TY - GEN
T1 - Motion Estimation of Plush Toys Through Detachable Acceleration Sensor Module and Machine Learning
AU - Kato, Kaho
AU - Ienaga, Naoto
AU - Sugiura, Yuta
N1 - Funding Information:
This work was supported by JST AIP-PRISM JPMJCR18Y2.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - We propose a system that estimates motion in a plush toy by means of an attached sensor device and gives the user a sound feedback corresponding to the predicted motion. We have created several different types of detachable acceleration sensor modules as an accessory for the toy. This module can be attached at any position on a commercially available plush toy. The user can create original motions by teaching through demonstration, and the captured sensor data is converted into 2D image data. We extracted the histograms of oriented gradients (HOG) features and performed learning with a support vector machine (SVM). In an evaluation, we decided the attaching parts and motions in advance, and participants moved a plush toy in accordance with these. Results showed that it was possible to estimate the plush toy’s motion with high accuracy, and the system was able to register a sound for each motion.
AB - We propose a system that estimates motion in a plush toy by means of an attached sensor device and gives the user a sound feedback corresponding to the predicted motion. We have created several different types of detachable acceleration sensor modules as an accessory for the toy. This module can be attached at any position on a commercially available plush toy. The user can create original motions by teaching through demonstration, and the captured sensor data is converted into 2D image data. We extracted the histograms of oriented gradients (HOG) features and performed learning with a support vector machine (SVM). In an evaluation, we decided the attaching parts and motions in advance, and participants moved a plush toy in accordance with these. Results showed that it was possible to estimate the plush toy’s motion with high accuracy, and the system was able to register a sound for each motion.
KW - Interactive plush toy
KW - Machine learning
KW - Teaching by demonstration
UR - http://www.scopus.com/inward/record.url?scp=85069634223&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-23528-4_39
DO - 10.1007/978-3-030-23528-4_39
M3 - Conference contribution
AN - SCOPUS:85069634223
SN - 9783030235277
T3 - Communications in Computer and Information Science
SP - 279
EP - 286
BT - HCI International 2019 - Posters - 21st International Conference, HCII 2019, Proceedings
A2 - Stephanidis, Constantine
PB - Springer Verlag
T2 - 21st International Conference on Human-Computer Interaction, HCI International 2019
Y2 - 26 July 2019 through 31 July 2019
ER -