研究者詳細 - 花沢　明俊

論文 - 花沢　明俊

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Flexible Natural Language-Based Image Data Downlink Prioritization for Nanosatellites 査読有り国際誌

Fielding E., Hanazawa A.

Aerospace 11 ( 11 ) 2024年11月

担当区分：最終著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Nanosatellites increasingly produce more data than can be downlinked within a reasonable time due to their limited bandwidth and power. Therefore, an on-board system is required to prioritize scientifically significant data for downlinking, as described by scientists. This paper determines whether natural language processing can be used to prioritize remote sensing images on CubeSats with more flexibility compared to existing methods. Two approaches implementing the same conceptual prioritization pipeline are compared. The first uses YOLOv8 and Llama2 to extract image features and compare them with text descriptions via cosine similarity. The second approach employs CLIP, fine-tuned on remote sensing data, to achieve the same. Both approaches are evaluated on real nanosatellite hardware, the VERTECS Camera Control Board. The CLIP approach, particularly the ResNet50-based model, shows the best performance in prioritizing and sequencing remote sensing images. This paper demonstrates that on-orbit prioritization using natural language descriptions is viable and allows for more flexibility than existing methods.

DOI： 10.3390/aerospace11110888

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85210416736&origin=inward

Flood susceptibility mapping leveraging open-source remote-sensing data and machine learning approaches in Nam Ngum River Basin (NNRB), Lao PDR 査読有り国際誌

Mangkhaseum S., Bhattarai Y., Duwal S., Hanazawa A.

Geomatics, Natural Hazards and Risk 15 ( 1 ) 2024年01月

担当区分：最終著者,　責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Frequent floods caused by monsoons and rainstorms have significantly affected the resilience of human and natural ecosystems in the Nam Ngum River Basin, Lao PDR. A cost-efficient framework integrating advanced remote sensing and machine learning techniques is proposed to address this issue by enhancing flood susceptibility understanding and informed decision-making. This study utilizes remote sensing geo-datasets and machine learning algorithms (Random Forest, Support Vector Machine, Artificial Neural Networks, and Long Short-Term Memory) to generate comprehensive flood susceptibility maps. The results highlight Random Forest’s superior performance, achieving the highest train and test Area Under the Curve of Receiver Operating Characteristic (AUROC) (1.00 and 0.993), accuracy (0.957), F1-score (0.962), and kappa value (0.914), with the lowest mean squared error (0.207) and Root Mean Squared Error (0.043). Vulnerability is particularly pronounced in low-elevation and low-slope southern downstream areas (Central part of Lao PDR). The results reveal that 36%–53% of the basin’s total area is highly susceptible to flooding, emphasizing the dire need for coordinated floodplain management strategies. This research uses freely accessible remote sensing data, addresses data scarcity in flood studies, and provides valuable insights for disaster risk management and sustainable planning in Lao PDR.

DOI： 10.1080/19475705.2024.2357650

Kyutacar

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85194529741&origin=inward

Clothing Recognition System Using MLLM and Its Utility 査読有り

Suzuki K., Hanazawa A.

Proceedings of SPIE - The International Society for Optical Engineering 13510 2025年01月

担当区分：最終著者,　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）

Clothing is one of the attributes of information that expresses a person's tastes and preferences.Therefore, automatically identifying and categorizing clothing can have significant marketing applications.In this paper, we developed a communication system based on clothing recognition as an example of multimodal communication with humans using MLLM (Multimodal Large Language Model).We examined the communication part such as recognizing the customer's attire and calling out to them, saying what they think of the outfit, and complimenting them.We planned the flow and content of the exchange.In addition, the system development and its effectiveness were verified.Experiments were conducted at stores, and results such as customers becoming interested in the system and the number of smiles increased after using the system were obtained.From these, the usefulness of the system was shown.

DOI： 10.1117/12.3057668

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85218336018&origin=inward

Identification of original markers by object recognition algorithms using machine learning 査読有り

So A., Hanazawa A.

