Sezaki & Nishiyama Laboratory
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Institute of Industrial Science / Center for Spatial Information Science in The University of Tokyo
Yuuki Nishiyama, Subaru Atsumi, Kota Tsubouchi, Kaoru Sezaki
A-UVI: GNSS-Assisted EO-based UV Index Estimation Method for Individual-level Precise UV Exposure Assessment Journal Article Open Access
In: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 9 (2), 2025.
Abstract | BibTeX | タグ: | Links:
@article{10.1145/3729463,
title = {A-UVI: GNSS-Assisted EO-based UV Index Estimation Method for Individual-level Precise UV Exposure Assessment},
author = {Yuuki Nishiyama and Subaru Atsumi and Kota Tsubouchi and Kaoru Sezaki},
url = {https://dl.acm.org/doi/10.1145/3729463},
doi = {10.1145/3729463},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
volume = {9},
number = {2},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Excessive or insufficient exposure to ultraviolet (UV) light can have adverse effects on health, including the development of skin cancer, cataracts, and osteoporosis. An Earth observation (EO)-based UV index can estimate area-level UV indexes without effort in open-sky environments but can not provide sufficient accuracy for shaded environments. In contrast, conventional methods for monitoring individual-level, i.e., personal, UV exposure, such as mobile and wearable UV sensors, face limitations in terms of measurement and usability, presenting challenges for practical long-term usage. To address these issues, we introduce A-UVI, a method that enhances the accuracy of the EO-based UV index by leveraging raw signals from global navigation satellite systems (GNSS). By integrating this EO-based UV index and an attenuation ratio estimated from raw GNSS signals, our method especially improves estimation accuracy in shady environments affected by obstructions. We evaluated our method on data collected by different GNSS receivers in different mobility scenarios encompassing a diverse range of contexts and observation areas over the course of three days. Our evaluation showed that A-UVI estimates the UV index with a precision exceeding existing methods by at least 44.25%, achieving 5.53 times higher estimation accuracy in forest environments. We also confirmed that A-UVI is compatible with GNSS receivers in consumer-grade smartphones and has an average accuracy that is 23% better than the baseline EO-based method. Our findings demonstrate that utilizing raw GNSS signals enables accurate estimation of the UV index in various conditions, including in shaded areas, without the need for particular measurement actions or devices. This marks a significant advancement in enabling passive individual-level UV exposure monitoring and adaptive UV exposure management beyond simple exposure tracking.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Zengyi Han, En Wang, Mohan Yu, Jie Wang, Yuuki Nishiyama, Kaoru Sezaki
HeadMon+: Domain Adaptive Head Dynamic-based Riding Maneuver Prediction Journal Article Open Access
In: IEEE Transactions on Mobile Computing, pp. 14, 2025.
Abstract | BibTeX | タグ: | Links:
@article{10.1109/TMC.2025.3562179,
title = {HeadMon+: Domain Adaptive Head Dynamic-based Riding Maneuver Prediction},
author = {Zengyi Han and En Wang and Mohan Yu and Jie Wang and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://ieeexplore.ieee.org/document/10969560},
doi = {10.1109/TMC.2025.3562179},
year = {2025},
date = {2025-04-17},
urldate = {2025-04-17},
journal = {IEEE Transactions on Mobile Computing},
pages = {14},
abstract = {Micro-mobility has become a vital means of transportation in recent years, however, it has also resulted in a rise in traffic incidents. Timely tracking and predicting riders' maneuvers hold the potential to ensure active protection and allow for sufficient time to avert accidents by issuing timely warnings and interventions. We contend that the rider's head dynamics can provide valuable information regarding their subsequent maneuvers. Riders' traveling habits, however diverse, not to mention the rapidly varying riding environment. The above factors contribute to significant disruptions in the data source, and various micro-mobility forms further exacerbate the issue. We accordingly present HeadMon+, which predicts the rider's subsequent maneuver by examining their head dynamics, and it can effectively adapt to various riding conditions and individuals. The system incorporates a deep learning framework with an advanced domain adversarial network. By single-time pre-training, HeadMon+ is capable of adapting to new data domains, including human subjects, and riding conditions for robust maneuver prediction. Based on our evaluation, we have found that the maneuver prediction of HeadMon+ has an overall precision of 94% with a prediction time gap of 4 seconds. HeadMon+'s low cost and rapid response capability make it easily deployed and then contribute to enhancing safe riding.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
細沼恵里, 山崎託, 三好匠, 田谷昭仁, 西山勇毅, 瀬崎薫
生成型マルチモーダルセマンティック通信における意味的類似度に基づいた生成コスト分析 Conference
電子情報通信学会 ネットワークシステム研究会（NS）, 電子情報通信学会, 沖縄, 2025.
Abstract | BibTeX | タグ: | Links:
@conference{nokeyf,
title = {生成型マルチモーダルセマンティック通信における意味的類似度に基づいた生成コスト分析},
author = {細沼恵里 and 山崎託 and 三好匠 and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
url = {https://ken.ieice.org/ken/paper/20250307Qcio/},
year = {2025},
date = {2025-03-07},
urldate = {2025-03-07},
booktitle = {電子情報通信学会 ネットワークシステム研究会（NS）},
publisher = {電子情報通信学会},
address = {沖縄},
abstract = {通信リソースが制限された環境下において，少ない伝送コストで画像伝送を行う手法として，著者らはマルチモーダルな生成型セマンティック通信を提案してきた．本手法では，機械学習を用いて元画像から複数種類の意味情報を抽出することで送信データ量を削減する．その後，受信端末が受信した意味情報を画像生成モデルへ入力することで画像伝送を実現するが，このとき，画像生成モデルではランダムな画像が生成されるため，受信端末は生成画像の中から元画像と意味情報の類似度が高い画像を選択する必要がある．しかし，本手法では，受信端末による出力結果の選択方法や結果の選択に係るコスト分析について未検討であった．そこで本稿では，受信端末が画像間の意味的な類似度に基づき出力結果を選択する手法を検討し，結果の選択に係るコストの分析を行う．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
伊藤愛香, 小嶋優未, 坪内考太, 西尾信彦, 下坂正倫, 田谷昭仁, 瀬崎薫, 西山勇毅
常時装着型イヤラブルデバイス利用環境における音声通知タイミング最適化に向けた基礎検討 Conference
UBIシンポジウム2025, 大阪, 日本, 2025.
BibTeX | タグ:
@conference{nokeyh,
title = {常時装着型イヤラブルデバイス利用環境における音声通知タイミング最適化に向けた基礎検討},
author = {伊藤愛香 and 小嶋優未 and 坪内考太 and 西尾信彦 and 下坂正倫 and 田谷昭仁 and 瀬崎薫 and 西山勇毅},
year = {2025},
date = {2025-02-26},
urldate = {2025-02-26},
booktitle = {UBIシンポジウム2025},
address = {大阪, 日本},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Xuefu Dong, Yifei Chen, Yuuki Nishiyama, Kaoru Sezaki, Yuntao Wang, Ken Christofferson, Alex Mariakakis
Recognizing Hidden-in-the-Ear Silent Spellings Conference
UBIシンポジウム2025, 大阪, 日本, 2025.
BibTeX | タグ:
@conference{Dong2025,
title = {Recognizing Hidden-in-the-Ear Silent Spellings},
author = {Xuefu Dong and Yifei Chen and Yuuki Nishiyama and Kaoru Sezaki and Yuntao Wang and Ken Christofferson and Alex Mariakakis},
year = {2025},
date = {2025-02-26},
urldate = {2025-02-26},
booktitle = {UBIシンポジウム2025},
address = {大阪, 日本},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
中村仁哉, 田谷昭仁, 瀬崎薫, 西山勇毅
日常行動センサデータを用いた運動改善のための目標設定支援 Conference
情報処理学会 第211回HCI研究会, 情報処理学会, 沖縄, 2025.
Abstract | BibTeX | タグ: | Links:
@conference{nokey,
title = {日常行動センサデータを用いた運動改善のための目標設定支援},
author = {中村仁哉 and 田谷昭仁 and 瀬崎薫 and 西山勇毅},
url = {https://www.sighci.jp/events/sig/211},
year = {2025},
date = {2025-01-15},
booktitle = {情報処理学会 第211回HCI研究会},
publisher = {情報処理学会},
address = {沖縄},
abstract = {近年，デスクワークの増加等の理由から，運動不足が社会的な課題になっている.運動不足改善のための目標設定では，達成可能な難易度の短期目標を設定することにより，自己効力感を向上させ，目標 達成率の向上に貢献することが明らかになっているが，個人で効果的な短期目標を設定することは容易では無い.また，近年のモバイル・ウェアラブルデバイスの急速な浸透により，個人の行動を容易に計測で きるようになったが，運動不足改善のための目標設定において，行動データを用いた目標設定支援技術はまだ十分に確立していない.本研究の目的は，対象者の属性に類似した他人の行動データを短期目標として設定することで，短期目標に対する自己効力感が向上するかを明らかにすることである.本論文では， 131人の一般人から事前に収集した歩数データをもとに，対象者の属性と時刻毎の歩数パターンが類似し，かつ歩数が多い者を抽出し，その者の歩数を短期目標として提示する機構を設計・開発した.10 名の実験 参加者に対して，前述の機構を用いて抽出した短期目標を提示したところ，実験参加者は提案に対し高い 自己効力感を示した.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Peng, Helinyi, Taya, Akihito, Nishiyama, Yuuki, Sezaki, Kaoru
AEDHunter: Investigating and Enhancing AED Retrieval through a Mobile Application Proceeding Open Access
2025.
