HIGA Hiroki

写真a

Title

Professor

Researcher Number(JSPS Kakenhi)

60295300
2 3
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Current Affiliation Organization 【 display / non-display 】

  • Concurrently   University of the Ryukyus   Graduate School of Engineering and Science   Interdisciplinary Intelligent Systems Engineering   Professor  

  • Duty   University of the Ryukyus   Faculty of Engineering   School of Engineering_Electrical and Systems Engineering Program   Professor  

  • Concurrently   University of the Ryukyus   Graduate School of Engineering and Science   Electrical Energy and Systems Control Engineering   Professor  

  • Concurrently   University of the Ryukyus   Graduate School of Engineering and Science   Professor  

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Academic degree 【 display / non-display 】

  • Tohoku University -  Doctor of Engineering

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External Career 【 display / non-display 】

  • 2006.04
    　
    　

    University of the Ryukyus, Faculty of Engineering, Associate Professor  

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Research Interests 【 display / non-display 】

  • Biomedical Engineering

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Research Areas 【 display / non-display 】

  • Life Science / Medical systems

  • Life Science / Rehabilitation science

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Research Theme 【 display / non-display 】

  • Assistive Robot for People with Physical Disabilities of the Extremities

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Published Papers 【 display / non-display 】

  • Self-Feeding Assistive Robotic Arm for People with Severe Disabilities -Evaluation of Drinking Water Tasks Using Plastic Bottle-

    Shotaro Gushi, Yuto Shimabukuro, Takashi Ishida, Hiroki Higa

    IEEJ Transactions on Electronics, Information and Systems ( IEEJ )  145 ( 11 ) 961 - 972   2025.11 [ Peer Review Accepted ]

    Type of publication: Research paper (scientific journal)

    　View Summary

    To assist people with severe disabilities to eat and drink at their own pace, we present a self-feeding robotic arm system with the functions of object detections using YOLOv5 model and user's mouth detection using MediaPipe. The redundant robotic arm with 7-DOF (degree-of-freedom) was made to perform natural eating motion and avoid obstacles by considering the home care and nursing care environments in Japan. An open-source software, ROS (Robot Operating System), and a motion planning framework, MoveIt! simulator including the motion planning solver using inverse kinematics, were used to simulate the 7-DOF robotic arm. Moreover, a single-finger operated interface was applied as a controller for the robotic arm system. We demonstrated that using the proposed robotic arm system, the tasks to grasp and move the plastic bottle to the user's mouth were conducted. From the simulation and experimental results, we found that the detections of the target object and the user's mouth were conducted effectively. In addition, it was shown that the success rates of the tasks were more than 80% or equal when having no object in the calculated trajectory of the robotic arm. Future works include conducting some experiments of the tasks with people with disabilities.

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  • Lung-AttNet: An Attention Mechanism-Based CNN Architecture for Lung Cancer Detection With Federated Learning

    CHAMAK SAHA, Somak SAHA, MD.ASADUR RAHMAN, MD. MAHMUDUL HAQUE MILU, HIROKI HIGA, MOHD ABDUR RASHID AND NASIM AHMED

    IEEE Access ( IEEE )  13   57369 - 57386   2025.04 [ Peer Review Accepted ]

    Type of publication: Research paper (scientific journal)

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    Lung cancer is one of the fatal diseases whose early diagnosis is essential to mitigate the death rate. Computed Tomography (CT) scans are widely used for lung cancer diagnosis, but manual interpretation by health professionals can lead to inconsistent results. To address this, we propose Lung-AttNet, a novel lightweight convolutional neural network (CNN) model enhanced with an attention mechanism. Lung AttNet incorporates a convolutional block with a Lightweight Global Attention Module (LGAM) to effectively distinguish between lung cancer types. The convolutional block extracts both low- and high dimensional features, while LGAM captures feature dependencies across channel and spatial dimensions. The model is evaluated using the Kaggle CT scan dataset, which includes four classes: adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal. Extensive experiments, including ablation studies, 5-fold cross-validation, and explainable AI (XAI) techniques such as Grad-CAM and LIME, demonstrate that Lung-AttNet achieves an average accuracy of 91.5%. Furthermore, to address medical data sensitivity and privacy concerns, the model is deployed in a Federated Learning (FL) framework, where the global model is trained using weights from local models rather than sharing raw data. In the FL environment, Lung-AttNet achieves an accuracy of 92% with 2 and 3 clients, underscoring its robustness and adaptability for real-world applications.