Proceedings of SPIE - The International Society for Optical Engineering 13164 2024年01月

担当区分：最終著者,　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）

We have been working on object detection by combining markers we design and machine learning. Object detection using such markers is particularly powerful under poor conditions where it is difficult to recognize the detection target such as the influence of occlusion is significant. In our previous study, we proposed markers which are robust to occlusion. In that study, we used SSD to identify markers with an accuracy of approximately 0.85. In present study, we propose two types of markers. One is an improvement of the previously proposed markers. It outperforms previous markers’ accuracy. The other uses a reflective material and allows detection from images captured by infrared cameras. For this reflective marker, we use two-step detection using CNN to identify. It increases the situations in which the markers can be used.

DOI： 10.1117/12.3018214

Scopus

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Harnessing Multi-satellite Remote Sensing Data and Machine Learning for Flood Risk Assessment in Nam Ngum River Basin, Lao PDR 査読有り国際誌

Mangkhaseum S., Duwal S., Bhattarai Y., Hanazawa A.

Proceedings of SPIE - The International Society for Optical Engineering 13083 2024年01月

担当区分：最終著者,　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）

The study focuses on flood susceptibility in the Nam Ngum River Basin, Lao PDR, an area prone to annual flooding due to monsoons and rainstorms. Flooding in this region significantly threatens human life, causes economic losses, and damages communities and agriculture. The study employs advanced remote sensing and machine learning techniques, including random forest (RF), support vector machine (SVM), and artificial neural networks (ANN), to address these issues and create detailed flood susceptibility maps. The machine learning models used historical flood data, Sentinel-1 SAR imagery from 2018 to 2020, and open-source flood data for training and validation. Eleven flood factors were considered. With 776 samples, 70% were trained, and 30% tested the model. Flood susceptibility map accuracy is assessed using statistical techniques such as multicollinearity, Kappa index, and area under the curve of receiver operating characteristics (AUROC). The generated flood susceptibility map is used to analyze the possible effect on the different land use/land cover classes and populations. RF outperforms SVM and ANN, achieving higher accuracy based on Receiver Operating Characteristics. The resulting flood susceptibility map reveals that 25-44% of the basin area is highly susceptible, predominantly in low-elevation and low-slope regions. Likewise, 85 to 90% of the people are highly vulnerable to flooding within 260 to 280 km2 of built-up area. The study proposes a new approach to using machine learning and readily available remote sensing data for flood susceptibility mapping. The findings of this study provide essential insights for policymakers, aiding in disaster risk reduction and facilitating sustainable development planning in Lao PDR.

DOI： 10.1117/12.3022819

Kyutacar

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195448927&origin=inward

Attitude determination of small satellites using Earth images matching 査読有り国際誌

Honda M., Hanazawa A.

Proceedings of SPIE - The International Society for Optical Engineering 13164 2024年01月

担当区分：最終著者,　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）

In this study, the coordinates of the Earth's surface in Earth images taken by small satellites were identified using image-matching techniques. The aim is to determine the attitude of small satellites based on these coordinates. This was confirmed with images and attitude data created using Unity.

DOI： 10.1117/12.3018540

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85192851609&origin=inward

Flood Susceptibility Mapping Using Publicly Available Big Data with Google Earth Engine and Deep Learning Algorithms 査読有り国際誌

Mangkhaseum S., Bhattarai Y., Duwal S., Hanazawa A.

2024 5th International Conference on Big Data Analytics and Practices, IBDAP 2024 38 - 43 2024年01月

担当区分：最終著者,　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）

Flood susceptibility mapping is essential for disaster risk management in flood-prone regions. This study utilizes point-based data from publicly available open-source earth system datasets, historical flood observation datasets, Google's cloud computing platform, and deep learning models to create flood susceptibility maps for the Nam Ngum River Basin in Lao PDR. The assessment integrates the digital elevation model (DEM), satellite-observed rainfall data, land use/land cover, and remote sensing image-derived indices such as NDVI to capture detailed flood causation factors. Deep learning techniques, including Artificial neural networks (ANN), Long short-term memory (LSTM), and Deep Neural Networks (DNN), are employed to analyze various hydro-meteorological and geomorphological parameters. The models were trained and tested using eleven flood conditioning variables and 390 locations to ensure predictive accuracy and reliability. These datasets were randomly divided into training and testing datasets in a 70:30 ratio. The developed models are evaluated based on performance metrics like accuracy, precision, and the area under the curve of Receiver Operating Characteristics (AUROC). This map identifies critical zones within the Nam Ngum River Basin at high risk of flooding, offering valuable insights for local authorities and stakeholders. This information is crucial for enhancing flood risk management, emergency planning, and mitigation strategies.