Abstract | BibTeX | タグ: | Links:
@proceedings{10.1145/3706599.3719746,
title = {AEDHunter: Investigating and Enhancing AED Retrieval through a Mobile Application},
author = {Peng, Helinyi and Taya, Akihito and Nishiyama, Yuuki and Sezaki, Kaoru},
url = {https://dl.acm.org/doi/10.1145/3706599.3719746},
doi = {10.1145/3706599.3719746},
year = {2025},
date = {2025-04-25},
urldate = {2025-04-25},
abstract = {Early defibrillation improves survival rates in out-of-hospital cardiac arrest (OHCA), yet public use of automated external defibrillators (AEDs) remains low due to unawareness of their locations. Existing solutions like 2D maps are often insufficient during emergencies, especially indoors where navigation is challenging. We developed AEDHunter, a gamified, location-based mobile application that enhances public awareness of AED locations by transforming the retrieval process into an engaging adventure. AEDHunter leverages smartphone sensors to analyze user retrieval patterns and navigation behaviors in real-world environments. Through experiments involving repeated interactions with AEDHunter, we investigate how users’ confidence, willingness, and behaviors in locating AEDs evolve. We also introduce a semi-supervised classification method to detect mobile device usage during these tasks, laying the groundwork for analyzing nuanced AED retrieval behaviors. This work-in-progress provides initial insights into enhancing AED awareness and retrieval through gamified applications.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
Ayaka Onodera, Riku Ishioka, Yuuki Nishiyama, Kaoru Sezaki
Multi-label Classification Model for Infant Activity Recognition Using Single Inertial Sensor Journal Article
In: IEEE Pervasive Computing, pp. 1-10, 2024.
Abstract | BibTeX | タグ: | Links:
@article{ieee_pc2024,
title = {Multi-label Classification Model for Infant Activity Recognition Using Single Inertial Sensor},
author = {Ayaka Onodera and Riku Ishioka and Yuuki Nishiyama and Kaoru Sezaki},
doi = {10.1109/MPRV.2024.3462483},
year = {2024},
date = {2024-11-01},
urldate = {2024-11-01},
journal = {IEEE Pervasive Computing},
pages = {1-10},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
abstract = {Recording and sharing childcare information is crucial for accurately assessing a child's health status and taking appropriate action in case of illness or other emergencies. Although numerous applications and systems have been proposed to assist in recording and sharing these records, the process is still performed manually, presenting a significant burden for parents. Therefore, automatic recording of infants' daily activities is required. In this study, we implement a machine learning model to recognize multi-labeled infant activities using a chest-mounted low-sampling rate accelerometer. We collected accelerometer data from twenty-four infants between 6 and 24 months as a dataset. Based on the data, we extracted 25 time- and frequency-domain features calculated from the single accelerometer and user features to recognize the fourteen daily activities. The performance evaluation considering multi-label classification showed that our proposed model reaches over 88% in the F1 score in the best case.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Eri Hosonuma, Taku Yamazaki, Takumi Miyoshi, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Image Generative Semantic Communication with Multi-Modal Similarity Estimation for Resource-Limited Networks Journal Article
In: IEICE Transactions on Communications, 2024.
@article{ieice2024_hosonuma,
title = {Image Generative Semantic Communication with Multi-Modal Similarity Estimation for Resource-Limited Networks},
author = {Eri Hosonuma and Taku Yamazaki and Takumi Miyoshi and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
year = {2024},
date = {2024-08-28},
journal = {IEICE Transactions on Communications},
abstract = {To reduce network traffic and support environments with limited re-
sources, a method for transmitting images with minimal transmission data
is required. Several machine learning-based image compression methods,
which compress the data size of images while maintaining their features,
have been proposed. However, in certain situations, reconstructing only
the semantic information of images at the receiver end may be sufficient.
To realize this concept, semantic-information-based communication, called
semantic communication, has been proposed, along with an image transmis-
sion method using semantic communication. This method transmits only
the semantic information of an image, and the receiver reconstructs it using
an image-generation model. This method utilizes a single type of semantic
information for image reconstruction, but reconstructing images similar to
the original image using only this information is challenging. This study
proposes a multi-modal image transmission method that leverages various
types of semantic information for efficient semantic communication. The
proposed method extracts multi-modal semantic information from an orig-
inal image and transmits only that to a receiver. Subsequently, the receiver
generates multiple images using an image-generation model and selects an
output image based on semantic similarity. The receiver must select the
result based only on the received features; however, evaluating semantic
similarity using conventional metrics is challenging. Therefore, this study
explores new metrics to evaluate the similarity between semantic features
of images and proposes two scoring procedures for evaluating semantic
similarity between images based on multiple semantic features. The results
indicate that the proposed procedures can compare semantic similarities,
such as position and composition, between the semantic features of the
original and generated images.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Takahiro Yabe, Kota Tsubouchi, Toru Shimizu, Yoshihide Sekimoto, Kaoru Sezaki, Esteban Moro, Alex Pentland
YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories Journal Article Open Access
In: Scientific Data, 11 (1), pp. 397, 2024, ISBN: 2052-4463.
Abstract | BibTeX | タグ: | Links:
@article{YJMob100K_sezalo,
title = {YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories},
author = {Takahiro Yabe and Kota Tsubouchi and Toru Shimizu and Yoshihide Sekimoto and Kaoru Sezaki and Esteban Moro and Alex Pentland},
url = {https://doi.org/10.1038/s41597-024-03237-9},
doi = {10.1038/s41597-024-03237-9},
isbn = {2052-4463},
year = {2024},
date = {2024-04-18},
urldate = {2024-04-18},
journal = {Scientific Data},
volume = {11},
number = {1},
pages = {397},
abstract = {Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications including transportation modeling, disaster management, and urban planning. The recent availability of large-scale human movement data collected from mobile devices has enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested on different datasets, due to the lack of open-source large-scale human mobility datasets amid privacy concerns, posing a challenge towards conducting transparent performance comparisons between methods. To this end, we created an open-source, anonymized, metropolitan scale, and longitudinal (75 days) dataset of 100,000 individuals'human mobility trajectories, using mobile phone location data provided by Yahoo Japan Corporation (currently renamed to LY Corporation), named YJMob100K. The location pings are spatially and temporally discretized, and the metropolitan area is undisclosed to protect users'privacy. The 90-day period is composed of 75 days of business-as-usual and 15 days during an emergency, to test human mobility predictability during both normal and anomalous situations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
田谷昭仁, 西山勇毅, 瀬崎薫
関数空間における交互方向乗数法を用いた知識蒸留による分散型連合学習 Conference Award
電子情報通信学会RISING2024研究会, 電子情報通信学会, 札幌, 2024.
Abstract | BibTeX | タグ: | Links:
@conference{RISING_Taya,
title = {関数空間における交互方向乗数法を用いた知識蒸留による分散型連合学習},
author = {田谷昭仁 and 西山勇毅 and 瀬崎薫},
url = {https://www.ieice.org/~rising/jpn/2024/index.html},
year = {2024},
date = {2024-11-11},
urldate = {2024-11-11},
booktitle = {電子情報通信学会RISING2024研究会},
publisher = {電子情報通信学会},
address = {札幌},
abstract = {高品質な機械学習モデルを構築するために大規模なデータセットを用いる学習が求められている．しかし，プライバシーの問題やデータの所有権の問題から，データを集約することが難しい場合がある．この問題を解決するために，データの所有者が個々に学習を行い，学習済みモデルのパラメータを共有することで，データを集約することなく学習を行う連合学習が提案されている．著者らは[1]において，IoT端末やセンサネットワーク向けの連合学習手法として，サーバを用いない分散型のアルゴリズムでかつ，知識蒸留を用いることで通信量削減と異種モデル混在環境での学習を可能にする学習手法を提案した．この手法は関数空間における分散合意最適化を行うことで，サーバを用いない学習アルゴリズムを実現した．本稿では，分散合意最適化よりも高速に収束する交互方向乗数法を用いる手法を提案する．提案手法は関数空間上での交互方向乗数法によるモデル更新を目指して，知識蒸留によるパラメータ更新を行う．また，提案手法と既存手法による収束速度の比較をシミュレーションにより行う．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
細沼恵里, 山崎 託, 三好 匠, 田谷昭仁, 西山勇毅, 瀬崎 薫
生成型画像伝送手法におけるエッジ抽出を用いた構図情報の再現性向上 Conference
2024年電子情報通信学会ソサイエティ大会, 電子情報通信学会, 埼玉, 2024.