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  • Development of a CNN-based decision support system for lung disease diagnosis using chest radiographs

    B. T. Magar, M. A. Rahman, P. K. Saha, M. Ahmad, M. A. Rashid, H. Higa

    AIP Advances   15 ( 3 )   2025.03 [ Peer Review Accepted ]

    Type of publication: Research paper (scientific journal)

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    Chest radiographs, or chest X-rays (CXRs), are widely used as first-line diagnostic tools for detecting various chest diseases. However, accurately interpreting CXRs remains challenging, as human diagnostic performance is influenced by individual expertise and other factors, often resulting in delays, high costs, and potential misinterpretations. To address these limitations, automated computer-based detection systems offer the potential to enhance diagnostic accuracy, reduce costs, and enable timely disease identification. This study presents CXRNet, a novel, efficient convolutional neural network (CNN)-based framework designed for multi-class classification of common chest diseases, including cardiomegaly, COVID-19, pneumonia, tuberculosis, and normal. The proposed CXRNet is a 16-layer architecture trained on frontal CXR images collected from diverse sources to ensure robust generalization across datasets. The model incorporates advanced strategies to overcome the limitations of previous approaches. Extensive testing under three different data distribution conditions demonstrated the model’s superior performance, achieving an average accuracy of 95.7%, precision of 95.3%, recall of 95.3%, and an F1-score of 95.3% for multi-class classification. Furthermore, for binary classification tasks, CXRNet achieved over 98% average accuracy across all conditions, outperforming existing methods. These results highlight the potential of CXRNet as a reliable decision support system for efficient and accurate chest disease diagnosis, paving the way for real-time clinical applications.

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      DOI Open Access

  • Development of proning pose classification system for selfcare of COVID-19 patients

    Shahadath Hossen, Md Osman Ali, Mohd Abdur Rashid, Hiroki Higa

    Progress in Engineering Science ( Elsevier )    100064   2025.03 [ Peer Review Accepted ]

    Type of publication: Research paper (scientific journal)

    　View Summary

    The COVID-19 situation is one of the most critical pandemics in human history. In COVID-19, severe breathing problems are caused in most cases due to lung damage that can be minimized by proning, a special type of exercise to increase the oxygen saturation level. The main purpose of this research is then to develop a proning pose classification system for self-care of COVID-19 patients. Since improper proning postures may lead to in juries and fatigue then the proning under the supervision of an expert is highly recommended which is very challenging to accommodate during isolation. To overcome this situation, K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Deep Neural Network (DNN) based systems have been developed to recognize the human pose. Additionally, cosine similarity is also estimated to assess the preciseness of the desired pose. MediaPipe library is used to generate 33 key points of the body parts which were used to recognize the human body structure and orientation from real-time poses captured in a web camera or mobile camera. A patient can perform his proning exercise using our developed system with the help of an Android mobile or computer. The SVM-based system with a linear kernel has achieved the best performance, with accuracy (train: 100 %; test: 99.5 %; validation: 99.2 %), precision (99.5 %), recall (99.6 %), F1-score (99.5 %), and AUC (99.7 %). It has also demonstrated the lowest latency of 112 ms, compared to 124 ms for CNN and 122 ms for DNN, respectively. Consequently, this model is recommended as the optimal solution for COVID-19 patients, enabling accurate and safe proning exercises to enhance recovery.

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  • Robotic Arm System for Assisting Persons with Physical Disabilities to Drink Water

    Shotaro Gushi, Yuto Shimabukuro, Hiroki Higa

    proc. of ICIIBMS2024     2024.11 [ Peer Review Accepted ]

    Type of publication: Research paper (international conference proceedings)

    　View Summary

    This paper presents a robotic arm system for the purpose of assisting persons with physical disabilities to drink water. We have developed a seven-degree-of-freedom robotic arm with a thermoplastic polyurethane hand. We also implemented functions of an object detection with YOLOv5 algorithm and a user’s mouth detection based on MediaPipe machine learning platform in the proposed system. Firstly, tasks of grasping and moving each detected object (a paper cup or a plastic bottle) to the mouth of a user were conducted to evaluate the system. Secondly, tasks of grasping the plastic bottle laid down on an experimental table and letting it stand on the table were carried out. From the experimental results, it was found that the object detection and the detection of the user’s mouth were successfully performed, and that the success rates of these tasks were 80 % and more.

