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
Helinyi Peng, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
AEDHunter: Investigating AED Retrieval in the Real World via Gamified Mobile Interaction and Sensing Journal Article ForthcomingTo Appear
In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Forthcoming.
BibTeX | タグ:
@article{nokey,
title = {AEDHunter: Investigating AED Retrieval in the Real World via Gamified Mobile Interaction and Sensing},
author = {Helinyi Peng and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
year = {2026},
date = {2026-10-11},
urldate = {2026-10-11},
journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
井上颯斗, 伊藤昌毅, 田谷昭仁, 西山勇毅, 瀬崎薫
京都市四条通における道路再配分政策が交通需要・バス運行に与える影響評価 Conference
研究報告高度交通システムとスマートコミュニティ（ITS）, 2026-ITS-104 (4), 情報処理学会, 岡山, 2026.
@conference{nokey,
title = {京都市四条通における道路再配分政策が交通需要・バス運行に与える影響評価},
author = {井上颯斗 and 伊藤昌毅 and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
year = {2026},
date = {2026-03-04},
urldate = {2026-02-25},
booktitle = {研究報告高度交通システムとスマートコミュニティ（ITS）},
volume = {2026-ITS-104},
number = {4},
pages = {1-6},
publisher = {情報処理学会},
address = {岡山},
abstract = {道路空間の再配分により車線が減少しても，交通手段の分散が生じることで自動車交通は長期的に悪化しにくいと報告されている．しかし京都市四条通では，地下鉄などへの代替手段への移行が限定的であり，道路空間再配分がバスの渋滞・遅延につながっている可能性がある．本研究では，整備前後の交通量・速度などのデータを用いて四条通の交通状態の推移を分析するとともに，実測データを基に構築した交通流シミュレーションにより，車線削減がバス運行に与える影響を評価する．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
田谷昭仁, 西山勇毅, 瀬崎薫
確率的タスクとプライバシ保護を考慮したセマンティック通信の要件検討 Conference
電子情報通信学会センサネットワークとモバイルインテリジェンス研究会（SeMI）, SeMI2025-62, 電子情報通信学会, 由布, 2026.
BibTeX | タグ:
@conference{nokey,
title = {確率的タスクとプライバシ保護を考慮したセマンティック通信の要件検討},
author = {田谷昭仁 and 西山勇毅 and 瀬崎薫},
year = {2026},
date = {2026-01-23},
urldate = {2026-01-23},
booktitle = {電子情報通信学会センサネットワークとモバイルインテリジェンス研究会（SeMI）, SeMI2025-62},
publisher = {電子情報通信学会},
address = {由布},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Xuefu Dong, Wenwei Li, Minhao Cui, Zilong Wang, Lupeng Zhang, Akihito Taya, Nishiyama Yuuki, Kaoru Sezaki, Lili Qiu, Jie Xiong
From a Point to Hundreds: Embracing LiDAR on Commodity Smartphones for Fine-grained Pulmonary Function Sensing Inproceedings ForthcomingTo Appear
In: Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems (SenSys ’26), Saint-Malo, France, Forthcoming.
BibTeX | タグ:
@inproceedings{nokey,
title = {From a Point to Hundreds: Embracing LiDAR on Commodity Smartphones for Fine-grained Pulmonary Function Sensing},
author = {Xuefu Dong and Wenwei Li and Minhao Cui and Zilong Wang and Lupeng Zhang and Akihito Taya and Nishiyama Yuuki and Kaoru Sezaki and Lili Qiu and Jie Xiong},
year = {2026},
date = {2026-05-11},
urldate = {2026-05-11},
booktitle = {Proceedings of the 23rd ACM Conference on Embedded Networked Sensor Systems (SenSys ’26)},
address = {Saint-Malo, France},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Yanzhi Li, Qize Feng, Xuefu Dong, Yuuki Nishiyama, Akihito Taya, Kaoru Sezaki
Estimation of the Center of Pressure Using Head Position During Balance Maintenance Inproceedings
In: Proc. 8th International Conference on Activity and Behavior Computing, Muroran & Hakodate, Hokkaido, Japan, 2026.