Abstract | BibTeX | タグ: | Links:
@conference{IEICE_Hosonuma,
title = {生成型画像伝送手法におけるエッジ抽出を用いた構図情報の再現性向上},
author = {細沼恵里 and 山崎 託 and 三好 匠 and 田谷昭仁 and 西山勇毅 and 瀬崎 薫},
url = {https://www.ieice.org/jpn_r/activities/taikai/society/2024/},
year = {2024},
date = {2024-09-10},
booktitle = {2024年電子情報通信学会ソサイエティ大会},
pages = {48},
publisher = {電子情報通信学会},
address = {埼玉},
abstract = {通信リソースが制限された環境下において画像を伝送するための手法として，著者らは元画像から抽出した意味情報に基づく生成型画像伝送手法を提案している．本手法では，送信端末は元画像から説明情報，構図情報，色情報などの意味情報のみを送信することで狭帯域通信を実現する．その後，これらの情報を受信した端末は画像生成モデルを用いて元画像を同一の意味情報をもった画像を復元する．本手法では，画像に含まれる意味情報を抽出するため，画像キャプショニング及びセマンティックセグメンテーションを用いてキャプションや領域分割画像を作成する．しかし，領域分割画像内の背景とオブジェクト部分の色が類似しているとき，受信端末側で構図情報を適切に認識できず，正しい構図が再現されない問題があった．そこで本稿では，領域分割画像に代えてエッジ画像を利用することで，正確に構図情報を再現できる生成型画像伝送手法を提案する．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
伊藤愛香, 坪内孝太, 西尾信彦, 下坂正倫, 田谷昭仁, 瀬崎薫, 西山勇毅
常時装着型イヤラブルデバイス利用環境における音声通知タイミング最適化に向けた基礎検討 Conference
研究報告ユビキタスコンピューティングシステム（UBI）, 2024-UBI-82 , 2024.
@conference{ubi82_ito,
title = {常時装着型イヤラブルデバイス利用環境における音声通知タイミング最適化に向けた基礎検討},
author = {伊藤愛香 and 坪内孝太 and 西尾信彦 and 下坂正倫 and 田谷昭仁 and 瀬崎薫 and 西山勇毅},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
booktitle = {研究報告ユビキタスコンピューティングシステム（UBI）},
volume = {2024-UBI-82},
pages = {1 - 7},
abstract = {近年, イヤホンやヘッドホンのような耳に常時装着するイヤラブルデバイスが広く普及し, その発展に伴い音声エージェント, 音声ナビゲーションや音声検索など, イヤラブルデバイスを介した音声による情報提供機会が増加している. イヤラブルデバイスの利用シーンとして屋外の歩行中が挙げられるが, 歩行中に通知を受け取ることは周囲への注意力の低下に影響するため, ユーザが受信可能なタイミングで通知を行う必要がある. 先行研究では画面通知の最適化について幅広く研究されているが, 音声通知に着目した研究は相対的に不足している. そこで, 本研究は歩行中という利用状況に焦点を当て, スマートフォンおよびイヤラブルデバイスのセンサデータを活用した音声通知のタイミング最適化について検討する. 歩行中の音声通知受信可否タイミングについての基礎調査の結果, 他の交通との衝突を回避する状況での音声通知は受信拒否されることが分かった. 本稿では, データ取得のために開発したアプリケーションと基礎調査の分析結果, およびモーションセンサデータを用いた音声通知受信可否タイミング検出アルゴリズムの可能性について報告する.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
顧修聞, 田谷昭仁, 西山勇毅, 瀬崎薫
パッシブモバイルセンシングを用いた育児ノイローゼの検知に向けた基礎的調査 Conference
研究報告ユビキタスコンピューティングシステム（UBI）, 2024-UBI-82 , 2024.
@conference{ubi82_gu,
title = {パッシブモバイルセンシングを用いた育児ノイローゼの検知に向けた基礎的調査},
author = {顧修聞 and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
booktitle = {研究報告ユビキタスコンピューティングシステム（UBI）},
volume = {2024-UBI-82},
pages = {1 - 8},
abstract = {育児ノイローゼは、育児の過程で発生する慢性的なストレスによって引き起こされる心理的な状態である． 育児ノイローゼの悪化は産後うつをはじめとする，うつ病に進行する可能性があり，早期発見が重要である． パッシブモバイルセンシングを用いたうつ症状の検知手法は数多く提案されているが，未就学児を育てる父母を対象とした研究は限られており，その行動パターンや心理状態に関する客観的な情報が不足している． そこで本研究では，パッシブモバイルセンシングを用いた育児ノイローゼの検知システムの構築に向けて，未就学児を育てる父母を含む，135名から行動データおよび心理状態を収集し，断面分析を行なった． 特に歩数，位置情報，通話頻度，心理状態の分析を行い，未就学児を育てる家庭の行動パターンの特徴を明らかにした．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
顧修聞, 西山勇毅, 瀬崎薫, 田谷昭仁
パッシブモバイルセンシングを用いた産後うつ症状の検知に関する一検討 Conference
情報処理学会 第86回全国大会（神奈川大学）, 情報処理学会, 横浜, 2024.
Abstract | BibTeX | タグ: | Links:
@conference{IPSJ86_GU,
title = {パッシブモバイルセンシングを用いた産後うつ症状の検知に関する一検討},
author = {顧修聞 and 西山勇毅 and 瀬崎薫 and 田谷昭仁},
url = {https://onsite.gakkai-web.net/ipsj/abstract/data/pdf/6ZB-05.html},
year = {2024},
date = {2024-03-17},
urldate = {2024-03-17},
booktitle = {情報処理学会 第86回全国大会（神奈川大学）},
publisher = {情報処理学会},
address = {横浜},
abstract = {産後うつ病（Postpartum Depression: PPD）は産後の女性の約15%が発症するうつ病の一つである。治療には早期発見が重要であるが、自身でその病状を認識することは難しい。また、モバイルセンシングを用いたうつ病検知が行なわれているが、既存研究では産後女性特有の状況には焦点を当たっていない。本研究では、Passive Mobile Sensing（PMS）を活用し、PPDの自動検知システムを開発することを目的とする。実験参加者とその家族の活動データを収集し、PPDとの関連を分析する。機械学習を用いてPPDの自動検知可能性を探る。この研究は、情報通信技術に基づく効率的な育児支援の発展に貢献することが期待される。},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
王振博, 西山勇毅, 加藤貴昭, 瀬崎薫, 田谷昭仁
学生アスリートの行動データを用いた心身のストレス・回復状態の検知に向けて:異なる時期の比較分析 Conference
情報処理学会 第86回全国大会（神奈川大学）, 情報処理学会, 横浜, 2024.
Abstract | BibTeX | タグ: | Links:
@conference{IPSJ86_Wang,
title = {学生アスリートの行動データを用いた心身のストレス・回復状態の検知に向けて:異なる時期の比較分析},
author = {王振博 and 西山勇毅 and 加藤貴昭 and 瀬崎薫 and 田谷昭仁},
url = {https://onsite.gakkai-web.net/ipsj/abstract/data/pdf/6ZB-04.html},
year = {2024},
date = {2024-03-17},
booktitle = {情報処理学会 第86回全国大会（神奈川大学）},
publisher = {情報処理学会},
address = {横浜},
abstract = {現在、学生アスリートの心身の健康管理は非常に重要な課題である。競技と学業のバランスを保つためには、より効果的な管理アプローチの開発が求められている。既存研究においては、身体活動と心身の健康状態の調査が、主観的および客観的な情報に依存しており、利用者に高い負荷がかかっている。そのため、エビデンスに基づく低負荷な心身健康管理手法の開発が必要とされている。そこで本研究では、パッシブモバイルセンシングを活用して、学業中・競技中における学生アスリートの行動データを収集し、行動データと心身のストレス・回復状態との関連性を分析する。特に、異なる時期での行動データと心理尺度との関係を詳細に比較検討する。},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Helinyi Peng, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Initial Investigation on Improving AED Delivery Efficiency by Encouraging Location Awareness Conference
2024年電子情報通信学会総合大会, 広島, 2024.
BibTeX | タグ: | Links:
@conference{IEICE_Peng,
title = {Initial Investigation on Improving AED Delivery Efficiency by Encouraging Location Awareness},
author = {Helinyi Peng and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://pub.confit.atlas.jp/ja/event/general2024/presentation/A-12-05},
year = {2024},
date = {2024-03-08},
booktitle = {2024年電子情報通信学会総合大会},
address = {広島},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
厚見昴, 石岡陸, 坪内孝太, 西山勇毅, 瀬崎薫
GNSS衛星ごとの信号情報に対する点群ニューラルネットワークを用いたUVインデックス推定 Conference Award
第81回ユビキタスコンピューティングシステム（UBI）研究発表会, 2024-UBI-81 , 福岡大学, 2024.
Abstract | BibTeX | タグ: | Links:
@conference{UBI81_Atsumi,
title = {GNSS衛星ごとの信号情報に対する点群ニューラルネットワークを用いたUVインデックス推定},
author = {厚見昴 and 石岡陸 and 坪内孝太 and 西山勇毅 and 瀬崎薫 },
url = {https://sigubi.ipsj.or.jp/seminar81/},
year = {2024},
date = {2024-02-29},
urldate = {2024-02-29},
booktitle = {第81回ユビキタスコンピューティングシステム（UBI）研究発表会},
volume = {2024-UBI-81},
pages = {1 - 8},
address = {福岡大学},
abstract = {個人の曝露した紫外線量の推定手法として，スマートフォンで GNSS 衛星から受信した信号情報を用いる方法が研究されている．既存手法では衛星を天球上での位置でグループ化して信号情報をグループ内統計値で代表するため，衛星単位の情報が失われるとともに衛星同士の位置関係の情報も利用できない．そこで，本研究では点群ニューラルネットワークを用いて衛星ごとの信号情報とその近傍関係を直接利用する UV インデックス推定手法を提案する．同一地域の 2 ヶ所において GNSS 信号と UV インデックスのデータを収集して検証した結果，提案手法が推定精度を向上させると示唆された．衛星ごとの信号情報を活用する手法が発展することで，実世界の多様な環境における高精度な紫外線量推定の実現が期待できる．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Xiuwen Gu, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Toward detecting maternity neurosis by using passive mobile sensing: preliminary investigation Inproceedings
In: 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom), Nara, Japan, 2024.