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Other Papers 【 display / non-display 】

  • Development of a Wireless EMG-Based Interface: Operating a Robot Arm Simulator

    Haruta Maeuejo, Shotaro Gushi, Hiroki Higa

      OKI-2025-08   2025.12

     

  • Study on Height Position of Hand Model and Position of a Control Panel: in Comparison Between AR and Real Worlds

    Haro Arakaki, Shotaro Gushi, Hiroki Higa, Gen Ouchi

      OKI-2025-12   2025.12

     

  • Fundamental Study of BCI Using in Combination of Visual and Tactile Stimulations

    Yuki Ozaki, Seisei Kina, Hiroki Higa

      OKI-2025-11   2025.12

     

  • A Study on Multiclass Classification of Motor Imagery Using Movement-Related Cortical Potentials

    Satoru Imamura, Seisei Kina, Hiroki Higa

      OKI-2025-10   2025.12

     

  • Improving Operability of the Facial Parts Interface

    Hiroya Kishaba, Seisei Kina, Shotaro Gushi, Hiroki Higa

      OKI-2025-09   2025.12

     

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Presentations 【 display / non-display 】

  • A Data Conversion for Medical Training System

    Mohamed Atef Mahmoud Mostafa Khalil, Ryosuke Umeda, Yuji Uehara, and Hiroki Higa

    電気学会九州支部沖縄支所講演会論文集  2016.12  -  2016.12 

  • Improvement of Implantable Functional Electrical Stimulation (FES) System

    Mayumi Nishiyama and Hiroki Higa

    電気学会九州支部沖縄支所講演会論文集  2012.12  -  2012.12 

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Grant-in-Aid for Scientific Research 【 display / non-display 】

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SDGs 【 display / non-display 】

  • 生体医工学

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Social Activity 【 display / non-display 】

  • 2025.12
    　
    　

  • IEICE 

    2025.11
    　
    　

  • 2020.12
    　
    　

  • 2020.03
    　
    　

  • 2020.02
    　
    　

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Media Coverage 【 display / non-display 】

  • はえばる福祉まつり出展

    南風原町  南風原町文化センター  2018.11

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    南風原町文化センターにて開催されたはえばる福祉まつりに研究室で開発した「サンシン演奏支援装置」を出展し、来場者に紹介、実演を行った。

  • プチ・オープン・ラボ

    琉球大学工学部  2013.8

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    地域の小学生を研究室に招待し、研究室で行っている研究活動を紹介し、実演を行った。また参加者に実験等の体験を行ってもらった。

  • はえばる福祉まつり出展

    南風原町  南風原町文化センター  2012.11

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    南風原町文化センターで開催されたはえばる福祉まつりに、研究室で開発した福祉機器を出展し、来場者に実演・説明を行った。

  • 沖縄産学官イノベーションフォーラムでのパネル展示

    沖縄県工業技術センター  2009.11

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    沖縄産学官イノベーションフォーラムにおいて研究紹介パネル展示を行った。

  • 親子サイエンスツアー

    オリオンビール  2007.10

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    オリオンビール名護工場で開催された親子サイエンスツアーに出展し、研究成果物の紹介とデモンストレーションを行った。

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Academic Activities 【 display / non-display 】

  • （ 県立武道館アリーナ棟 ）

    2013.10
    　
    　

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    沖縄の産業まつりにおいて、研究成果物の出展とデモンストレーションを行った。

  • （ 国立病院機構沖縄病院 ）

    2010.11
    　
    　

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    国立病院機構沖縄病院において開催された文化祭において研究紹介を行い、研究成果物のデモンストレーションを行った。

  • （ 県立武道館アリーナ棟 ）

    2007.10
    　
    　

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    那覇市で開催された沖縄の産業まつりに出展し、研究成果物の実演・説明を行った。

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