2025/09/13 更新

ガルシア クリスティーナ
Christina Alvarez Garcia
Christina Alvarez Garcia
Scopus 論文情報  
総論文数: 0  総Citation: 0  h-index: 3

Citation Countは当該年に発表した論文の被引用数

所属
研究本部 重点プロジェクトセンター ケアXDXセンター
職名
助教

出身大学院

  • 2024年03月   九州工業大学   生命体工学研究科   人間知能システム工学専攻   博士課程・博士後期課程   修了   日本国

取得学位

  • 九州工業大学  -  博士(工学)   2024年03月

学内職務経歴

  • 2024年11月 - 現在   九州工業大学   研究本部   重点プロジェクトセンター   ケアXDXセンター     助教

  • 2024年04月 - 2024年10月   九州工業大学   大学院生命体工学研究科     支援研究員

所属学会・委員会

論文

  • Two-Stage Reservoir Computing for Sensor-Specific Activity Recognition Using the WEAR Inertial Dataset 査読有り 国際誌

    Tu Truong Huynh, Umang Dobhal, Christina Garcia, Hirofumi Tanaka, Sozo Inoue

    In Companion of the 2025 on ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '25) ( Association for Computing Machinery, New York )   2025   2025年10月

     詳細を見る

    記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    Aalto University, Espoo, Finland   https://www.ubicomp.org/ubicomp-iswc-2025/   2025年10月14日  -  2025年10月16日

    In this paper, team VedaNam Coders proposes a Reservoir Computing approach to address robustness and generalization in the 2nd WEAR Dataset Challenge of UbiComp HASCA 2025. The main task involves recognising 18 activities from inertial data collected at four locations in the body. The challenge emphasises generalisation and robustness to randomly sampled, sensor-specific 1-second windows from unseen participants, with augmentations simulating diverse wearing conditions. Prior baseline approaches, such as DeepConvLSTM and TinyHAR, struggle with low-motion or overlapping classes with limitations on handling short windows, and noisy inputs. In response, we propose a two-stage hybrid pipeline combining CNNs for spatial feature learning and Reservoir Computing (RC) for short-term temporal dynamics. To evaluate robustness and generalization, we compared our RC-based approach with three gradient boosting models and evaluated four RC variations. Experimental results show that the merged-limb two-stage RC strategy achieves the highest test macro F1 score (0.52888), outperforming the three challenge baselines and tree-based models. This study demonstrates the potential of reservoir computing across different granularities and architectures for building robust HAR models.

    DOI: https://doi.org/10.1145/3714394.3756190

    DOI: https://doi.org/10.1145/3714394.3756190

  • Psychological Data Collection of Elderly Care Workers for Stress Detection 査読有り 国際誌

    aoya Miyake, Haru Kaneko, Elsen Ronando, Xinyi Min, Christina Garcia, Sozo Inoue

    International Journal of Activity and Behavior Computing ( Care XDX Center, Kyushu Institute of Technology )   2025 ( 3 )   1 - 14   2025年09月

     詳細を見る

    担当区分:責任著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    Kitakyushu, Japan   https://cennser.org/ICIEV/index.html   2025年05月26日  -  2025年05月29日

    n this paper, we aim to develop a stress detection model for caregivers using data collected from a real-world care facility. The importance of stress management has grown, and many physiological datasets and stress detection studies exist for nurses. Caregivers experience various types of stress, including interpersonal issues and assisting with toileting, and are physically active throughout the day, moving around the floor and performing tasks like transfer assistance. These diverse stressors and physical activity can significantly affect stress detection using wearable sensors. However, sufficient data collection in caregiving settings is not
    being conducted compared to other fields (e.g., nursing, doctor). In this study, we conduct a data collection experiment and evaluate the results of stress detection machine learning using that data. In the data collection, we create 8-day dataset from four caregivers that includes care record data, wearable sensor data, and self-reported stress labels. Next, we extracted statistical features from the time-series data and performed stress detection using a Random Forest model. As a result, we achieved a maximum classification accuracy of 72%. While data augmentation improved the detection of minority classes such as High Stress, it also lowered the overall classification performance, revealing a trade-off that remains a challenge. Nevertheless, this dataset makes it possible to analyze caregiver stress in relation to specific care activities, representing an important step toward stress-aware support in the caregiving domain.