Abstract | BibTeX | タグ: | Links:
@inproceedings{nokey,
title = {Toward detecting maternity neurosis by using passive mobile sensing: preliminary investigation},
author = {Xiuwen Gu, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki},
url = {https://ieeexplore.ieee.org/document/10880785},
doi = {10.1109/HEALTHCOM60970.2024.10880785},
year = {2024},
date = {2024-11-18},
booktitle = {2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom)},
address = {Nara, Japan},
abstract = {Child-rearing depression, triggered by the chronic stress of parenting, can lead to serious mental health issues if not detected early. This study uses passive mobile sensing to analyze the behavioral and psychological patterns of households with preschool children. By collecting data from 131 participants (including 18 parents of preschoolers), we aim to differentiate child-rearing anxiety and behavior patterns. Our focus includes step counts, location data, call frequency, and psychological states. Results indicate that parents of preschoolers have fewer steps, visit fewer locations, and have higher call activity. They also show higher stress and anxiety but lower depression levels, suggesting that family support may mitigate depressive symptoms. These insights could aid in developing early detection and intervention strategies for child-rearing depression.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Liqiang Xu, Yuuki Nishiyama, Kota Tsubouchi, Kaoru Sezaki
Deep Learning-Based Compressed Sensing for Mobile Device-Derived Sensor Data Inproceedings
In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp. 5023–5030, Association for Computing Machinery, Boise, ID, USA, 2024, ISBN: 9798400704369.
Abstract | BibTeX | タグ: | Links:
@inproceedings{10.1145/3627673.3680050,
title = {Deep Learning-Based Compressed Sensing for Mobile Device-Derived Sensor Data},
author = {Liqiang Xu and Yuuki Nishiyama and Kota Tsubouchi and Kaoru Sezaki},
url = {https://doi.org/10.1145/3627673.3680050},
doi = {10.1145/3627673.3680050},
isbn = {9798400704369},
year = {2024},
date = {2024-10-23},
urldate = {2024-10-23},
booktitle = {Proceedings of the 33rd ACM International Conference on Information and Knowledge Management},
pages = {5023–5030},
publisher = {Association for Computing Machinery},
address = {Boise, ID, USA},
series = {CIKM '24},
abstract = {As the capabilities of smart sensing and mobile technologies continue to evolve and expand, storing diverse sensor data on smartphones and cloud servers becomes increasingly challenging. Effective data compression is crucial to alleviate these storage pressures. Compressed sensing (CS) offers a promising approach, but traditional CS methods often struggle with the unique characteristics of sensor data-like variability, dynamic changes, and different sampling rates-leading to slow processing and poor reconstruction quality. To address these issues, we developed Mob-ISTA-1DNet, an innovative CS framework that integrates deep learning with the iterative shrinkage-thresholding algorithm (ISTA) to adaptively compress and reconstruct smartphone sensor data. This framework is designed to manage the complexities of smartphone sensor data, ensuring high-quality reconstruction across diverse conditions. We developed a mobile application to collect data from 30 volunteers over one month, including accelerometer, gyroscope, barometer, and other sensor measurements. Comparative analysis reveals that Mob-ISTA-1DNet not only enhances reconstruction accuracy but also significantly reduces processing time, consistently outperforming other methods in various scenarios.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Manaka Ito, Kota Tsubouchi, Nobuhiko Nishio, Masamichi Shimosaka, Akihito Taya, Kaoru Sezaki, Yuuki Nishiyama
Investigating Acceptable Voice-based Notification Timings through Earable Devices: A Preliminary Field Study Inproceedings Award
In: Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 30–34, Association for Computing Machinery, Melbourne, Australia, 2024.
Abstract | BibTeX | タグ: | Links:
@inproceedings{ubicomp2024_sonotify_ito,
title = {Investigating Acceptable Voice-based Notification Timings through Earable Devices: A Preliminary Field Study},
author = {Manaka Ito and Kota Tsubouchi and Nobuhiko Nishio and Masamichi Shimosaka and Akihito Taya and Kaoru Sezaki and Yuuki Nishiyama},
doi = {10.1145/3675094.3677579},
year = {2024},
date = {2024-10-05},
urldate = {2024-10-05},
booktitle = {Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {30–34},
publisher = {Association for Computing Machinery},
address = {Melbourne, Australia},
series = {UbiComp '24},
abstract = {Earable devices, a subset of wearable technology, are designed to be worn on the ear and used in daily life. These innovative devices enable users to receive voice-based notifications through a minute built-in speaker without requiring any user operations, seamlessly integrating technology into everyday activities. The timing deemed acceptable for receiving voice-based notifications through earable devices varies based on the user and surrounding situation; thus, inappropriate notification timing may reduce usability. However, determining the safest and most comfortable timing for voice-based notifications using earable devices remains unclear. This study investigates the acceptable timing of voice-based notifications through earable devices. To explore the acceptable timing, we developed a smartphone application, SoNotify, which can send dummy voice-based notifications and collect sensor data on a smartphone and an earable device. Our field studies with eight participants showed that voice-based notifications were highly acceptable during outdoor walking, with an acceptance rate of approximately 86%. However, users tended to refuse notifications in situations in which they needed to concentrate on avoiding collisions with pedestrians, cyclists, or vehicles.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Zhenbo Wang, Akihito Taya, Kato Takaaki, Kaoru Sezaki, Yuuki Nishiyama
Toward Detecting Student-Athletes' Condition Using Passive Mobile and Wearable Sensing Inproceedings Award
In: Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 51–55, Association for Computing Machinery, Melbourne VIC, Australia, 2024.
Abstract | BibTeX | タグ: | Links:
@inproceedings{ubicomp2024_alife_wang,
title = {Toward Detecting Student-Athletes' Condition Using Passive Mobile and Wearable Sensing},
author = {Zhenbo Wang and Akihito Taya and Kato Takaaki and Kaoru Sezaki and Yuuki Nishiyama},
doi = {10.1145/3675094.3677583},
year = {2024},
date = {2024-10-05},
urldate = {2024-10-05},
booktitle = {Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing},
pages = {51–55},
publisher = {Association for Computing Machinery},
address = {Melbourne VIC, Australia},
series = {UbiComp '24},
abstract = {Student-athletes (SA) face stress from balancing athletic and academic demands, and monitoring their physical and mental stress is crucial for better well-being. Questionnaires and dedicated measurement equipment have been used to assess
SAs’ mental and physical status. However, these methods are not scalable and are difficult to use continuously. In this paper, we propose a method for monitoring the physical and mental state of SA using passive mobile and wearable sensing technology to monitor it with a low burden. First, we developed a platform for collecting daily, training, and resetting behavior data from smartphones and wearable devices. Second, as a preliminary study, we collected the behavior and Stress and Recovery States Scale (SRSS) data for four weeks with 19 SA and analyzed the collected data to understand their unique behavior patterns and living environments. The results demonstrate that wearable devices and smartphones can automatically collect data on "exercise intensity during competitions" and "lifestyle patterns," specifically tailored to the mental and physical states of SA. Furthermore, this preliminary research establishes a foundation for future efforts using machine learning to predict the physical and mental states of SA.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Zengyi Han, Xuefu Dong, Liqiang Xu, Zhen Zhu, En Wang, Yuuki Nishiyama, Kaoru Sezaki
RideGuard: Micro-Mobility Steering Maneuver Prediction with Smartphones Inproceedings
In: 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS), IEEE Computer Society, Jersey City, New Jersey, USA, 2024.
BibTeX | タグ: | Links:
@inproceedings{ICDCS2024_Han,
title = {RideGuard: Micro-Mobility Steering Maneuver Prediction with Smartphones},
author = {Zengyi Han and Xuefu Dong and Liqiang Xu and Zhen Zhu and En Wang and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://icdcs2024.icdcs.org/},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
booktitle = {2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)},
publisher = {IEEE Computer Society},
address = {Jersey City, New Jersey, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Helinyi Peng, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Poster: Location Awareness in AED Retrieval: A Simulation-Based Investigation Inproceedings
In: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services, Association for Computing Machinery, Tokyo, Japan, 2024.
Abstract | BibTeX | タグ: | Links:
@inproceedings{MobiSys2024_Peng,
title = {Poster: Location Awareness in AED Retrieval: A Simulation-Based Investigation},
author = {Helinyi Peng and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://www.sigmobile.org/mobisys/2024/program.html},
year = {2024},
date = {2024-06-03},
urldate = {2024-06-03},
booktitle = {Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services},
publisher = {Association for Computing Machinery},
address = {Tokyo, Japan},
series = {MobiSys '24},
abstract = {Public awareness of Automated External Defibrillator (AED) locations is crucial for their prompt retrieval during cardiac emergencies. In this study, we propose a simulation-based approach as a preliminary step toward developing gamified mobile apps that can enhance this awareness. By simulating AED retrieval on real-world pedestrian networks under various scenarios, we identify key elements that can improve retrieval efficiency. Our findings confirm the viability of this framework and highlight crucial aspects for improvement, shedding light on more efficient future application development.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Subaru Atsumi, Riku Ishioka, Kota Tsubouchi, Yuuki Nishiyama, Kaoru Sezaki
Poster: Towards Estimating UV Index with a Smartphone Utilizing GNSS Signals as a Point Cloud Inproceedings
In: Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services, Association for Computing Machinery, Tokyo, Japan, 2024.