BibTeX | タグ:
@inproceedings{nokey,
title = {Estimation of the Center of Pressure Using Head Position During Balance Maintenance},
author = {Yanzhi Li and Qize Feng and Xuefu Dong and Yuuki Nishiyama and Akihito Taya and Kaoru Sezaki},
year = {2026},
date = {2026-03-10},
urldate = {2026-03-10},
booktitle = {Proc. 8th International Conference on Activity and Behavior Computing},
address = {Muroran & Hakodate, Hokkaido, Japan},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
A Mechanical Wi-Fi Antenna Device for Automatic Orientation Tuning with Bayesian Optimization Inproceedings Award
In: Proc. IEEE Consumer Communications & Networking Conference 2026, Las Vegas, NV, USA, 2026.
BibTeX | タグ:
@inproceedings{nokey,
title = {A Mechanical Wi-Fi Antenna Device for Automatic Orientation Tuning with Bayesian Optimization},
author = {Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
year = {2026},
date = {2026-01-11},
booktitle = {Proc. IEEE Consumer Communications & Networking Conference 2026},
journal = {Proc of IEEE Consumer Communications & Networking Conference 2026},
address = {Las Vegas, NV, USA},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Yutong Feng, Akihito Taya, Yuuki Nishiyama, Jun Liu, Kaoru Sezaki
Adaptive Detection of Spread Spectrum Signals With General Array Configurationg Journal Article
In: IEEE Transactions on Signal Processing, pp. 1-13, 2025.
Abstract | BibTeX | タグ: | Links:
@article{11251169,
title = {Adaptive Detection of Spread Spectrum Signals With General Array Configurationg},
author = {Yutong Feng and Akihito Taya and Yuuki Nishiyama and Jun Liu and Kaoru Sezaki},
doi = {10.1109/TSP.2025.3633721},
year = {2025},
date = {2025-11-18},
urldate = {2025-11-18},
journal = {IEEE Transactions on Signal Processing},
pages = {1-13},
abstract = {This paper investigates the signal activity detection problem for spread spectrum signals in low probability of detection communication systems under Gaussian disturbance with unknown covariance matrix. We assume that the receiver is equipped with an adaptive antenna array with general array configuration, comprising of a primary array with high–gain antennas and a reference array with low–gain antennas. It has been shown that this reference array structure can benefit interference suppression. Since it is difficult to derive the uniformly most powerful detector for the detection problem, we resort to using the Wald test and generalized likelihood ratio test (GLRT) schemes to design the detectors. Analytical performance of the proposed Wald test and GLRT is derived, indicating that the proposed two detectors bear constant false alarm rate property. Finally, numerical simulations are carried out, which verify the correctness of the theoretical analysis. Besides, it is revealed that the use of reference channel can facilitate to enhance the detection performance, but an increasing in the antenna number does not necessarily lead to an improvement in detection performance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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}
}
Qize Feng, Yanzhi Li, Akihito Taya, Sezaki Kaoru, Yuuki Nishiyama
Towards Measuring Postural Sway Using Earable Devices Conference
研究報告ユビキタスコンピューティングシステム（UBI）, 2025-UBI-88 淡路, 2025.
BibTeX | タグ:
@conference{ubi88_qize_cop,
title = {Towards Measuring Postural Sway Using Earable Devices},
author = {Qize Feng and Yanzhi Li and Akihito Taya and Sezaki Kaoru and Yuuki Nishiyama},
editor = {Postural sway quantification is critical for fall-risk assessment, rehabilitation monitoring, and VR-sickness detection, yet gold-standard force platforms remain costly, immobile, and clinic-bound. Existing wearable IMU solutions typically require dedicated waist belts that lack seamless integration into daily life. This study proposes an earable IMU paradigm leveraging consumer-style devices to bridge this gap. We collected synchronized earable IMU and force plate recordings from 10 healthy participants across 6 standardized balance conditions. Four predictive models (linear regression, random forest, Adaboost and CNN) were trained to predict clinically meaningful Center of Pressure (CoP) trajectory metrics from 6-axis ear IMU data. CNN achieved strong performance (R² values reaching 0.90 for specific metrics), with particularly robust predictions for average velocity in eyes-closed conditions (R² $>$ 0.85). These findings support earable devices as a scalable, wireless, and clinically aligned alternative for continuous balance monitoring in both clinical and home settings.},
year = {2025},
date = {2025-11-26},
urldate = {2025-11-26},
booktitle = {研究報告ユビキタスコンピューティングシステム（UBI）},
address = {淡路},
series = {2025-UBI-88},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
芝塚育大, 藤重凱人, 坪内孝太, 西山勇毅, 下坂正倫
ウェアラブルUWBの時系列CIRを用いた呼吸推定 Conference AwardTo Appear
情報処理学会ユビキタスコンピューティングシステム研究会 (IPSJ SIGUBI), 2025.
