Faculty Profiles - INOUE Sozo

Honors and Awards - INOUE Sozo

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Best Paper Award

International Conference on Activity and Behavior Computing (ABC) Toward Recognizing Nursing Activity in Endotracheal Suctioning Using Video-based Pose Estimation 2023.09.07

Hoang Anh Vy Ngo, Quynh N Phuong Vu, Noriyo Colley, Shinji Ninomiya, Satoshi Kanai, Shunsuke Komizunai, Atsushi Konno, Misuzu Nakamura, Sozo Inoue

Excellent Paper Award

International Conference on Activity and Behavior Computing (ABC) Activity Prediction Method for Nursing Care Records with Missing Entries 2024.05.28

Ryuichiro Okuda, Qingxin Xia, Takuya Maekawa, Takahiro Hara, Sozo Inoue

Excellent Paper Award

International Conference on Activity and Behavior Computing (ABC) A Relabeling Approach to Signal Patterns for Beacon-based Indoor Localization in Nursing Care Facility 2023.09.07

Chrisitina A. Garcia, Quynh N Phuong Vu, Haru Kaneko, Sozo Inoue

Excellent Paper Award

International Conference on Activity and Behavior Computing (ABC) Function Estimation of Multiple IoT Devices by Communication Traffic Analysis 2022.10.27

Yuichi Hattori, Yutaka Arakawa,Sozo Inoue

Best Paper Award

The 40th Joint Conference on Asia-Pacific Association of Medical Informatics (APAMI) Enhancing Nursing Care Records with A Spoken Dialogue System based on Smartphones 2020.12.01

Tittaya Mairittha, Nattaya Mairittha, Sozo Inoue

Country：Japan

Background and Objective: This study describes the integration of a spoken dialogue system and
nursing records on an Android smartphone application intending to help nurses reduce documentation
time and improve the overall experience of a healthcare setting. The application also incorporates with
collecting personal sensor data and activity labels for activity recognition.
Methods: We developed a joint model based on a bidirectional long-short term memory and conditional random fields (Bi-LSTM-CRF) to identify user intention and extract record details from user
utterances. Then, we transformed unstructured data into record inputs on the smartphone application.
Results: The joint model achieved the highest F1-score at 96.79%. Moreover, we conducted an experiment to demonstrate the proposed model's capability and feasibility in recording in realistic settings.
Our preliminary evaluation results indicate that when using the dialogue-based, we could increase the
percentage of documentation speed to 58.13% compared to the traditional keyboard-based.
Conclusions: Based on our findings, we highlight critical and promising future research directions
regarding the design of the efficient spoken dialogue system and nursing records.

学生優秀講演賞

SOFT九州支部学術講演会自然言語を用いた構造化介護記録アプリの評価 2020.11.28

田中龍之介, ティッタヤ　マイリッタ, 井上創造

Best Paper Award

Activity and Behavior Computing, Springer Nature Improvement of Human Action Recognition Using 3D Pose Estimation 2020.08.29

Kohei Adachi, Paula Lago, Tsuyoshi Okita, Sozo Inoue

学生奨励賞

情報処理学会ユビキタスコンピューティングシステム(UBI)研究報告日常行動のZero-shot Deep Learningに向けた基底的動作の自動抽出の検討 2020.02.24

竹下昌志, 松木萌, 前川卓也, 井上創造

Country：Japan

行動認識技術はユビキタスコンピューティングを実現する基盤技術の一つである．一般的な行動認識技術では教師あり機械学習が用いられているが，特徴設計を手動で行わなければいけない点と教師データの収集が手間である点が，応用のボトルネックとなる．これらの問題に対し，深層学習を用いることで特徴量抽出の自動化をめざす取り組みや，データ収集効率化を目指し未知クラスを推定する取り組みが行われている．我々は，それらの問題を解決するために深層学習手法に基づいた未知クラス推定手法 (Zero-shot Deep Learning) を提案する．未知クラスの推定を実現するため，行動を構成する基底的な動作の自動抽出および，それらの生起を表す属性ベクトルと呼ばれる表現の自動獲得を目指す．本稿では属性ベクトル推定精度の向上を目指し，(検証 1 )深層学習過程での畳み込む時間長の調整，(検証2) 活性化関数の検証，の 2 つの検証を行った．その結果，検証 1 では推定精度が向上し，検証 2 では活性化関数に温度付き Softmax 関数を用いることが基底的行動の抽出に有効であることがわかった．