Abstract | BibTeX | タグ: | Links:
@inproceedings{MobiSys2024_Atsumi,
title = {Poster: Towards Estimating UV Index with a Smartphone Utilizing GNSS Signals as a Point Cloud},
author = {Subaru Atsumi and Riku Ishioka and Kota Tsubouchi and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://www.sigmobile.org/mobisys/2024/program.html},
year = {2024},
date = {2024-06-03},
urldate = {2024-06-03},
booktitle = {Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services},
publisher = {Association for Computing Machinery},
address = {Tokyo, Japan},
series = {MobiSys '24},
abstract = {Monitoring and controlling the exposure of an individual to ultraviolet radiation is crucial for personal health. The use of the global navigation satellite system (GNSS) signals received by a personal off-the-shelf smartphone has been studied as a novel estimation method. In the existing method, satellites are grouped based on their positions and the signal information is represented by group statistics, leading to a coarse estimation. We propose a new ultraviolet (UV) index estimation method that directly utilizes satellite-wise information and their spatial relationships with a point-cloud neural network, considering the similarity between GNSS signals and point clouds. We collected GNSS signals and UV index data from two locations within the same area and demonstrated that the proposed method enhances the estimation accuracy and smoothness.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Xuefu Dong, Yifei Chen, Yuuki Nishiyama, Kaoru Sezaki, Yuntao Wang, Kenneth Christofferson, Alex Mariakakis
ReHEarSSE: Recognizing Hidden-in-the-Ear Silently Spelled Expressions Inproceedings
In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, Hawaii, US, 2024.
Abstract | BibTeX | タグ: | Links:
@inproceedings{CHI2024_Dong,
title = {ReHEarSSE: Recognizing Hidden-in-the-Ear Silently Spelled Expressions},
author = {Xuefu Dong and Yifei Chen and Yuuki Nishiyama and Kaoru Sezaki and Yuntao Wang and Kenneth Christofferson and Alex Mariakakis},
url = {https://chi2024.acm.org/},
year = {2024},
date = {2024-05-14},
urldate = {2024-05-14},
booktitle = {Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems},
publisher = {Association for Computing Machinery},
address = {Hawaii, US},
series = {CHI '24},
abstract = {Silent speech interaction (SSI) allows users to discreetly input text without using their hands. Existing wearable SSI systems typically require custom devices and are limited to a small lexicon, limiting their utility to a small set of command words. This work proposes ReHEarSSE, an earbud-based ultrasonic SSI system capable of generalizing to words that do not appear in its training dataset, providing support for nearly an entire dictionary’s worth of words. As a user silently spells words, ReHEarSSE uses autoregressive features to identify subtle changes in ear canal shape. ReHEarSSE infers words using a deep learning model trained to optimize connectionist temporal classification (CTC) loss with an intermediate embedding that accounts for different letters and transitions between them. We find that ReHEarSSE recognizes unseen words with an accuracy of pmnice89.310.9%.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Junya Maruyama, Yudai Honma, Yuuki Nishiyama, Yoshihiro Kawahara
An Optimization Method for Indoor Trajectory Estimation from Spatially Sparse and Noisy Beacon Data Inproceedings
In: OHOW 2023 – The 2nd International Symposium on One Health / Infrastructure Management and sustainable built environment, pp. 6–8, MDPI, Basel, Switzerland, 2024.
BibTeX | タグ: | Links:
@inproceedings{maruyama2023optimization,
title = {An Optimization Method for Indoor Trajectory Estimation from Spatially Sparse and Noisy Beacon Data},
author = {Junya Maruyama and Yudai Honma and Yuuki Nishiyama and Yoshihiro Kawahara},
url = {https://sciforum.net/paper/view/17308},
year = {2024},
date = {2024-04-16},
urldate = {2024-04-16},
booktitle = {OHOW 2023 – The 2nd International Symposium on One Health / Infrastructure Management and sustainable built environment},
pages = {6–8},
publisher = {MDPI},
address = {Basel, Switzerland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Eri Hosonuma, Taku Yamazaki, Takumi Miyoshi, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Exploiting Spatial and Descriptive Information for Generative Compression Inproceedings
In: IEEE Consumer Communications & Networking Conference, Las Vegas, NV, USA, 2024.
Abstract | BibTeX | タグ: | Links:
@inproceedings{CCNC2024_Eri,
title = {Exploiting Spatial and Descriptive Information for Generative Compression},
author = {Eri Hosonuma and Taku Yamazaki and Takumi Miyoshi and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://ccnc2024.ieee-ccnc.org/program/posters},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {IEEE Consumer Communications & Networking Conference},
address = {Las Vegas, NV, USA},
abstract = {There will be an increase in situations where images taken in specific locations are transmitted through networks for various services. However, this trend can lead to significant communication loads due to simultaneous transmission of images from multiple locations. Therefore, it is important to reduce the amount of network traffic in image transmission. While traditional compression methods focus on minimizing information loss in images, some applications only require the retention of semantic information, suggesting potential improvements in communication efficiency. This paper proposes an image generation-based transmission method for highly-efficient communications exploiting composition and descriptive information. The proposed method extracts specific information from an image to decrease the amount of data transmission, and reconstructs the image using an image-generative model by a receiver. In addition, image compression and reconstruction in the proposed method are demonstrated through an experiment. The experimental results indicate a need for a method to evaluate the output and a method for image reconstruction based on this evaluation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Shota Ono, Taku Yamazaki, Takumi Miyoshi, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Experimental Evaluation Toward Mobility-Driven Model Integration Between Edges Inproceedings
In: 2024 IEEE 21st Consumer Communications & Networking Conference, IEEE, Las Vegas, NV, USA, 2024.
Abstract | BibTeX | タグ: | Links:
@inproceedings{CCNC_Ono,
title = {Experimental Evaluation Toward Mobility-Driven Model Integration Between Edges},
author = {Shota Ono and Taku Yamazaki and Takumi Miyoshi and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://ieeexplore.ieee.org/document/10454772},
doi = {10.1109/CCNC51664.2024.10454772},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {2024 IEEE 21st Consumer Communications & Networking Conference},
publisher = {IEEE},
address = {Las Vegas, NV, USA},
abstract = {We propose a user mobility-driven federated learning method, which integrates learning models from different regions, leveraging user mobility. This method aims to improve performance of learning models in specific regions by merging them with models from other areas. In regions with less user mobility, our method creates unique regional models, while in areas with high mobility, it integrates models for enhanced performance. Evaluation results indicate that accuracy improved with additional training, although it temporarily decreased after model integration.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Xuefu Dong, Yifei Chen, Yuuki Nishiyama, Kaoru Sezaki, Yuntao Wang, Ken Christofferson, Alex Mariakakis
ReHEarSSE: Recognizing Hidden-in-the-Ear Silently Spelled Expressions Inproceedings
In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, Honolulu, HI, USA, 2024, ISBN: 9798400703300.
Abstract | BibTeX | タグ: Acoustic sensing, autoregressive model, earable computing, silent speech interface, text entry | Links:
@inproceedings{10.1145/3613904.3642095,
title = {ReHEarSSE: Recognizing Hidden-in-the-Ear Silently Spelled Expressions},
author = {Xuefu Dong and Yifei Chen and Yuuki Nishiyama and Kaoru Sezaki and Yuntao Wang and Ken Christofferson and Alex Mariakakis},
url = {https://doi.org/10.1145/3613904.3642095},
doi = {10.1145/3613904.3642095},
isbn = {9798400703300},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems},
publisher = {Association for Computing Machinery},
address = {Honolulu, HI, USA},
series = {CHI '24},
abstract = {Silent speech interaction (SSI) allows users to discreetly input text without using their hands. Existing wearable SSI systems typically require custom devices and are limited to a small lexicon, limiting their utility to a small set of command words. This work proposes ReHEarSSE, an earbud-based ultrasonic SSI system capable of generalizing to words that do not appear in its training dataset, providing support for nearly an entire dictionary’s worth of words. As a user silently spells words, ReHEarSSE uses autoregressive features to identify subtle changes in ear canal shape. ReHEarSSE infers words using a deep learning model trained to optimize connectionist temporal classification (CTC) loss with an intermediate embedding that accounts for different letters and transitions between them. We find that ReHEarSSE recognizes 100 unseen words with an accuracy of 89.3%.},
keywords = {Acoustic sensing, autoregressive model, earable computing, silent speech interface, text entry},
pubstate = {published},
tppubtype = {inproceedings}
}
Yutong Feng, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki, Jun Liu
Compressive Detection of Stochastic Sparse Signals With Unknown Sparsity Degree Journal Article To Appear
In: IEEE Signal Processing Letters, pp. 1-5, 2023.
