through the operator or robot to realize the full-scale browsing of the scene to realize the interaction; the second is based on the data glove and the position tracker to realize the interaction, the last one is a human-computer interaction technology which is based on force feedback. The specific implementations of these interaction modes will be introduced below. Mouse and Keyboard Human-Computer Interaction Scene Interaction Based on TrackballThe assembly scene interaction does not refer to the operation of the entire assembly scene (including the objects in it), but to change the position of the camera or observer

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Development of Finger Motion Skill Learning Support System based on Data Gloves

Cited by 9 publications

References 0 publications

Learning force patterns with a multimodal system using contextual cues

Learning force patterns with a multimodal system using contextual cues

Training the Body: The Potential of AIED to Support Personalized Motor Skills Learning

Human–Machine Interaction Method of Virtual Assembly

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