@conference{oai:ipsj.ixsq.nii.ac.jp:02005622,
title = {ウェアラブルUWBの時系列CIRを用いた呼吸推定},
author = {芝塚育大 and 藤重凱人 and 坪内孝太 and 西山勇毅 and 下坂正倫},
year = {2025},
date = {2025-11-19},
urldate = {2025-11-19},
booktitle = {情報処理学会ユビキタスコンピューティングシステム研究会 (IPSJ SIGUBI)},
abstract = {呼吸波形や換気量は健康や認知負荷などを評価する重要な生理指標であるが，測定には一般に装着負荷の大きい機器が必要となる．一方，無線信号の反射から胸郭変動を捉えられるUltra-Wideband（UWB）は，低負荷で呼吸波形まで再現可能な有力な計測手法として注目されている．しかし，従来の研究では，環境設置型での計測を前提としており，ウェアラブルに適用した場合，UWBレーダと体との相対的な位置や向きによる影響や，体動によって呼吸信号が埋もれるなどの課題については十分に議論されていない．本研究では，日常的に装着可能な形態で呼吸波形を計測することを目的として，手首装着型UWBを提案する．この計測形態では，手の配置や姿勢によるレーダと体との向き・距離の違い，および手の動作に伴うセンサ位置の変動が呼吸波形推定に影響を及ぼす可能性があるため，その影響を実験的に検証する．被験者の特定の姿勢・動作下でUWBの時系列CIRから呼吸波形を推定し，環境設置型と手首装着型とを比較して，呼吸波形復元の精度を評価する．},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Tong Xing, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Toward synthesizing infants’ daily motion data using LLM Conference
電子情報通信学会 ソサイエティ大会, 電子情報通信学会, 岡山, 2025.
BibTeX | タグ:
@conference{nokey,
title = {Toward synthesizing infants’ daily motion data using LLM},
author = {Tong Xing and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
year = {2025},
date = {2025-09-11},
urldate = {2025-09-11},
booktitle = {電子情報通信学会 ソサイエティ大会},
publisher = {電子情報通信学会},
address = {岡山},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
高天, 董学甫, 田谷昭仁, 西山勇毅, 瀬崎薫
Expression Recognition Based on Ear Canal Shape Detection Using Earbud and Ultrasound Conference Award
第24回情報科学技術フォーラム (FIT2025), 情報処理学会・電子情報通信学会 札幌, 2025, (FIT論文賞受賞論文).
BibTeX | タグ: | Links:
@conference{gao2025expression,
title = {Expression Recognition Based on Ear Canal Shape Detection Using Earbud and Ultrasound},
author = {高天 and 董学甫 and 田谷昭仁 and 西山勇毅 and 瀬崎薫},
url = {https://www.ipsj.or.jp/award/fit_ronbun.html
https://www.ipsj.or.jp/award/9faeag0000004f1r-att/CJ-002_1.pdf},
year = {2025},
date = {2025-09-03},
urldate = {2025-09-03},
booktitle = {第24回情報科学技術フォーラム (FIT2025)},
pages = {CJ-002_1},
address = {札幌},
organization = {情報処理学会・電子情報通信学会},
note = {FIT論文賞受賞論文},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
田谷昭仁, 西山勇毅, 瀬崎薫
大規模言語モデルを介したIoTにおける自然言語プログラミング Conference
電子情報通信学会センサネットワークとモバイルインテリジェンス研究会(SeMI) SeMI2025-28, 盛岡, 2025.
BibTeX | タグ:
@conference{nokey,
title = {大規模言語モデルを介したIoTにおける自然言語プログラミング},
author = {田谷昭仁 and 西山勇毅 and 瀬崎薫},
year = {2025},
date = {2025-07-31},
booktitle = {電子情報通信学会センサネットワークとモバイルインテリジェンス研究会(SeMI) SeMI2025-28},
address = {盛岡},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
田谷昭仁, 西山勇毅, 瀬崎薫
[依頼講演] 交互方向乗数法を用いた知識蒸留による分散連合学習と制御理論との関連 Conference
電子情報通信学会RISING研究会, 盛岡, 2025.