Abstract | BibTeX | タグ: | Links:
@article{10285002,
title = {Compressive Detection of Stochastic Sparse Signals With Unknown Sparsity Degree},
author = {Yutong Feng and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki and Jun Liu},
doi = {10.1109/LSP.2023.3324573},
year = {2023},
date = {2023-10-15},
urldate = {2023-10-15},
journal = {IEEE Signal Processing Letters},
pages = {1-5},
abstract = {In this letter, we investigate the problem of detecting compressed stochastic sparse signals with unknown sparsity degree under Bernoulli–Gaussian model. In addition to the generalized likelihood ratio test (GLRT) proposed in [1], the corresponding Rao test and Wald test are derived in this letter. By observing that obtaining their analytical performance is challenging, we further propose a new probability constraint estimator (PCE) of the unknown sparsity degree. Interestingly, by adopting the PCE, the GLRT, Rao and Wald tests are shown to be statistically equivalent and reduce to a new detector (i.e., the detector with PCE) with a simple structure. The analytical performance of the detector with PCE is thus derived, which is verified by Monte Carlo simulations. Finally, numerical experiments illustrate that the proposed Rao test and the detector with PCE outperform the original GLRT.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Shota Ono, Taku Yamazaki, Takumi Miyoshi, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
AMoND: Area-Controlled Mobile Ad-hoc Networking with Digital Twin Journal Article Open Access
In: IEEE Access, pp. 1-1, 2023.
Abstract | BibTeX | タグ: | Links:
@article{10214544,
title = {AMoND: Area-Controlled Mobile Ad-hoc Networking with Digital Twin},
author = {Shota Ono and Taku Yamazaki and Takumi Miyoshi and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
doi = {10.1109/ACCESS.2023.3304374},
year = {2023},
date = {2023-08-01},
urldate = {2023-08-01},
journal = {IEEE Access},
pages = {1-1},
abstract = {Future smart cities are expected to provide intelligent services such as predictions, detections, and automation through digital twins. However, the creation of digital twins requires the processing of an enormous amount of data, thereby leading to an increase in mobile network traffic. This traffic is produced by applications in user devices and city services, which engage in local consumption at the city scale through sensor and camera devices using mobile networks. Such increased traffic can compromise the communication speed and stability. To alleviate this burden, traffic offloading becomes a crucial consideration in the beyond-5G era. This paper presents a scheme known as Area-Controlled Mobile Ad-Hoc Networking (AMoND). AMoND uses a hierarchical structure of a location layer and an ad-hoc layer to construct area-controlled mobile ad-hoc networks (MANETs) for mutual support of the digital twin and MANETs. AMoND effectively suppresses mobile network traffic by harnessing the digital twin to assist the MANETs during data collection for the digital twin construction. Importantly, the digital twin used in AMoND focuses on the management of node location information and does not need to reproduce the real space on a computer fully. AMoND is not dependent on a specific MANET protocol and can be used as an add-on. AMoND exhibits the ability to reduce traffic volumes by up to approximately 65%, while maintaining arrival rates that are comparable to existing MANET protocols under certain conditions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Eri Hosonuma, Taku Yamazaki, Takumi Miyoshi, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Investigating Indicators for LPWA-based Congestion Estimation in Large-scale Indoor Environments Conference
International Conference on Emerging Technologies for Communications 2023 , Sapporo, Japan, 2023, ISSN: 2188-5079.
Abstract | BibTeX | タグ: | Links:
@conference{ICETC2023_Eri,
title = {Investigating Indicators for LPWA-based Congestion Estimation in Large-scale Indoor Environments},
author = {Eri Hosonuma and Taku Yamazaki and Takumi Miyoshi and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://www.ieice.org/publications/proceedings/summary.php?expandable=8&iconf=ICETC&session_num=O3&number=O3-4&year=2023},
doi = {10.34385/proc.79.O3-4},
issn = {2188-5079},
year = {2023},
date = {2023-11-29},
urldate = {2023-11-29},
booktitle = {International Conference on Emerging Technologies for Communications 2023 },
address = {Sapporo, Japan},
abstract = {Estimating congestion in a specific space is important to promote behavior changes for realizing safe and comfortable daily lives. In particular, passive estimation methods have been proposed to reduce installation costs and improve the privacy of people in a target space. They can passively estimate the number of people staying in the specific space using the received signal strength indicator (RSSI) of control messages exchanged by wireless sensor nodes installed in the space. However, they are designed for use only in open spaces such as a single room and an outdoor event venue. Therefore, multiple nodes must be installed in every space to estimate congestion areas in a spacious indoor space composed of multiple rooms such as an entire floor of a building. This paper analyzes the impact of people's position and posture in the spacious indoor environment on RSSI of exchanged messages between low power wide area (LPWA) nodes for estimating congestion areas in a spacious indoor space.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
北森迪耶, 坪内孝太, 西尾信彦, 西山勇毅, 下坂正倫
ハンズフリーのデバイス操作のための汎用イヤラブルデバイスのIMUセンサーを用いた表情認識手法 Conference Award
研究報告ユビキタスコンピューティングシステム（UBI）, 2023-UBI-80 , 2023.
BibTeX | タグ:
@conference{ubi80_earable,
title = {ハンズフリーのデバイス操作のための汎用イヤラブルデバイスのIMUセンサーを用いた表情認識手法},
author = {北森迪耶 and 坪内孝太 and 西尾信彦 and 西山勇毅 and 下坂正倫},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-01},
booktitle = {研究報告ユビキタスコンピューティングシステム（UBI）},
volume = {2023-UBI-80},
pages = {1 - 8},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
王振博, 田谷昭仁, 加藤貴昭, 瀬崎薫, 西山勇毅
学生アスリートの行動データを用いた心身のストレス・回復状態の検知に向けて：行動データと心理尺度との関係分析 Conference Award
研究報告ユビキタスコンピューティングシステム（UBI）, 2023-UBI-80 , 2023.
@conference{ubi80_alife,
title = {学生アスリートの行動データを用いた心身のストレス・回復状態の検知に向けて：行動データと心理尺度との関係分析},
author = {王振博 and 田谷昭仁 and 加藤貴昭 and 瀬崎薫 and 西山勇毅},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-01},
booktitle = {研究報告ユビキタスコンピューティングシステム（UBI）},
volume = {2023-UBI-80},
pages = {1 - 8},
abstract = {競技と学業の両立を目指す学生アスリートにとって，自らの心身の心理的ストレスと回復状況の精緻
な把握は極めて重要である．既存研究においては，身体活動や心身健康の調査は主に主観的な報告に依存
しており，持続的で客観的なデータのサポートが不足しているため，ある程度の偏りと制限性が存在する．
さらに，学生アスリートの特有のニーズやライフスタイルに対する研究は相対的に不足している．そこで
本研究では，スマートフォン・ウェアラブルデバイスに搭載されたセンサを活用して，学生アスリートの競
技中の行動データを途切れることなく継続的に追跡する．多角的なデータ分析を通じて，学生アスリート
の行動データと心身のストレス・回復状態との関連性を分析する．また，競技中のアスリートの運動量，各
睡眠ステージの時間，各異なる時間帯のデータと心身のストレス・回復状態との関連性を詳細に分析する．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
伊藤愛香, 厚見昴, Wang Zhenbo, Gu Xiuwen, 田谷昭仁, 西山勇毅, 瀬崎薫
対話中の非言語行動と大規模言語モデルを活用したシームレスな質疑応答補助システムの提案 Conference
第31回 マルチメディア通信と分散処理ワークショップ (DPSWS2023), 富山, 2023.
BibTeX | タグ: | Links:
@conference{dpsws2023_ito,
title = {対話中の非言語行動と大規模言語モデルを活用したシームレスな質疑応答補助システムの提案},
author = {伊藤愛香 and 厚見昴 and Wang Zhenbo and Gu Xiuwen and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
url = {https://www.dpsws.org/2023/},
year = {2023},
date = {2023-10-25},
urldate = {2023-10-25},
booktitle = {第31回 マルチメディア通信と分散処理ワークショップ (DPSWS2023)},
address = {富山},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
小野翔多, 山崎託, 三好匠, 田谷昭仁, 西山勇毅, 瀬崎薫
ユーザの移動性を活用した地域連携型連合学習における学習モデル統合手法の評価 Conference
第31回 マルチメディア通信と分散処理ワークショップ (DPSWS2023), 富山, 2023.
Abstract | BibTeX | タグ: | Links:
@conference{dpsws_ono,
title = {ユーザの移動性を活用した地域連携型連合学習における学習モデル統合手法の評価},
author = {小野翔多 and 山崎託 and 三好匠 and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
url = {https://www.dpsws.org/2023/},
year = {2023},
date = {2023-10-25},
urldate = {2023-10-25},
booktitle = {第31回 マルチメディア通信と分散処理ワークショップ (DPSWS2023)},
address = {富山},
abstract = {近年，クラウドやエッジなどの計算施設でセンサシングデータを分析することは多様なサービス提供に利用されているが，設備コストや電力消費の増加が問題となっている．
これに対処するため，高性能な市販デバイスを活用し，モバイルデバイス同士で学習を協力して実行する連合学習が提案されている．
連合学習は大規模施設の設置費用や消費電力を削減でき，ユーザのプライバシーも保護できる．
さらに，地域特有のデータ特性を考慮に入れた地域限定型連合学習も提案されているが，これは特定の地域を一つだけに限定して実行され，地域間の連携は考慮されていない．
そこで，本稿では，ユーザの移動性を活用して，地域ごとの学習モデルを統合する地域連携型連合学習を提案する．
この手法は，特定の地域で学習されたモデルを他の地域と統合し，それによって学習モデルの性能を向上させることを目指す．
提案手法により，地域ごとに特性のあるモデルを作成しつつ，必要に応じて地域間での学習モデルの統合が可能となり，学習モデルの性能向上が期待できる．
評価の結果，学習モデルの統合は一時的に学習精度が低下するが，追加学習によってモデルの精度が向上することが確認された．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
細沼恵里, 山崎託, 三好匠, 田谷昭仁, 西山勇毅, 瀬崎薫
領域的情報と説明的情報を用いた生成型画像圧縮伝送に関する初期検討 Conference
2023年電子情報通信学会通信ソサイエティ大会, 名古屋, 2023.
