TY - JOUR
KW - Artificial intelligence
KW - HCI
KW - Hidden Markov models
KW - Human computer interaction
KW - Human computer interfaces
KW - Inertial navigation systems
KW - Inertial sensor
KW - Inertial sensors
KW - Intangible cultural heritages
KW - Learning systems
KW - machine learning
KW - Methodological frameworks
KW - Modeling and recognition
KW - perception
KW - Real time
KW - Sensory perception
KW - Statistical modeling
KW - Wheels
AU - S. Manitsaris
AU - A. Glushkova
AU - F. Bevilacqua
AU - F. Moutarde
AB - This research has been conducted in the context of the ArtiMuse project that aims at the modeling and renewal of rare gestural knowledge and skills involved in the traditional craftsmanship and more precisely in the art of wheel-throwing pottery. These knowledge and skills constitute intangible cultural heritage and refer to the fruit of diverse expertise founded and propagated over the centuries thanks to the ingeniousness of the gesture and the creativity of the human spirit. Nowadays, this expertise is very often threatened with disappearance because of the difficulty to resist globalization and the fact that most of those "expertise holders" are not easily accessible due to geographical or other constraints. In this article, a methodological framework for capturing and modeling gestural knowledge and skills in wheel-throwing pottery is proposed. It is based on capturing gestures using wireless inertial sensors and statistical modeling. In particular, we used a system that allows for online alignment of gestures using a modified Hidden Markov Model. This methodology is implemented into a human-computer interface, which permits both the modeling and recognition of expert technical gestures. This system could be used to assist in the learning of these gestures by giving continuous feedback in real time by measuring the difference between expert and learner gestures. The system has been tested and evaluated on different potters with rare expertise, which is strongly related to their local identity.
BT - ACM Journal on Computing and Cultural Heritage
DA - jul
DO - 10.1145/2627729
LA - English
M1 - 2
N1 - Publisher: Association for Computing Machinery
N2 - This research has been conducted in the context of the ArtiMuse project that aims at the modeling and renewal of rare gestural knowledge and skills involved in the traditional craftsmanship and more precisely in the art of wheel-throwing pottery. These knowledge and skills constitute intangible cultural heritage and refer to the fruit of diverse expertise founded and propagated over the centuries thanks to the ingeniousness of the gesture and the creativity of the human spirit. Nowadays, this expertise is very often threatened with disappearance because of the difficulty to resist globalization and the fact that most of those "expertise holders" are not easily accessible due to geographical or other constraints. In this article, a methodological framework for capturing and modeling gestural knowledge and skills in wheel-throwing pottery is proposed. It is based on capturing gestures using wireless inertial sensors and statistical modeling. In particular, we used a system that allows for online alignment of gestures using a modified Hidden Markov Model. This methodology is implemented into a human-computer interface, which permits both the modeling and recognition of expert technical gestures. This system could be used to assist in the learning of these gestures by giving continuous feedback in real time by measuring the difference between expert and learner gestures. The system has been tested and evaluated on different potters with rare expertise, which is strongly related to their local identity.
PY - 2014
T2 - ACM Journal on Computing and Cultural Heritage
TI - Capture, Modeling, and Recognition of Expert Technical Gestures in Wheel-Throwing Art of Pottery
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979833345&doi=10.1145%2f2627729&partnerID=40&md5=e685801c0e916dcbf4996855b1b3097a
VL - 7
SN - 15564673 (ISSN)
ER -