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publications
Nurse Activity Recognition in Gastrostomy Tube Feeding Using Video-Based Pose with Large Language Model-Guided Features
Published in 7th International Conference on Activity and Behavior Computing (ABC 2025), Khalifa University, Abu Dhabi, UAE, 2025
In this paper, we improve nursing activity recognition in gastrostomy tube feeding (GTF) with temporal variations and sequential errors by integrating activity context to Large Language Model (LLM) for guided feature selection and post-processing. GTF is a delicate nursing procedure that allows direct stomach access in children for supplemental feeding or medication, but it is underrepresented in datasets, posing challenges for accurate detection. Manual feature engineering may overlook subtle but important motion cues, particularly in opening and closing the gastrostomy cover, where changes are minimal and localized to the hands. Additionally, sequence inconsistencies and missed activities limit the effectiveness of pose estimation methods in healthcare. Leveraging the contextual adaptability of LLMs, we generate new features suggested by the language model, combining them with hand-crafted features to optimize the model. For post-processing, a sliding window smoothing method based on majority voting is applied. To mitigate duration-based discrepancies, a priority handling is incorporated for short-duration activities to pre- serve their recognition accuracy while addressing repeated labels caused by long-duration actions. Particularly, we applied activity recognition to our unique GTF dataset collected from recorded video of two nurses, two students, and two staff members for three days with 17 labeled activities. Keypoints are extracted using YOLO11. Compared to the baseline, the application of LLM to GTF nurse activity recognition with pose estimation improved the Random Forest performance of F1-score from 54% to 57%. Additionally, incorporating the sliding window smoothing approach based on majority voting with short-term action priority, resulted in a 3% further increase.
Recommended citation: Lingfeng Zhao*, Christina Garcia, Shunsuke Komizunai, Noriyo Colley, Atsuko Sato, Mayumi Kouchiyama, Toshiko Nasu, Sozo Inoue http://zhao-lingfeng.github.io/files/paper1.pdf
大規模言語モデルを活用した経管栄養における看護行動認識の高精度化（発表予定）
Published in 情報処理学会 マルチメディア、分散、協調とモバイル（DICOMO2025）シンポジウム（発表予定）, 2025
本研究の目的は，経管栄養（Gastrostomy Tube Feeding：GTF）の看護行動認識精度を向上させることである．GTFの動作は，短時間動作が長時間動作に埋もれやすく，時系列制約が考慮されないという課題があるため，既存手法では適切な認識が困難である．本研究では，ビデオに基づく姿勢推定と大規模言語モデル（LLM）を活用し，時系列文脈を考慮した特徴量の自動生成および手作業設計特徴との統合により認識精度の向上を図る．さらに，多数決による時間窓平滑化と短時間動作の優先処理を組み合わせた後処理手法を提案し，誤検出の低減を試みた．実験では，中国労災病院で収集されたGTFデータセットを用いて提案手法の有効性を検証した．このデータセットには，看護師、看護教員、看護学生の3グループが3日間にわたり実施した計17種類の動作が含まれている．実験の結果，従来の手法と比較して，F1スコアが53%から66%へと向上し，後処理手法の導入により，最終的にF1スコアは68%に向上した．本研究は，看護師が行う多数のスキルの中でも特に誤認識リスクの高い経管栄養に注目し，その認識精度を向上させることで，教育現場における定量的かつ客観的な評価支援を実現する可能性を示した．
Recommended citation: Zhao Lingfeng*, Christina Garcia, 小水内 俊介, コリー 紀代, 佐藤 敦子, 河内山 真由美, 那須 敏子, 井上 創造 http://zhao-lingfeng.github.io/files/paper2.pdf
talks
Talk 1 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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