Abstract | BibTeX | タグ: | Links:
@conference{ubi78_nishiyamae,
title = {領域的情報と説明的情報を用いた生成型画像圧縮伝送に関する初期検討},
author = {細沼恵里 and 山崎託 and 三好匠 and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
url = {https://www.ieice.org/jpn_r/activities/taikai/society/2023/assets/pdf/program2023s.pdf},
year = {2023},
date = {2023-09-12},
urldate = {2023-09-12},
booktitle = {2023年電子情報通信学会通信ソサイエティ大会},
address = {名古屋},
abstract = {空間内を撮影した画像をネットワークを介して伝送するシステムは多々存在する．しかし，将来的な通信端末数の増加に伴い，今後，多地点で撮影した画像を同時に伝送することが困難になる可能性がある．これに対し，様々な画像圧縮技術が提案されているが，これらの手法は画像内の全情報を維持したまま圧縮を行う．しかし，場面によっては，画像内の全情報を維持して圧縮することが冗長となる可能性がある．そこで，画像から特定の情報を抽出して伝送し，受信端末が画像を再生成することでデータ量を削減できると考えられる．本稿では，画像から特定の情報を抽出して伝送し，受信端末が画像生成モデルを用いて画像を復元する手法を提案する．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
小野翔多, 山崎託, 三好匠, 田谷昭仁, 西山勇毅, 瀬崎薫
連合学習におけるスマートフォンの電力消費量の調査 Conference Award
2023年電子情報通信学会通信ソサイエティ大会, 名古屋, 2023.
Abstract | BibTeX | タグ: | Links:
@conference{society2023_ono,
title = {連合学習におけるスマートフォンの電力消費量の調査},
author = {小野翔多 and 山崎託 and 三好匠 and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
url = {https://www.ieice.org/jpn_r/activities/taikai/society/2023/assets/pdf/program2023s.pdf},
year = {2023},
date = {2023-09-12},
urldate = {2023-09-12},
booktitle = {2023年電子情報通信学会通信ソサイエティ大会},
address = {名古屋},
abstract = {集中管理型の連合学習では，学習のための計算量が大規模サーバやデータセンタなどの中央サーバに集中するとともに，データ集約のための通信量が増加する問題がある．
そこで，学習クライアントとしてモバイルデバイスを用いるユーザ参加型連合学習が提案されている．
本手法により，前述の計算量と通信量の問題を解決できるが，モバイルデバイスの電力消費量の増加が懸念される．モバイルデバイスは通常，電源に接続されておらずバッテリー駆動であることが多いため，学習に参加する際，電力消費量とバッテリー残量を考慮する必要がある．
本稿では，モバイルデバイスとしてスマートフォンを用い，ユーザ参加型連合学習のアプリケーションを実装して，電力消費量とバッテリー残量が学習性能に与える影響を評価する．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
大塚理恵子, 伊藤昌毅, 太田恒平, 瀬崎薫
ユーザ識別子付きスマホ位置情報を用いた大規模事業所の出勤時刻分析－時差出勤エリアマネジメントの実現に向けて－ Conference
第67回土木計画学研究発表会・春大会（自由投稿型）, 2023.
Abstract | BibTeX | タグ: area management, big data in urban transport, commuter's trip, location data with user-ID, staggered commuting | Links:
@conference{jsce_otsuka2023,
title = {ユーザ識別子付きスマホ位置情報を用いた大規模事業所の出勤時刻分析－時差出勤エリアマネジメントの実現に向けて－},
author = {大塚理恵子 and 伊藤昌毅 and 太田恒平 and 瀬崎薫},
url = {https://jsce-ip.org/2022/12/15/ip67/},
year = {2023},
date = {2023-06-03},
urldate = {2023-06-03},
booktitle = {第67回土木計画学研究発表会・春大会（自由投稿型）},
abstract = {近年，地方都市では地域公共交通利用者が減少し，交通事業者の経営が悪化している．地域公共交通を維持していくためには個々の事業者や個別路線の改善にとどまらず，地域全体の移動需要を把握し，その地域の事情を考慮した施策の検討が必要である．本研究では日々，一定の移動需要を生み出すセグメントの代表的存在である通勤者を取り上げ，地方都市の通勤行動分析に取り組む．地域全体の移動実態を俯瞰できるデータソースとしてユーザ識別子付きのスマホ位置情報に着目し，熊本県内の大規模事業所を対象に通勤者の出勤時刻分布について分析した．複数事業所を対象とした時差出勤エリアマネジメント施策の検討材料としての活用が期待できる．},
keywords = {area management, big data in urban transport, commuter's trip, location data with user-ID, staggered commuting},
pubstate = {published},
tppubtype = {conference}
}
石岡陸, 坪内孝太, 西山勇毅, 瀬崎薫
異なる環境条件におけるGNSS信号強度とUVインデックスの関係 Conference
研究報告ユビキタスコンピューティングシステム（UBI）, 2023-UBI-78 , 2023.
@conference{ubi78_ishioka,
title = {異なる環境条件におけるGNSS信号強度とUVインデックスの関係},
author = {石岡陸 and 坪内孝太 and 西山勇毅 and 瀬崎薫},
year = {2023},
date = {2023-05-24},
urldate = {2023-05-24},
booktitle = {研究報告ユビキタスコンピューティングシステム（UBI）},
volume = {2023-UBI-78},
pages = {1 - 8},
abstract = {近年，個人の浴びる紫外線（UV）量の推定手法として，スマートフォンの衛星測位システム（GNSS）センサを用いる方法が研究され始めた．GNSS による推定は，ユーザの負担なくUV 量を推定できる可能性があるという原理的な利点を持っている一方で，十分な有効性が示されている環境は限定的である．そこで，本研究では，実際のユースケースに近い形でデータを収集できるシステムを考案し，そのシステムを用いて，3 つの地域・2 つの時期・4 つの収集形態（頭の上・ポケットの中・リュックの中・地面に固定）でデータを収集した．さらに，収集したデータを用いて，UV 量推定に向けた基礎的検討として，衛星の信号強度とUV インデックスの相関を計算し，異なる条件下での相関の違いについて比較を行った．分析結果のうち，特に，スマートフォンがリュックの中にあっても相関があること，衛星によって相関の強さが大きく異なることは，今後のGNSS によるUV 量推定の精度改善につながると期待される．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
西山勇毅, 加藤貴昭, 瀬崎薫
パッシブモバイルセンシングを用いた学生アスリートのコンディション検知に向けた基礎調査 Conference
研究報告ユビキタスコンピューティングシステム（UBI）, 2023-UBI-78 , 2023.
@conference{ubi78_nishiyama,
title = {パッシブモバイルセンシングを用いた学生アスリートのコンディション検知に向けた基礎調査},
author = {西山勇毅 and 加藤貴昭 and 瀬崎薫},
year = {2023},
date = {2023-05-24},
urldate = {2023-05-24},
booktitle = {研究報告ユビキタスコンピューティングシステム（UBI）},
volume = {2023-UBI-78},
pages = {1 - 8},
abstract = {学生アスリートにとって心身のストレスとその回復状態を手軽に認識できることは，競技と学業生活を健康に過ごす上で非常に重要である．既存研究では，アンケート調査や血液検査，高性能な生体センサを用いて心身のストレス状態の計測が行われているが，計測負荷が大きく継続利用は難しい．そこで本研究では，市販のスマートフォン・ウェアラブルデバイスに搭載されたセンサを活用し，低負荷にアスリートのコンディションを検出するシステムを開発する．特に本稿では，アスリートのコンディション検知に向けて，データ収集基盤の設計と実装する．さらにデータ収集実験を実施し，収集データからコンディション検知に機構に向けた基礎的な調査を行う．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
牛島秀暢, 石岡陸, 田谷昭仁, 西山勇毅, 瀬崎薫
自動運転車と歩行者間の合意形成手法の基礎的検討 Conference
電子情報通信学会 総合大会, 2023.
@conference{ieice2023_ushijima,
title = {自動運転車と歩行者間の合意形成手法の基礎的検討},
author = {牛島秀暢 and 石岡陸 and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
year = {2023},
date = {2023-03-27},
urldate = {2023-03-27},
booktitle = {電子情報通信学会 総合大会},
abstract = {自動運転社会の到来から、これまで運転者と歩行者が暗黙的に行ってきた合意形成が行われなくなる事から、これまで起き得なかったような事故やトラブルが発生すると考えられている。そのため、新しい自動運転車と歩行者間での合意形成手法が必要となる。本研究では、狭路の一方通行における追い越し場面を想定し、音声情報と視覚情報を複合的に組み合わせた自動運転車と歩行者との合意形成手法の基礎的検討をした。},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
細沼恵里, 三好匠, 山崎託, 西山勇毅, 瀬崎薫
LPWAによる屋内空間の混雑領域推定に向けた検討 Conference
2023年電子情報通信学会総合大会, 2023.