    DOI: https://doi.org/10.60401/ijabc.120

    DOI: https://doi.org/10.60401/ijabc.120

  • Summary of the Silent Speech Decoding Challenge Using EEG Data for Arabic Inner Speech Recognition 招待有り 査読有り 国際誌

    Muhammad E. H. Chowdhury, Rusab Sarmun, Diala Bushnaq, Malek Chabbouh, Raghad Aljindi, Shona Pedersen, Md. Ahasan Atick Faisal, Nazmun Nahid, Iqbal Hassan, Ryuya Munemoto, Christina Garcia, Sozo Inoue

    International Journal of Activity and Behavior Computing ( Care XDX Center, Kyushu Institute of Technology )   2025 ( 2 )   1 - 21   2025年09月

     詳細を見る

    担当区分:責任著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    Abu Dhabi, United Arab Emirates   https://abc-research.github.io/2025   2025年04月21日  -  2025年04月25日

    The Silent Speech Decoding Challenge, hosted as part of the ABC 2025 conference, was designed to advance the decoding of silent (inner) speech from electroencephalography (EEG) signals, specifically targeting Arabic language processing. Participants leveraged advanced machine learning (ML) and deep learning (DL) techniques to classify EEG data from a uniquely collected dataset. This dataset, gathered by Qatar University's Machine Learning Group, consisted of EEG recordings from ten subjects across multiple sessions, capturing both silent and overt speech for six distinct inner speech commands. Participants were tasked with developing robust ML/DL models to accurately decode these neural signals, addressing challenges such as generalization across subjects and robustness to noise and motion artifacts.

    DOI: https://doi.org/10.60401/ijabc.104

    DOI: https://doi.org/10.60401/ijabc.104

    その他リンク: https://autocare.ai/abc2025

  • Summary of the BeyondSmile Challenge on Detecting Depression Through Facial Behavior and Head Gestures 招待有り 査読有り 国際誌

    Rahul Islam, Tongze Zhang, Anlan Dong, Melik Ozolcer, Christina Garcia, Milyun Ni’ma Shoumi, Prerna Jamloki, Sozo Inoue, Sang Won Bae

    International Journal of Activity and Behavior Computing ( Care XDX Center, Kyushu Institute of Technology )   2025 ( 2 )   1 - 15   2025年09月

     詳細を見る

    担当区分:責任著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    Abu Dhabi, United Arab Emirates   https://abc-research.github.io/2025   2025年04月21日  -  2025年04月25日

    Early detection of depressive episodes is crucial in managing mental health disorders such as Major Depressive Disorder (MDD) and Bipolar Disorder. However, existing methods often necessitate active participation or are confined to clinical settings. While facial expressions have shown promise in laboratory settings for identifying depression, their potential in real-world applications remains largely unexplored. In this challenge we introduce a data collected state of the art facial behavior sensing system, that tracks different facial behavior primitives such as Action Units, Landmarks, Head Pose, Eye Open state and others for task of detecting depressive episode in the wild. The challenge duration was three months, from Dec 12, 2024 to Feb 28, 2025 and 8 teams participated. Among the final teams, Team Persistence (Team ID 160) achieved 0.77 AUROC for universal model and 0.88 AUROC for hybrid model and became the winner.