DOI： 10.1109/IBDAP62940.2024.10689702

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85206582261&origin=inward

VERTECS: A COTS-based payload interface board to enable next generation astronomical imaging payloads 査読有り国際誌

Fielding E., Schulz V.H., Chatar K.A.A., Sano K., Hanazawa A.

Proceedings of SPIE - The International Society for Optical Engineering 13101 2024年01月

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

Due to advances in observation and imaging technologies, modern astronomical satellites generate large volumes of data. This necessitates efficient onboard data processing and high-speed data downlink. Reflecting this trend is the Visible Extragalactic background RadiaTion Exploration by CubeSat (VERTECS) 6U Astronomical Nanosatellite. Designed for the observation of Extragalactic Background Light (EBL), this mission is expected to generate a substantial amount of image data, particularly within the confines of CubeSat capabilities. This paper introduces the VERTECS Camera Control Board (CCB), an open-source payload interface board leveraging Commercial Off-The-Shelf (COTS) components, with a Raspberry Pi Compute Module 4 at its core. The VERTECS CCB hardware and software have been designed from the ground up to serve as the sole interface between the VERTECS bus system and astronomical imaging payload, while providing compute capability not usually seen in nanosatellites of this class. Responsible for mission data processing, it will facilitate high-speed data transfer from the imaging payload via gigabit Ethernet, while also providing a high-bitrate serial connection to the payload X-band transmitter for mission data downlink. Additional interfaces for secondary payloads are provided via USB-C and standard 15-pin camera connectors. The Raspberry Pi embedded within the VERTECS CCB operates on a standard Linux distribution, streamlining the software development process. Beyond addressing the current mission’s payload control and data handling requirements, the CCB sets the stage for future missions with heightened data demands. Furthermore, it supports the adoption of machine learning and other compute-intensive applications in orbit. This paper delves into the development of the VERTECS CCB, offering insights into the design and validation of this next-generation payload interface, to ensure that it can survive the rigors of space flight.

DOI： 10.1117/12.3019471

Kyutacar

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85201968106&origin=inward

Leveraging Deep Learning Algorithms and Open-Source Spatial Datasets for Analysis of Flood and its relationship with land cover change, population growth in the Nam Ngum River Basin (NNRB), Lao PDR 査読有り

Mangkhaseum S., Hanazawa A.

Proceedings of the International Astronautical Congress, IAC 438 - 450 2024年01月

担当区分：最終著者,　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）

Monitoring transformations in land use and land cover (LULC) has significant effects on various aspects of the environment, including water resources, climate change, and biodiversity loss on a global scale. Exponential human population growth and the expansion of human land use have led to numerous changes and problems in ecosystems, natural disasters, and increased pressure on natural resource management at all scales. Remote sensing and earth observation have been employed for this purpose for decades, providing the capability to detect changes over extensive geographical areas. This study combines point-based data from publicly available open-source earth system datasets, historical flood observation datasets, Google’s cloud computing platform, and advanced remote sensing and deep learning techniques to create detailed flood susceptibility maps. Key datasets include digital elevation model (DEM), satellite-observed rainfall data, land use/land cover, and remote sensing image-derived indices such as NDVI and Sentinel-1 SAR imagery. The assessment employs various deep learning models, including Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Deep Neural Networks (DNN), to analyze hydro-meteorological and geomorphological parameters. These models were trained and tested using eleven flood conditioning variables and samples divided into training and testing datasets in a 70:30 ratio. Performance metrics such as accuracy, precision, and the area under the curve of Receiver Operating Characteristics (AUROC) were used to evaluate the models. The resulting flood susceptibility maps identify critical zones within the Nam Ngum River Basin at high risk of flooding, revealing that 36-53% of the basin area is highly susceptible, especially in low-elevation and low-slope regions. Additionally, 85-93% of the population is highly vulnerable to flooding within 261 to 296 km2 of built-up area. The results of this study show the alarming situation of flood risk management. The study thus offers valuable insights for local authorities and stakeholders, enhancing flood risk management, emergency planning, and mitigation strategies. The findings provide essential information for policymakers, aiding disaster risk reduction and facilitating sustainable development planning in Lao PDR. This study has significant implications for the hydrological community. The method proposed was initially employed in the region, and the findings contributed to improvements in local and regional policies and the decision-making process concerning urban development, as well as planning and water resource management, environmental conservation, and preventing extreme events such as floods in the Nam Ngum watershed areas.