@conference{ubi78_nishiyamab,
title = {LPWAによる屋内空間の混雑領域推定に向けた検討},
author = {細沼恵里 and 三好匠 and 山崎託 and 西山勇毅 and 瀬崎薫},
year = {2023},
date = {2023-03-24},
urldate = {2023-03-24},
booktitle = {2023年電子情報通信学会総合大会},
abstract = {空間内に設置した無線センサノードが送受信する信号の受信信号強度（RSSI: Received Signal Strength Indication）に基づき混雑度を推定する手法が提案されている．これらの手法では，室内やイベント会場内などの開けた領域にセンサノードを設置し，定期的に制御メッセージを交換することで，各ノードが受信したメッセージのRSSIに基づき領域内の滞在人数を推定する．
しかし，これらの手法によって，上述する領域が複数存在する建物のフロア全体など，広域な屋内空間内の混雑領域を推定するためには，各領域内に多数のノードを設置する必要がある．そこで，本稿では，低消費電力かつ広域通信が可能な無線通信規格であるLPWA（Low Power Wide Area）を用いた低コストかつ様々な広域屋内環境に適用可能な混雑領域推定手法の実現に向けて，LPWAノードを用いたRSSIの計測実験を行う．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
細沼恵里, 山崎託, 三好匠, 田谷昭仁, 西山勇毅, 瀬崎薫
広域屋内空間における人の滞在が受信信号強度に与える影響の解析 Conference
電子情報通信学会 情報通信マネジメント研究会（ICM）, 沖縄, 2023.
Abstract | BibTeX | タグ: LPWA, 受信信号強度, 屋内空間, 混雑領域推定 | Links:
@conference{ubi78_nishiyamac,
title = {広域屋内空間における人の滞在が受信信号強度に与える影響の解析},
author = {細沼恵里 and 山崎託 and 三好匠 and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
url = {https://ken.ieice.org/ken/program/index.php?tgs_regid=4f1b43d5391165137a938d73306c6d65ed10411e4ef8561f3b1839e6d46826f0&tgid=IEICE-ICM},
year = {2023},
date = {2023-03-17},
urldate = {2023-03-17},
booktitle = {電子情報通信学会 情報通信マネジメント研究会（ICM）},
address = {沖縄},
abstract = {快適かつ安全な都市空間を維持するため，空間内の混雑度を推定する様々な手法が提案されている．
特に，導入コストやプライバシの観点から，無線センサノード同士が送受信するメッセージの受信信号強度の変化を基に空間内の滞在人数を推定する手法が提案されている．しかし，従来手法は，一つの部屋やイベント会場など，ある特定の空間内での利用を想定している．
そのため，建物のフロア全体などの広域な屋内空間内の混雑度とその領域を推定するためには，各部屋に多数のノードを設置する必要がある．そこで，著者らは，広域通信が可能なLPWA（Low Power Wide Area）を用いることにより，少ないノード台数で混雑領域を推定する手法の実現を目指している．本稿では，混雑領域の推定に利用可能な指標を調査するため，広域屋内空間における人の滞在場所がLPWAノードの受信信号強度に与える影響を実験により解析する．},
keywords = {LPWA, 受信信号強度, 屋内空間, 混雑領域推定},
pubstate = {published},
tppubtype = {conference}
}
小野翔多, 山崎託, 三好匠, 田谷昭仁, 西山勇毅, 瀬崎薫
[奨励講演] スポット型連合学習におけるユーザ滞在時間が学習性能に与える影響の評価 Conference
電子情報通信学会 情報通信マネジメント研究会（ICM）, 沖縄, 2023.
Abstract | BibTeX | タグ: 連合学習，スポット，位置情報 | Links:
@conference{nokeyc,
title = {[奨励講演] スポット型連合学習におけるユーザ滞在時間が学習性能に与える影響の評価},
author = {小野翔多 and 山崎託 and 三好匠 and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
url = {https://ken.ieice.org/ken/program/index.php?tgs_regid=4f1b43d5391165137a938d73306c6d65ed10411e4ef8561f3b1839e6d46826f0&tgid=IEICE-ICM},
year = {2023},
date = {2023-03-17},
urldate = {2023-03-17},
booktitle = {電子情報通信学会 情報通信マネジメント研究会（ICM）},
address = {沖縄},
abstract = {連合学習では，モデルの更新のたびに通信が発生し，モバイルネットワークの通信量が増加する.そこで， 人が集まりやすい特定のスポットに着目し，そこに集まる人たちのみでデバイス間直接通信を用いて学習するスポッ ト型連合学習を提案する.提案手法は，特定のスポットに適したデータを学習し，周辺ユーザに対してサービスを提 供できるが，移動によってスポットに存在するユーザ数が変動する.本稿では，実装実験によってユーザの滞在時間 が学習に与える影響を評価した.実験の結果，デバイスの離脱が学習性能を低下させる傾向があることが確認できた.},
key = {連合学習，スポット，位置情報},
keywords = {連合学習，スポット，位置情報},
pubstate = {published},
tppubtype = {conference}
}
小野翔多, 山崎託, 三好匠, 田谷昭仁, 西山勇毅, 瀬崎薫
無線アドホックネットワークにおけるユーザ参加型連合学習の実装実験 Conference
2023 電子情報通信学会総合大会, 埼玉, 2023.
BibTeX | タグ: MANET, モバイルネットワーク, 位置情情報, 負荷分散
@conference{nokey,
title = {無線アドホックネットワークにおけるユーザ参加型連合学習の実装実験},
author = {小野翔多 and 山崎託 and 三好匠 and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
year = {2023},
date = {2023-03-07},
urldate = {2023-03-07},
booktitle = {2023 電子情報通信学会総合大会},
address = {埼玉},
keywords = {MANET, モバイルネットワーク, 位置情情報, 負荷分散},
pubstate = {published},
tppubtype = {conference}
}
小野寺文香, 石岡陸, 西山勇毅, 瀬崎薫
ウェアラブルデバイスを用いた乳幼児コンテクストの検知に向けた一検討 Conference
情報処理学会 第85回全国大会（電気通信大学）, 情報処理学会, 2023.
BibTeX | タグ: | Links:
@conference{ipsj2023_onodera,
title = {ウェアラブルデバイスを用いた乳幼児コンテクストの検知に向けた一検討},
author = {小野寺文香 and 石岡陸 and 西山勇毅 and 瀬崎薫},
url = {https://www.ipsj.or.jp/event/taikai/85/index.html},
year = {2023},
date = {2023-03-02},
urldate = {2023-03-02},
booktitle = {情報処理学会 第85回全国大会（電気通信大学）},
publisher = {情報処理学会},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Suxing Lyu, Yuuki Nishiyama, Kaoru Sezaki, Takahiko Kusakabe
Generic Trip Purpose Inference Modelling on Trip Chain Conference
研究報告ユビキタスコンピューティングシステム（UBI）：博士論文セッション, 2023-UBI-77 , 2023.
@conference{ubi77_SuxingLyu,
title = {Generic Trip Purpose Inference Modelling on Trip Chain},
author = {Suxing Lyu and Yuuki Nishiyama and Kaoru Sezaki and Takahiko Kusakabe},
year = {2023},
date = {2023-02-01},
urldate = {2023-02-01},
booktitle = {研究報告ユビキタスコンピューティングシステム（UBI）：博士論文セッション},
volume = {2023-UBI-77},
pages = {1 - 2},
abstract = {Urban transportation systems are under increasing pressure from rapid population growth. At the same time, urban functions are becoming complex and diversified. In this context, the rapid transformation of cities has long promoted the demand for human mobility (travel behavior) analysis. Trip purpose, one of the behavioral factors, is crucial to understanding human mobility generation. But keeping track of trip purpose is not easy. Nowadays, there is a huge volume of human mobility data passively collected by mobile devices. Trip purpose is right the missing item form the data. The more reliable we are in the inference of the missing items, the more beneficial we can get from the data. A generic trip purpose inference can bring semantic information to human mobility. Consequently, the decision of urban transportation and urban infrastructure development will be improved and supported based on the understanding of human mobility generation. The doctoral study reports the findings on developing the generic privacy-insensitive trip purpose inference for one-day travel (trip chain).},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs Inproceedings
In: Proceedings of IEEE Global Communications (GLOBECOM) 2023, IEEE, Kuala Lumpur, Malaysia, 2023.
@inproceedings{Globecom2023_Taya,
title = {Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs},
author = {Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
year = {2023},
date = {2023-12-04},
urldate = {2023-12-04},
booktitle = {Proceedings of IEEE Global Communications (GLOBECOM) 2023},
publisher = {IEEE},
address = {Kuala Lumpur, Malaysia},
series = {GLOBECOM 2023},
abstract = {Federated learning (FL) achieves collaborative learning without the need for data sharing, thus preventing privacy leakage. To extend FL into a fully decentralized algorithm, researchers have applied distributed optimization algorithms to FL by considering machine learning (ML) tasks as parameter optimization problems. Conversely, the consensus-based multi-hop federated distillation (CMFD) proposed in the authors' previous work makes neural network (NN) models get close with others in a function space rather than in a parameter space. Hence, this study solves two unresolved challenges of CMFD: (1) communication cost reduction and (2) visualization of model convergence. Based on a proposed dynamic communication cost reduction method (DCCR), the amount of data transferred in a network is reduced; however, with a slight degradation in the prediction accuracy. In addition, a technique for visualizing the distance between the NN models in a function space is also proposed. The technique applies a dimensionality reduction technique by approximating infinite-dimensional functions as numerical vectors to visualize the trajectory of how the models change by the distributed learning algorithm.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}