    DOI: https://doi.org/10.60401/ijabc.102

    DOI: https://doi.org/10.60401/ijabc.102

    その他リンク: https://autocare.ai/abc2025

  • Understanding Normal and Unusual Activities Related to Parkinson’s Disease from Sensor Data: Summary of Activity and Behavior Computing Tremor Challenge 招待有り 査読有り 国際誌

    Shahera Hossain, Tahera Hossain, Christina Garcia, Tahia Tazin, Md. Mamun Ibrahim, Taihei Fujioka, Sozo Inoue, Md Atiqur Rahman Ahad

    International Journal of Activity and Behavior Computing ( Care XDX Center, Kyushu Institute of Technology )   2025 ( 2 )   1 - 18   2025年09月

     詳細を見る

    担当区分:責任著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    Abu Dhabi, United Arab Emirates   https://abc-research.github.io/2025   2025年04月21日  -  2025年04月25日

    Recognizing normal and unusual activities (e.g., tremor) from sensor data, particularly those related to the wear-off phenomenon of Parkinson’s disease (PD), is crucial to improving the quality of life of elderly individuals. This paper presents five selected methods from the Activity and Behavior Computing Tremor Challenge 2025, which focused on developing robust models to recognize Parkinson’s disease-related activities using wearable triaxial accelerometer data. These approaches range from traditional machine learning pipelines based on handcrafted multi-domain features to advanced deep learning architectures incorporating temporal modeling and attention mechanisms.
    The review also highlights innovative strategies such as adaptive window segmentation and data augmentation using conditional variational autoencoders. Each method addressed key challenges, including data label misalignment, class imbalance, noisy sensor readings, and the limited size of the dataset. The explored models ranged from traditional XGBoost classifiers to deep learning architectures such as CNN-LSTM hybrids, attention-enhanced DeepConvLSTM and Transformer-based classifiers, each demonstrating varying levels of
    success. This summary provides insights into the trade-offs between model interpretability and predictive performance, emphasizes the effectiveness of combining feature engineering with deep learning, and outlines directions for future research in scalable PD monitoring systems. The findings highlight the need for more advanced approaches to reliably decipher these challenging activities.

    DOI: https://doi.org/10.60401/ijabc.103

    DOI: https://doi.org/10.60401/ijabc.103

    その他リンク: https://autocare.ai/abc2025

  • 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

    Lingfeng Zhao1 Christina Garcia, Shunsuke Komizunai, Noriyo Colley, Atsuko Sato, Mayumi Kouchiyama, Toshiko Nasu, Sozo Inoue

    マルチメディア,分散,協調とモバイル(DICOMO2025)シンポジウム ( DICOMO )   2025   524 - 533   2025年06月

     詳細を見る

    記述言語:日本語   掲載種別:研究論文(研究会,シンポジウム資料等)

    Hahabata, Japan   https://tsys.jp/dicomo/2025/program/program.html   2025年06月25日  -  2025年06月27日

    本研究の目的は,経管栄養(Gastrostomy Tube Feeding,GTF)の看護行動認識の精度を向上させることである.
    GTFにおける行動は,短時間の動作が長時間の動作に埋もれやすいことや,厳密な時系列制約が考慮されないといった課題がある.
    その結果、既存の手法では適切な認識が困難であり,より効果的な認識手法が求められている.

    本研究では,ビデオに基づくポーズ推定と大規模言語モデル(Large Language Model,LLM)を活用した新たな特徴量選択および後処理手法を提案する.
    具体的には,LLMを用いて時系列的な文脈を考慮した特徴量を自動生成し,これを手作業で設計した特徴量と統合することで,認識精度の向上を図る.
    さらに,多数決に基づく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導入し,誤検出の低減を試みる.

    実験では,中国労災病院で収集した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。
    今後は,より大規模なデータセットでの検証を進めるとともに、LLMを活用した動作認識のさらなる最適化や,リアルタイムでの動作識別の実現を目指す.