DOI： 10.52202/078376-0042

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105000109331&origin=inward

省計算リソースの畳み込みオートエンコーダによる画像圧縮手法

森川靖仁, 花沢明俊

人工知能学会全国大会論文集 ( 一般社団法人人工知能学会 ) JSAI2023 ( 0 ) 3T5GS702 - 3T5GS702 2023年01月

担当区分：最終著者,　責任著者記述言語：日本語掲載種別：研究論文（大学，研究機関等紀要）

近年CubeSatをはじめとする超小型人工衛星が様々な機関で開発されている。超小型人工衛星は通信容量および計算処理のリソースが限られているため、撮影した衛星画像の送受信には画像圧縮を省計算リソースで行う必要がある。本研究では、超小型人工衛星に搭載可能な省計算リソースの畳み込みオートエンコーダの設計について、ダウンサンプリングの手法を変えて復元精度の比較を行った。プーリング層を搭載しているモデル、プーリング層の代わりにストライド幅の広い畳み込み層を用いたモデル、ストライド幅の広い畳み込み層のみを用いたモデルの３つについて、圧縮前後の画像に対してSSIM、PSNRでの評価を行った。その結果、プーリング層の代わりにストライド幅の広い畳み込み層を用いることによって復元精度が向上し、加えて画像のエッジがより再現されることが確かめられた。

DOI： 10.11517/pjsai.jsai2023.0_3t5gs702

CiNii Research

その他リンク： https://kyutech.repo.nii.ac.jp/records/2001562

Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels 査読有り国際誌

Cespedes A.J.J., Pangestu B.H.B., Hanazawa A., Cho M.

Applied Sciences (Switzerland) 12 ( 17 ) 2022年09月

記述言語：英語掲載種別：研究論文（学術雑誌）

CubeSat requirements in terms of size, weight, and power restrict the possibility of having redundant systems. Consequently, telemetry data are the primary way to verify the status of the satellites in operation. The monitoring and interpretation of telemetry parameters relies on the operator’s experience. Therefore, telemetry data analysis is less reliable, considering the data’s complexity. This paper presents a Machine Learning (ML) approach to detecting anomalies in solar panel systems. The main challenge inherited from CubeSat is its capability to perform onboard inference of the ML model. Nowadays, several simple yet powerful ML algorithms for performing anomaly detection are available. This study investigates five ML algorithm candidates, considering classification score, execution time, model size, and power consumption in a constrained computational environment. The pre-processing stage introduces the windowed averaging technique besides standardization and principal component analysis. Furthermore, the paper features the background, bus system, and initial operational data of BIRDS-4, a constellation made of three 1U CubeSats released from the International Space Station in March 2021, with a ML model proposal for future satellite missions.

DOI： 10.3390/app12178634

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85137944404&origin=inward

Validation of Random Forest Algorithm to Monitor Land Cover Classification and Change Detection using Remote Sensing Data in Google Earth Engine 査読有り国際誌

Mangkhaseum S., Hanazawa A.

Proceedings of SPIE - The International Society for Optical Engineering 12177 2022年01月

担当区分：最終著者,　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）

Monitoring land cover classification and change detection based on remote sensing images using a machine learning algorithm has become one of the important factors. For our case study, we select Vientiane capital as the study area. Our proposed method aims to perform the land cover classification using random forest algorithm supervised classification in Google Earth Engine (GEE) and post classification comparison (PCC) of change detection using Arc GIS software between 1990 and 2020 with 5 years interval periods are evaluated. In this paper, we utilize GEE combining with multiple sources of satellite optical images time-series from three main satellites, Landsat 5, Landsat 8, and Sentinel 2 integrating with multiple spectral, spatial, temporal, and textural features. Spectral indices such as NDVI and NDBI are calculated to enhance the accurate performance. Our results show that all six classes are obtained highly accurate land cover classification, with overall accuracy over 97.73% for training data and 90.35% for testing data, and kappa statistic of 0.97 for training data and 0.87 for testing data in 2020.

