Offline statistical analysis of gestural skills in pottery interaction

Computer Assisted Learning

Manitsaris

2017

Self Cite

This paper presents a methodological framework for the use of gesture recognition technologies in the learning/mastery of the gestural skills required in wheel-throwing pottery. In the case of self-instruction or training, learners face difficulties due to the absence of the teacher/expert and the consequent lack of guidance. Motion capture technologies, machine learning, and gesture recognition may provide a way of overcoming such issues. The proposed methodology is used to record and model expert gestures and then to compare this model in real time with the gestures performed by the learner. Differences in kinematic aspects such as hand distances are detected, and optical/sonic sensorimotor feedback is provided to the learner by the system, alerting him/ her when errors occur and guiding him/her to achieve better results. In the case described here, the system was evaluated with 11 learners. With the use of our system, the gestural performance of learners during self-training has been improved in comparison to cases of self-training without computer assistance. they also demand experience that involves gestural know-how that is controlled by hand movement intelligence and human creativity. Such experience is not limited only to the implementation of simple manual tasks but coexists with tacit knowledge. This tacit knowledge and the necessary motor skills are diffused within communities of practice, which interact with each other and ensure that the necessary knowledge and skills are transmitted from one generation to the next. Motor skills may include specific body postures to make preparatory adjustments as well as the manipulation of tools, objects, or materials used by the hands and fingers. Gestural know-how is considered at one and the same time to be traditional, contemporary, and living because it not only refers to past knowledge but also to contemporary experience that evolves through time. This transmission of experience and knowledge has an economic, social, and cultural value, because it affects not only the well-being of the job holder or artisan but also the sustainable development of society in general.Within this context, the transmission of gestural know-how is typically made person-to-person by establishing a mirroring system between the expert and the learner. Using this "me-to-you" observation system ("me" as expert and "you" as learner), the learner perceives and understands the movements of the expert as meaningful action and not simply as displacement of space. Nevertheless, access to this expertise of gestural skills as professional know-how can sometimes be difficult to achieve due to geographical or time constraints or due to the limited number of experts that hold these skills. Within industry especially, the availability of experts for teaching or training purposes may be limited due to lack of time, in the sense that extracting an expert from his/her everyday workspace may impact on productivity.Also, in relation to craftsmanship, a number of craft traditions may include "s...

Section: Discussionmentioning

confidence: 99%

Gesture recognition and sensorimotor learning‐by‐doing of motor skills in manual professions: A case study in the wheel‐throwing art of pottery

Computer Assisted Learning

Manitsaris

2017

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“…For the recognition of the postural risk factors, four sets of Hidden Markov Models (HMM) were used, one for each factor. HMMs were used because they have proved to be a prominent tool for gesture recognition [8,9]. In Fig.…”

Section: Recognition Of Risk Factors On Awkward Gestures By Using Hidden Markov Modelsmentioning

confidence: 99%

Hidden Markov Modelling And Recognition Of Euler-Based Motion Patterns For Automatically Detecting Risks Factors From The European Assembly Worksheet

Padilla

Menychtas

2020 IEEE International Conference on Image Processing (ICIP)

et al. 2020

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To prevent work-related musculoskeletal disorders (WMSD) the ergonomists apply manual heuristic methods to determine when the worker is exposed to risk factors. However, these methods require an observer and the results can be subjective. This paper proposes a method to automatically evaluate the ergonomic risk factors when performing a set of postures from the ergonomic assessment worksheet (EAWS). Joint angle motion data have been recorded with a full-body motion capture system. These data modeled the motion patterns of four different risk factors, with the use of hidden Markov models (HMMs). Based on the EAWS, automated scores were assigned by the HMMs and were compared to the scores calculated manually. Because the method proposed here is intrusive and requires expensive equipment, kinematic data from a reduced set of two sensors was also evaluated.

“…Then, we have selected one indicative expert data sequence and trained ArtOrasis system with it. However according to the statistical analysis done in (Volioti et al, 2014) all the 11 joints are not involved in pottery…”

Section: Comparison Of Pottery Expert and Learner Gesturesmentioning

confidence: 99%

Gesture Recognition Technologies for Gestural Know-how Management - Preservation and Transmission of Expert Gestures in Wheel Throwing Pottery

Proceedings of the 7th International Conference on Computer Supported Education

Manitsaris

2015

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The acquisition of gestural know-how in manual professions constitutes a real challenge since it passes from master to learner, through a many years long « in person » transmission. However this binding transmission is not always possible for practical reasons; the learner must train himself alone, by using traditional Knowledge Management tools such as e-documentation and multimedia contents. These tools present important limitations, only providing the learner expert knowledge in a descriptive way, with a low attractiveness and interaction level, without any sensorimotor feedback. It thus becomes crucial to find novel ways to preserve and transmit know-how. In this work we present the idea of a methodological framework for gestural know-how management in wheel throwing pottery, based on motion capture and gesture recognition technologies. In combination with machine learning techniques, they permit to model the practical, cinematic aspects of potter's expertise. These technologies can be used to compare experts' and learners' simulated performances and to provide real-time feedback to the learner, guiding him in the adjustment of his gestures. The final goal is to propose a novel and highly interactive embodied pedagogical application for gestural know-how transmission, supporting « self » trainings, and making them more efficient.