    その他リンク: https://tsys.jp/dicomo/2025/program/program_abst.html#3B-4

  • 発達障害者の異常行動認識における時間パラメータの最適化

    Taihei Fujioka, Christina Garcia, Haru Kaneko, Sozo Inoue

    マルチメディア,分散,協調とモバイル(DICOMO2025)シンポジウム ( DICOMO )   2025   284 - 294   2025年06月

     詳細を見る

    担当区分:責任著者   記述言語:日本語   掲載種別:研究論文(研究会,シンポジウム資料等)

    Hahabata, Japan   https://tsys.jp/dicomo/2025/program/program.html   2025年06月25日  -  2025年06月27日

    本研究では、発達障害者の異常行動を模擬したポーズ推定データに対し、行動の文脈情報を大規模 言語モデル(LLM)に組み込むことで、時間パラメータ(ウィンドウサイズ、オーバーラップ率、シーケ ンス長)を最適化する手法を提案する。発達障害者支援施設においては、職員の人員不足や微細な動作の 見落としが課題であり、異常行動の検出は困難を伴う。既存手法では、これらの行動が不規則かつ予測困 難であるため、誤検出や見逃しが頻発していた。本研究の主な貢献は、異常行動を含む独自のラベル付き データセットの構築と、Zero-shot および Few-shot プロンプトによる LLM の活用方法を示したことであ る。収集した行動データの文脈情報を活かして LLM にプロンプトを与えることで、各行動の特性に応じた 最適なウィンドウサイズ、オーバーラップ率、LSTM の系列長を自動的に提案させた。データセットは、5 人の健常者が実演した 8 種の行動(うち異常行動 4 種)を記録したものであり、対象に障害のある方は含 まれていない。評価実験では、全行動および異常行動ごとの分類性能について、LLM を用いないベースラ イン手法と比較した。その結果、Few-shot プロンプトを用いることで、「物を投げる」で 7.69 %、「攻撃す
    る」で 7.31 %、「頭を振る」で 4.68 %、「爪を噛む」で 1.24 %の F1 スコアの改善が見られた。Zero-shot プロンプトにおいても、すべての異常行動で F1 スコア 96 %以上を達成した。

    その他リンク: https://tsys.jp/dicomo/2025/program/program_abst.html#2B-3

  • Summary of the Virtual Data Generation for Complex Industrial Activity Recognition 招待有り 査読有り 国際誌

    Qingxin Xia, Christina Garcia, Umang Dobhal, Min Xinyi, Tanigaki Kei, Nagoya Yoshimura, Sozo Inoue, Takuya Maekawa, Kaishun Wu

    International Journal of Activity and Behavior Computing ( Care XDX Center, Kyushu Institute of Technology )   2025 ( 2 )   1 - 12   2025年05月

     詳細を見る

    担当区分:責任著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    Abu Dhabi, United Arab Emirates   https://abc-research.github.io/2025   2025年04月21日  -  2025年04月25日

    This paper presents a summary of the virtual data generation for complex industrial activity recognition Challenge, which focused on exploring virtual data generation techniques to improve the performance of human activity recognition (HAR) in complex industrial environments. The challenge utilized the OpenPack dataset, a large-scale multimodal collection of sensor data captured during real-world packaging operations. Participants were tasked with generating synthetic accelerometer data to augment a baseline HAR model.

    DOI: https://doi.org/10.60401/ijabc.101

    DOI: https://doi.org/10.60401/ijabc.101

    その他リンク: https://autocare.ai/abc2025

  • Toward Abnormal Activity Recognition of Developmentally Disabled Individuals Using Pose Estimation 査読有り 国際誌

    Taihei Fujioka, Christina Garcia, Sozo Inoue

    International Journal of Activity and Behavior Computing ( Care XDX Center, Kyushu Institute of Technology )   2025 ( 1 )   1 - 28   2025年05月

     詳細を見る

    担当区分:責任著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    Abu Dhabi, United Arab Emirates   https://abc-research.github.io/2025   2025年04月21日  -  2025年04月25日