DOI： 10.1117/12.2626085

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85131785502&origin=inward

Comparison of Machine Learning Classifiers for Land Cover Changes using Google Earth Engine 査読有り国際誌

Mangkhaseum S., Hanazawa A.

Proceedings of the 2021 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2021 2021年01月

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

Increasing urbanization causes a variety of environmental changes, not only in regional but in the global scale, especially in developing countries. The developing countries like Lao PDR have very few data on environment and urban management and face many difficulties due to poor data management. Nowadays, using the integration of geographic information system (GIS) and remote sensing provides us with an effective result in determining the land use and land cover changes as well as providing valuable information needed for planning and researching. A new approach has been introduced for detecting and monitoring urban expansion by using the Big Data platform via the Google Earth Engine (GEE) cloud computing. Also, the machine learning algorithms have been recommended in this research, such as Random Forest (RF), Classification and Regression Tree (CART), Support Vector Machine (SVM) and Minimum Distance (MD). In this paper, we conduct GEE combining with multiple sources of satellite optical images time-series from three main satellites, Landsat 5 and Landsat 8 and Sentinel 2. For our dataset collection, we manually classify our dataset based on the pixel-based approach and object-based approach which were divided into training sample (70%) and testing sample (30%). Accuracy was assessed through metrics derived from a confusion/error matrix of the classifier. From the results, RF and CARF outperformed other classifiers and exhibited overall accuracy both training data and testing sample whereas SVM performed mediocrely and Minimum distance displayed least performance.

DOI： 10.1109/ICARES53960.2021.9665186

Scopus

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Human detection using color contrast-based histograms of oriented gradients 査読有り国際誌

Matsumura R., Hanazawa A.

International Journal of Innovative Computing, Information and Control ( ICIC International 学会 ) 15 ( 4 ) 1211 - 1222 2019年08月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2019 ICIC International. In this paper, we propose a method for human detection using color contrast-based Histograms of Oriented Gradients (HOG). The proposed method calculates the color similarities between a pixel of interest and the pixels in its neighborhood. By applying the same gradient calculations as HOG on these color similarities, gradient orientation histogram can be made for color contrast. It can capture edge information derived from color contrast even when the luminance contrast is small. We evaluate the proposed method using the following three types of classifiers in the INRIA and NICTA datasets: Real Adaboost, Support Vector Machine (SVM), and random forest. As a result, the proposed method exhibits higher performance than HOG.

DOI： 10.24507/ijicic.15.04.1211

Scopus

CiNii Research

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脳卒中片麻痺手の機能改善に有効な手浴方法の提案査読有り

永井あけみ, 長弘千恵, 山本真弓, 花沢明俊, 夏目季代久

形態・機能 ( コ・メディカル形態機能学会 ) 17 ( 2 ) 86 - 93 2019年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

脳卒中片麻痺手の機能回復は難しく、従来は発症後３ヶ月間の自然回復期以後の回復はないとされ（以後この現象をプラトーと言う）、麻痺側への訓練は中止されていた。このような状況の中、WHOが脳卒中片麻痺に対する鍼の効果を承認し、「標準経穴部位」（ツボ）を発表した。看護分野では、手浴は清潔ケアの一つとして古くから行われてきた。本研究では脳卒中片麻痺手の機能改善に有効な手浴方法の提案を目的とする。肘から指先まで12ヶ所のツボ押し洗いと拘縮改善に効果があるとされるストレッチングを手浴に取り入れた。対象者は脳卒中プラトー以後の慢性期片麻痺手患者３名で、各被験者に本手浴法を６日間実施し、２名に手の随意運動の改善がみられ手浴後の血圧も有意に低下した。以上の結果より、本提案手浴法による麻痺手改善の効果が示唆された。本手浴法は費用もかからず他セラピーと容易に併用できる事が利点であるが、症例が少ないことから更なる検証が必要である。