BibTeX | タグ:
@conference{nokey,
title = {[依頼講演] 交互方向乗数法を用いた知識蒸留による分散連合学習と制御理論との関連},
author = {田谷昭仁 and 西山勇毅 and 瀬崎薫},
year = {2025},
date = {2025-07-30},
booktitle = {電子情報通信学会RISING研究会},
address = {盛岡},
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にむけて 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にむけて},
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},
urldate = {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}
}
Zengyi Han, Yuuki Nishiyama, Kaoru Sezaki
RideStyle: Riding Style Representation from Head-Body Dynamics via Adversarial Learning Inproceedings
In: EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Shanghai, China, 2025.
BibTeX | タグ: | Links:
@inproceedings{2025mobiquitous_han,
title = {RideStyle: Riding Style Representation from Head-Body Dynamics via Adversarial Learning},
author = {Zengyi Han and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://mobiquitous.eai-conferences.org/2025/program/},
year = {2025},
date = {2025-11-07},
booktitle = {EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
address = {Shanghai, China},
series = {MobiQuitous},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Xuefu Dong, Liqiang Xu, Lixing He, Zengyi Han, Kenneth Christofferson, Yifei Chen, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
Recognizing Hidden-in-the-Ear Private Key for Reliable Silent Speech Interface Using Multi-Task Learning Inproceedings Award
In: Companion of the 2025 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, Association for Computing Machinery, Finland, 2025.
@inproceedings{ubicomp2025_dong,
title = {Recognizing Hidden-in-the-Ear Private Key for Reliable Silent Speech Interface Using Multi-Task Learning},
author = {Xuefu Dong and Liqiang Xu and Lixing He and Zengyi Han and Kenneth Christofferson and Yifei Chen and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
year = {2025},
date = {2025-10-16},
urldate = {2025-10-16},
booktitle = {Companion of the 2025 on ACM International Joint Conference on Pervasive and Ubiquitous Computing},
publisher = {Association for Computing Machinery},
address = {Finland},
series = {UbiComp '25},
abstract = {We explore a silent speech pathway that learns a person-specific “hidden-in-the-ear” key by jointly modeling whisper-like audio and subtle ear-canal/muscle movements. Using multi-task learning, the system links these signals to produce a robust, privacy-aware biometric that strengthens authentication and improves reliability for earable-based silent speech interfaces.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Jia Tang, Xiuwen Gu, Akihito Taya, Kaoru Sezaki, Yuuki Nishiyama
Toward Detecting Postpartum Depression Using Passive Mobile Sensing: Exploratory Analysis Inproceedings
In: Companion of the 2025 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, Association for Computing Machinery, Finland, 2025.
BibTeX | タグ: | Links:
@inproceedings{10.1145/3714394.3754405,
title = {Toward Detecting Postpartum Depression Using Passive Mobile Sensing: Exploratory Analysis},
author = {Jia Tang and Xiuwen Gu and Akihito Taya and Kaoru Sezaki and Yuuki Nishiyama},
url = {https://doi.org/10.1145/3714394.3754405},
doi = {10.1145/3714394.3754405},
year = {2025},
date = {2025-10-12},
urldate = {2025-10-12},
booktitle = {Companion of the 2025 on ACM International Joint Conference on Pervasive and Ubiquitous Computing},
publisher = {Association for Computing Machinery},
address = {Finland},
series = {UbiComp '25},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Helinyi Peng, Akihito Taya, Yuuki Nishiyama, Kaoru Sezaki
AEDHunter: Investigating and Enhancing AED Retrieval through a Mobile Application Inproceedings Open Access
In: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713958.
Abstract | BibTeX | タグ: | Links:
@inproceedings{10.1145/3706599.3719746,
title = {AEDHunter: Investigating and Enhancing AED Retrieval through a Mobile Application},
author = {Helinyi Peng and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://dl.acm.org/doi/10.1145/3706599.3719746},
doi = {10.1145/3706599.3719746},
isbn = {9798400713958},
year = {2025},
date = {2025-04-25},
urldate = {2025-04-25},
booktitle = {Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {CHI EA '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 = {inproceedings}
}
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}
}
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}
}
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, 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 and Akihito Taya and Yuuki Nishiyama and Kaoru Sezaki},
url = {https://ieeexplore.ieee.org/document/10880785},
doi = {10.1109/HEALTHCOM60970.2024.10880785},
year = {2024},
date = {2024-11-18},
urldate = {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}
}
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}
}
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}
}
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}
}
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}
}
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}
}
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}
}
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}
}