    抄録
    In this study, we propose to optimize temporal parameters with pose estimation data of simulated abnormal activities of developmentally disabled individuals by incorporating behavior context to Large Language Models (LLMs). Facilities for the developmentally disabled face the challenge of detecting abnormal behaviors because of limited staff and the difficulty of spotting subtle movements. Traditional methods often struggle to identify these behaviors because abnormal actions are irregular and unpredictable, leading to frequent misses or misclassifications. The main contributions of this work is the creation of a unique dataset with labeled abnormal behaviors and the proposed application of LLMs to this dataset comparing results of Zero-Shot and Few-Shot. Our method leverages the context of the collected abnormal activity data to prompt LLMs to suggest window size, overlap rate, and LSTM model’s length sequence tailored to the specific characteristics of these activities.

    DOI: https://doi.org/10.60401/ijabc.39

    DOI: https://doi.org/10.60401/ijabc.39

    その他リンク: https://autocare.ai/abc2025

  • Nurse Activity Recognition in Gastrostomy Tube Feeding Using Video-Based Pose with Large Language Model-Guided Features 査読有り 国際誌

    Lingfeng Zhao, Christina Garcia, Shunsuke Komizunai, Noriyo Colley, Atsuko Sato, Mayumi Kouchiyama, Toshiko Nasu, Sozo Inoue

    International Journal of Activity and Behavior Computing ( Care XDX Center, Kyushu Institute of Technology )   2025 ( 1 )   1 - 28   2025年05月

     詳細を見る

    担当区分:責任著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    Abu Dhabi, United Arab Emirates   https://abc-research.github.io/2025   2025年04月21日  -  2025年04月25日

    抄録
    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. 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.

    DOI: https://doi.org/10.60401/ijabc.38

    DOI: https://doi.org/10.60401/ijabc.38

    その他リンク: https://autocare.ai/abc2025

  • Temporal and spatial analysis of activity patterns: A case study of neighborhood park in the Orio–Hibikino area, Kitakyushu, Japan 査読有り 国際誌

    Dini Hardilla, Christina A. Garcia, Bart J. Dewanker

    Frontiers of Architectural Research ( Higher Education Press )   2025   1 - 18   2025年04月

     詳細を見る

    記述言語:英語   掲載種別:研究論文(学術雑誌)

    This research explores how the park's design features and temporal conditions, such as weather and season, affect user activity in parks near neighborhood communities. The hypothesis is that user behavior patterns are influenced by complex interactions between spatial and non-spatial characteristics, including the park's physical layout, park features, demographic composition, weather conditions, and temporal variations. This study has two main goals: to analyze the spatial distribution of activities and temporal variations of activities occurring in walking paths, open areas, playground amenities, and benches across four parks located in the neighborhood communities of Kitakyushu using real-world datasets collected over summer and autumn in 2024. Activity patterns have been evaluated by user spatial patterns with similar activity and location and relabel activity zone and park feature performances. Results reveal that certain locations emerge as activity nodes or “hotspots” for particular activities, depending on the season and time of day, emphasizing the dynamic interaction between park design and user engagement. This study provides insights supporting the adaptability and responsive approach to park design and planning, considering both spatial temporal dynamics in understanding and optimizing park usage patterns.

    DOI: https://doi.org/10.1016/j.foar.2025.04.005

    DOI: https://doi.org/10.1016/j.foar.2025.04.005

    Kyutacar

    その他リンク: https://www.sciencedirect.com/science/article/pii/S2095263525000603

  • Toward Detecting and Explaining Stress of Nurses Using Wearable Devices and LLMs 査読有り 国際誌

    Naoya Miyake, Haru Kaneko, Elsen Ronando, Christina Garcia, Sozo Inoue

    Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024) ( Springer Cham )   1212   288 - 299   2024年12月

     詳細を見る

    担当区分:責任著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    Northern Ireland, United Kingdom   https://ucami.org/ucami2024/   2024年11月27日  -  2024年11月29日