学生奨励賞（携帯センサと近接センサを用いた看護業務時間に影響を与える要因の分析）

情報処理学会ユビキタスコンピューティングシステム(UBI)研究報告日常行動のZero-shot Deep Learningに向けた基底的動作の自動抽出の検討 2020.02.24

竹下昌志, 松木萌, 前川拓也, 井上創造

Country：Japan

Activity recognition technology is required in ubiquitous computing. In recent years, activity recognition technology uses machine learning, but machine learning has two problems; extracting feature of sensor data is required to carry out by hand and the data collection task is quite laborous. To overcome these problems, we aim to propose the method combining deep learning model and Zero-shot learning model. However, we are worried that by combining these approach, the performance of unknown activity class recognition become decreases. In this paper, we study how to recognize attribute vector in the middle layer for unknown activity classes recognition. At the result, proposed model which recognize single-attribute in the middle layer achieve 60 % improvement
compare to the multi-label classification neural network model.

学生優秀講演賞

日本知能情報ファジィ学会九州支部学術講演会ゼロショット行動認識のための中間表現の探索 2019.11.30

竹下昌志, 松木萌, 井上創造

Country：Japan

Human activity recognition is a popular research area. For the last few decades, researchers are using different types of machine learning algorithms to detect activities from various types of data such as motion capture data, accelerometer data, gyroscope data etc. The researchers have used various machine-learning algorithms to detect simple as well as complex human activities. In this paper, we are focusing on “Emteq activity recognition challenge” where the task is to identify 8 types of human activities. The challenge dataset contains accelerometer, gyroscope and magnetometer data. For this purpose, we have extracted important features from the sensor data and used Random Forest classifier to detect activities. We have used one- person leave out cross validation for our proposed model. The F1 score for our proposed model on one-person leave out cross validation is around 47%. Between persons test that is one person’s training data and another person’s test data, the F1 score is around 30%.

2nd Place Award

Emteq Activity recognition challenge 2019.09.11

Kohei Adachi, Md. Shafiqul Islam, Sozo Inoue

最優秀プレゼンテーション賞

マルチメディア，分散，協調とモバイル(DICOMO2019)シンポジウムセンサ行動認識におけるZero-shot学習法のためのベクトル表現の性質の分析 2019.07.03

松木萌, 井上創造

優秀論文賞

マルチメディア，分散，協調とモバイル(DICOMO2019)シンポジウムセンサ行動認識におけるZero-shot学習法のためのベクトル表現の性質の分析 2019.07.03

松木萌, 井上創造

Best Paper Award Runner-up (Crowdsourcing System Management for Activity Data with Mobile Sensors)

2019.05.31

Nattaya Mairittha, Sozo Inoue

Best PhD Forum Presentation Award (Sensor-based Daily Activity Understanding in Caregiving Center)

2019.03.04

Tahera Hossain, Sozo Inoue

2018 Excellent Student Award of IEEE Fukuoka Section "A Study on Sensor-based Activity Recognition Having Missing Data"

2019.02.01

Tahera Hossain, Md Atiqur Rahman Ahad, Hiroki Goto, Sozo Inoue

（学生優秀ポスター発表賞）行動認識における単語ベクトル投影モデルのゼロショット学習法の試み

SOFT九州支部夏季ワークショップ2018in 霧島 2018.08.28

松木萌, 井上創造

活動功労賞

マルチメディア，分散，協調とモバイル(DICOMO2018)シンポジウム 2018.07.06

井上創造

（ヤングリサーチャ賞）スマートフォンを用いた行動認識におけるセンサデータ欠損時の精度向上手法

マルチメディア，分散，協調とモバイル(DICOMO2018)シンポジウム 2018.07.05

後藤広樹, Tahera Hossain, 井上創造

(Best paper award) "A Study on Sensor-based Activity Recognition Having Missing Data

2018.06.28

Tahera Hossain, Md Atiqur Rahman Ahad, Hiroki Goto, Sozo Inoue

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Honors and Awards - INOUE Sozo