DOI： 10.11172/keitaikinou.17.86

CiNii Article

CiNii Research

その他リンク： https://ci.nii.ac.jp/naid/130007661091

広域視野範囲におけるサッカー選手の視覚情報処理査読有り

今村律子, 乾眞寛, 徳島了, 花沢明俊, 坂元瑞貴, 山本勝昭, 磯貝浩久

バイオメディカル・ファジィ・システム学会誌 ( バイオメディカル・ファジィ・システム学会 ) 16 ( 1 ) 105 - 116 2014年01月

記述言語：日本語掲載種別：研究論文（学術雑誌）

本研究では,サッカー選手を対象とし,有効視野の範囲110°と生理的視野180°の広範囲で視覚刺激が呈示された場合に,視覚情報の獲得がどのように行われているのかを注視点距離から検討することを目的とした.対象者から3mの半円上にモニターを設置し,正面,有効視野110°位置,生理的視野180°の3つの視覚刺激条件において,指押し課題と全身反応課題の2つの反応形態の測定を実施した.その結果,呈示条件における注視点距離に差は見られなかった.しかし,反応時間と注視点距離の関係を検討した結果,反応形態において,全身反応課題では,注視点が長いと反応時間が速いという関連が見られた.また,指導者によるサッカー選手としてのパフォーマンス評価の順位は,全身反応課題においてすべての条件に注視点距離と有意な相関が見られたため,注視点距離が長い選手は「周りが良く見えている選手」として評価されている可能性があることが示唆された.

DOI： 10.24466/jbfsa.16.1_105

Kyutacar

CiNii Article

その他リンク： https://ci.nii.ac.jp/naid/110009818168

認知症高齢者の情動機能回復評価を目的とした笑顔画像計測

花沢明俊, 中村克樹

日本知能情報ファジィ学会ファジィシステムシンポジウム講演論文集 ( 日本知能情報ファジィ学会 ) 29 ( 0 ) 79 - 79 2013年01月

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

簡単な学習や軽度の運動などの非薬物療法による認知症の改善、進行抑制効果が確認されており、認知リハビリテーションと総称されている。認知リハビリでは、記憶や言語理解といった脳の高次機能だけでなく、排泄や笑顔などの表情表出など、生活機能全般および情動・コミュニケーション機能の改善も報告されている。認知機能回復については、知能テストと類似した方法によって測定され、療法の効果測定や課題の難易度調整に用いられているが、情動機能改善を定量評価する手法が存在しない。情動機能に対する働きかけが、認知リハビリの効果をより高める可能性があることから、情動機能に対する認知リハビリの効果測定のため、画像技術による認知症高齢者の笑顔計測を試みた。

DOI： 10.14864/fss.29.0_79

Kyutacar

CiNii Article

その他リンク： https://ci.nii.ac.jp/naid/130005480482

Occipital gamma-oscillations modulated during eye movement tasks: Simultaneous eye tracking and electrocorticography recording in epileptic patients 査読有り

Nagasawa T., Matsuzaki N., Juhász C., Hanazawa A., Shah A., Mittal S., Sood S., Asano E.

NeuroImage 58 ( 4 ) 1101 - 1109 2011年10月

記述言語：英語掲載種別：研究論文（学術雑誌）

We determined the spatio-temporal dynamics of cortical gamma-oscillations modulated during eye movement tasks, using simultaneous eye tracking and intracranial electrocorticography (ECoG) recording. Patients with focal epilepsy were instructed to follow a target moving intermittently and unpredictably from one place to another either in an instantaneous or smooth fashion during extraoperative ECoG recording. Target motion elicited augmentation of gamma-oscillations in the lateral, inferior and polar occipital regions in addition to portions of parietal and frontal regions; subsequent voluntary eye movements elicited gamma-augmentation in the medial occipital region. Such occipital gamma-augmentations could not be explained by contaminations of ocular or myogenic artifacts. The degree of gamma-augmentation was generally larger during saccade compared to pursuit trials, while a portion of the polar occipital region showed pursuit-preferential gamma-augmentations. In addition to the aforementioned eye movement task, patients were asked to read a single word popping up on the screen. Gamma-augmentation was elicited in widespread occipital regions following word presentation, while gamma-augmentation in the anterior portion of the medial occipital region was elicited by an involuntary saccade following word presentation rather than word presentation itself. Gamma-augmentation in the lateral, inferior and polar occipital regions can be explained by increased attention to a moving target, whereas gamma-augmentation in the anterior-medial occipital region may be elicited by images in the peripheral field realigned following saccades. In functional studies comparing brain activation between two tasks, eye movement patterns during tasks may need to be considered as confounding factors. © 2011 Elsevier Inc..

DOI： 10.1016/j.neuroimage.2011.07.043

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=80052636999&origin=inward

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