    In this paper, we improve nurse stress detection using wearable device data by balancing the dataset through Large Language Models (LLMs). The rise in wearable technologies has spurred stress detection studies in both daily life and healthcare settings. However, integrating an LLM on stress detection with wearable devices is still emerging. Data imbalance and faulty sensor readings, especially from EDA sensors in devices like the E4, can significantly impact the accuracy of stress detection models. To address this, we utilize LLMs to mitigate data imbalance by generating relevant synthetic samples through varied contextual prompting strategies. We compared Zero-shot, Few-shot, Zero-shot CoT, and Few-shot CoT prompting ability on data generation. We benchmark the LLM approach against SMOTE and Random Sampling comparing overall performance on stress detection. Furthermore, we employ the RACCCA (Relevance, Accuracy, Completeness, Clarity, Coherence, Appropriateness) framework to assess the quality of prompting and output.

    DOI: https://doi.org/10.1007/978-3-031-77571-0_28

    DOI: https://doi.org/10.1007/978-3-031-77571-0_28

    その他リンク: https://link.springer.com/book/10.1007/978-3-031-77571-0

  • Synthetic Skeleton Data Generation using Large Language Model for Nurse Activity Recognition 査読有り 国際誌

    Umang Dobhal, Christina Garcia, Sozo Inoue

    In Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '24) ( Association for Computing Machinery, New York )   2024   493 - 499   2024年10月

     詳細を見る

    担当区分:責任著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)

    Melbourne, Australia   https://www.ubicomp.org/ubicomp-iswc-2024/   2024年10月05日  -  2024年10月09日

    In this paper, we improve nurse activity recognition by employing a Large Language Model (LLM) to generate synthetic pose estimation data. Keypoint data extracted from recorded videos of a single nurse performing Endotracheal suctioning (ES) activities using You Only Look Once v7 (YOLOv7) is used as a database. We explore the issue of data imbalances that hinder the effectiveness of activity recognition algorithms. To counter this, we utilize LLMs to artificially augment the dataset by generating varied synthetic samples through prompting strategies with different content and context. Additionally, we generate synthetic datasets in equal volumes using Random Sampling and Generative Adversarial Networks (GAN) to benchmark against our LLM-based approach. To evaluate, we compared the performance between baseline data and different augmentation approaches. The similarity between original and synthetic data is measured using the Kolmogorov-Smirnov (K-S) test.

    DOI: https://doi.org/10.1145/3675094.3678445

    DOI: https://doi.org/10.1145/3675094.3678445

    その他リンク: https://dl.acm.org/doi/abs/10.1145/3675094.3678445

▼全件表示

口頭発表・ポスター発表等

  • Indoor Localization in Nursing Care Facility Using Large Language Models 招待有り

    Christina Garcia

    7th International Conference on Activity and Behavior Computing  2025年04月  7th International Conference on Activity and Behavior Computing

     詳細を見る

    開催期間: 2025年04月21日 - 2025年04月25日   記述言語:英語   開催地:Abu Dhabi, United Arab Emirates   国名:アラブ首長国連邦  

    In this study, we use Large Language Models (LLMs) for room recognition in nursing facilities, leveraging contextual data to address data limitations. Traditional models struggle with Received Signal Strength Indicator (RSSI) variability and sparse data. LLMs offer a promising alternative by integrating domain knowledge to address signal inconsistencies and improve localization accuracy.

    その他リンク: https://abc-research.github.io/2025

学術関係受賞

  • Best Short Paper Awardee

    12th International Conference on Informatics, Electronics & Vision (ICIEV)   LLM-based Indoor Localization in Nursing Care Facility: A Zero-Shot Ablation Study   2025年05月29日

    Christina Garcia, Sozo Inoue

     詳細を見る

    受賞国:日本国

    Awarded Best Short Paper for the work "LLM-based Indoor Localization in Nursing Care Facility: A Zero-Shot Ablation Study" presented during the 12th International Conference on Informatics, Electronics & Vision (ICIEV) in Japan last May 26-29, 2025.

