PROJECTS

[引用はこちら/Reference]

Hiyori Tsuji, Takumi Yamamoto, Maiko Kobayashi, Kyoshiro Sasaki, Noriko Aso, Yuta Sugiura, CradlePosture: Camera-Based Approach for Estimating Neonate’s Posture Based on Caregiver’s Holding Behaviors, 2025 IEEE/SICE International Symposium on System Integration, January 21-24, 2025, Munich, Germany

抱き動作において子どもの身体姿勢は安全を確保するために重要となる．本研究では，ユーザが適切な抱き動作を学習するシステムを想定し，抱き動作時の新生児の身体姿勢を計測する手法を提案する．本手法では，カメラで撮影したユーザの抱き動作から，抱かれている新生児の姿勢角を推定する．ユーザの骨格推定を行い，その情報を基に新生児の姿勢角を推定する回帰モデルを構築することで，実際の新生児に直接センサを装着することなく角度を推定することが可能となる．回帰モデルの評価を行った結果，個人内検証では傾斜角（人形を水平な状態から頭を上げる方向に胸部を中心に回転させた角度），内転角（人形を体軸を中心に水平な状態から人の体側に回転させた角度）ともに高い推定が可能である一方で，個人間検証では傾斜角のみ推定が可能であると分かった．

In holding behaviors, the posture of a neonate is crucial for ensuring safety. In this study, assuming a system for users to learn proper holding posture, we propose an approach for measuring the posture of a neonate during holding behaviors. Our method estimates the posture angles of a neonate based on holding behaviors captured by a camera. By using regression models to estimate a neonate’s posture angles based on the posture of a person holding a neonate, estimation can be made of the posture angles of the neonate without directly attaching sensors to an actual neonate. The results show for within-participant validation, both the inclination angle– the angle at which the doll is rotated around its chest from a horizontal position towards raising its head– and the adduction angle– the angle at which the doll is rotated from a horizontal position around its body axis towards the person’s body side– could be estimated with high accuracy, but for between-participant validation, only inclination angles could be estimated.