  • Outstanding Contribution Awardee

    7th International Conference on Activity and Behavior Computing   Outstanding Contribution Awardee   2025年04月25日

    Christina Garcia

     詳細を見る

    受賞国:アラブ首長国連邦

    Awarded with Outstanding Contribution for actively serving as Challenge Organization Chair co-hosting four coding challenges with various universities while also serving as the Publication Chair.

  • Excellent Paper Awardee

    5th International Conference on Activity and Behavior Computing   A Relabeling Approach to Signal Patterns for Beacon-based Indoor Localization in Nursing Care Facility   2023年09月09日

    Christina Garcia, Sozo Inoue

     詳細を見る

    受賞国:ドイツ連邦共和国

    Awarded Excellent Paper for the work "A Relabeling Approach to Signal Patterns for Beacon-based Indoor Localization in Nursing Care Facility" presented in the 5th International Conference on Activity and Behavior Computing held in Kaiserslautern, Germany last September 7th - 9th, 2023.

  • Best Paper Awardee

    22nd World Conference on Applied Science, Engineering and Technology   Non-invasive Diabetes Detection using Facial Texture Features Captured in a Less Restrictive Environment   2019年09月27日

    Christina Garcia

     詳細を見る

    受賞国:シンガポール共和国

    Awarded Best Paper for the work titled "Non-invasive Diabetes Detection using Facial Texture Features Captured in a Less Restrictive Environment" presented during the 22nd World Conference on Applied Science, Engineering and Technology held on September 2019 in Singapore.

  • Best Department Project Thesis Awardee

    Mindanao State University - Iligan Institute of Technology, College of Engineering   Fast Lock-in Time Phase Locked Loop Frequency Synthesizer for Continuous-Time Sigma-Delta ADC   2013年04月08日

    Christina Garcia, Stella Sofia Sabate

     詳細を見る

    受賞国:フィリピン共和国

    Bachelor thesis implemented using Synopsis Industry tool grade for design. Frequency Detector with NOR gates and divide-by-64 with pseudo-NMOS divide-by-2 frequency divider is proposed, designed and simulated in TSMC 0.18 um 1P6M CMOS process technology to come up with minimum chip area and achieve fast lock-in time. This PLL design is specifically intended for Continuous-Time Sigma-Delta ADC operating at 640MHz frequency which is an important component of ICs used in electronics and communication devices whose clock rates and timing relationships are vital. This work has a lock-time of around 2.5 us which is a fast lock-in value for the lock-in time of ADC clock generator. T

学会・委員会等活動

  • IEEE Philippines, Region 10 Chapter   Mentor  

    2025年09月

     詳細を見る

    フィリピンにおけるスマートモビリティ向けAIソリューションの提案・開発を行う10チームを指導。技術開発、ピッチ、ターゲット市場、提案書についてフィードバックと助言を提供。これはワークショップ「IEEE Philippines Smart Mobility Hackathon 2025」の一環です。

  • 2025年 九州工業大学 若手技術者育成アカデミー助成金   Grant Recipient, Implementor  

    2025年09月 - 現在

     詳細を見る

    助成金を申請し、「学術的ライティングと研究コミュニケーション能力の強化」プログラムを提案しました。本提案では、学生の研究ライティング、プレゼンテーション、英語コミュニケーション能力を強化するための一連の的を絞った充実した活動を導入します。目標は、不明確な構成、アイデア形成への自信不足、学会での効果的なコミュニケーション不足といった主要な課題に対処することです。

社会貢献活動(講演会・出前講義等)

  • Human-Centered Design in Smart Mobility Systems

    役割:講師

    IEEE Philippines – Region 10 Chapter ACEI 2025  IEEE Smart Mobility Hackathon 2025  MSUIIT, Iligan City, Philippines (Joined Online)  2025年09月02日 - 2025年09月04日

     詳細を見る

    対象: 大学生, 大学院生, 教育関係者, 研究者, 学術団体, 行政機関

    種別:セミナー・ワークショップ

    屋内移動システム向け人間中心のAIソリューション設計と、プロジェクトの成功的な実施に向けたステークホルダーとの協働方法について講演を行いました。

  • Bridging Boundaries in Research and Collaboration

    役割:講師

    Institute of Electronics Engineers of the Philippines – Manila Student Chapter (IECEP-MSC)  Webinar Series, “The ECEsentials: A Journey through Innovation and Excellence!”  Metro Manila, Philippines (Online)  2025年02月25日

     詳細を見る

    対象: 大学生

    種別:セミナー・ワークショップ

    海外留学生としての研究活動の方法と学際的な共同研究の進め方について講演を行った。

  • Applications of AI in Healthcare

    役割:講師

    Computer Science Department. MSU-IIT, Philippines  CSC 194 - Computer Science Seminar   Iligan City, Philippines (Online)  2025年02月05日

     詳細を見る

    対象: 大学生

    種別:講演会

    人工知能を医療に応用する多分野連携研究の実施方法について講演を行い、画像・動画・センサーデータを用いた3つの研究事例を紹介した。

  • From Curiosity to Innovation: Bridging Boundaries in Research and Collaboration

    役割:講師

    Ho Chi Minh University of Technology, VNU-HCM  Student Forum of the International Symposium on Applied Science 2024  Ho Chi Minh City University of Technology (HCMUT), Vietnam  2024年10月18日

     詳細を見る

    対象: 大学生

    種別:セミナー・ワークショップ

    海外留学生としての研究活動の方法と学際的な共同研究の進め方について講演を行った。

  • Engineering Your Future: Getting into Grad School

    役割:講師

    IEEE Philippines – Manila Chapter  Grad School Goals: Unlocking Opportunities Webinar  Metro Manila, Philippines (Online)  2024年05月25日

     詳細を見る

    対象: 大学生, 大学院生, 教育関係者, 研究者

    種別:セミナー・ワークショップ

    大学院留学のための海外研究奨学金の申請方法と、留学中に成功し予定通り卒業するための方法について講演を行いました。準備すべきこと、予想されること、外国人としての生活に適応する方法について。

国際会議開催(学会主催除く)

  • 8th International Conference on Activity and Behavior Computing

    Care XDX Center  https://abc-research.github.io  2026年03月09日 - 2026年03月12日

  • 37th Australian Conference on Human-Computer Interaction (HCI)

    OzCHI, The University of Sydney  https://www.ozchi.org/2025/  2025年11月29日 - 2025年12月03日

  • 24th Mexican International Conference on Artificial Intelligence (MICAI)

    Centro de Investigación en Matemáticas (CIMAT), Universidad de Guanajuato (UG)  https://micai.org/2025/  2025年11月03日 - 2025年11月07日

  • International Symposium on Applied Science 2025

    Ho Chi Minh City University of Technology  https://fas.hcmut.edu.vn/isas, https://isaschallenge2025.my.canva.site/  2025年10月17日 - 2025年10月19日

  • IEEE International Conference on Artificial Intelligence Engineering and Technology (IICAIET 2025)

    IEEE Sabah Section  https://iicaiet.ieeesabah.org/  2025年08月26日 - 2025年08月28日

  • 12th International Conference on Informatics, Electronics & Vision (ICIEV)

    University of East London, Kyushu Institute of Technology  https://cennser.org/ICIEV/index.html  2025年05月26日 - 2025年05月29日

  • 7th International Conference on Activity and Behavior Computing

    Care XDX Center  https://abc-research.github.io/2025  2025年04月21日 - 2025年04月25日

  • International Symposium on Applied Science 2024

    Ho Chi Minh City University of Technology  https://fas.hcmut.edu.vn/isas2024  2024年10月18日 - 2024年10月20日

▼全件表示