Journal · 2021

Successful Reboot of High-Performance Sporting Activities by Japanese National Women’s Handball Team in Tokyo, 2020 during the COVID-19 Pandemic: An Initiative Using the Japan Sports–Cyber Physical System (JS–CPS) of the Sports Research Innovation Project (SRIP)

Issei Ogasawara , Shigeto Hamaguchi , Ryosuke Hasegawa , Yukihiro Akeda , Naoki Ota , Gajanan Revankar , Shoji Konda , Takashi Taguchi , Toshiya Takanouchi , Kojiro Imoto , Nobukazu Okimoto , Katsuhiko Sakuma , Akira Uchiyama , Keita Yamasaki , Teruo Higashino , Kazunori Tomono , Ken Nakata

International Journal of Environmental Research and Public Health, Vol. 18, No. 18: 9865, 2021.

DOI: 10.3390/ijerph18189865

Abstract

The COVID-19 pandemic has negatively impacted sporting activities across the world. However, practical training strategies for athletes to reduce the risk of infection during the pandemic have not been definitively studied. The purpose of this report was to provide an overview of the challenges we encountered during the reboot of high-performance sporting activities of the Japanese national handball team during the 3rd wave of the COVID-19 pandemic in Tokyo, Japan. Twenty-nine Japanese national women's handball players and 24 staff participated in the study. To initiate the reboot of their first training camp after COVID-19 stay-home social policy, we conducted: web-based health-monitoring, SARS-CoV-2 screening with polymerase chain reaction (PCR) tests, real-time automated quantitative monitoring of social distancing on court using a moving image-based artificial intelligence (AI) algorithm, physical intensity evaluation with wearable heart rate (HR) and acceleration sensors, and a self-reported online questionnaire. The training camp was conducted successfully with no COVID-19 infections. The web-based health monitoring and the frequent PCR testing with short turnaround times contributed remarkably to early detection of athletes' health problems and to risk screening. During handball, AI-based on-court social-distance monitoring revealed key time-dependent spatial metrics to define player-to-player proximity. This information facilitated appropriate on- and off-game distancing behavior for teammates. Athletes regularly achieved around 80% of maximum HR during training, indicating anticipated improvements in achieving their physical intensities. Self-reported questionnaires related to the COVID management in the training camp revealed a sense of security among the athletes that allowed them to focus singularly on their training. The challenges discussed herein provided us considerable knowledge about creating and managing a safe environment for high-performing athletes in the COVID-19 pandemic via the Japan Sports-Cyber Physical System (JS-CPS) of the Sports Research Innovation Project (SRIP, Japan Sports Agency, Tokyo, Japan). This report is envisioned to provide informed decisions to coaches, trainers, policymakers from the sports federations in creating targeted, infection-free, sporting and training environments.

Research Note

COVID-19 の流行はスポーツ活動を世界的に止めましたが、感染リスクを下げながらトップ選手の練習をどう再開するかについて、実際の手順を検証した報告はほとんどありませんでした。本稿は、東京での流行第3波のさなかにハンドボール女子日本代表の合宿を再開したときに直面した課題と、その対処をまとめたものです。対象は選手29名とスタッフ24名です。

用いた手立ては複数あります。Web を使った日々の健康モニタリング、SARS-CoV-2 の PCR 検査によるスクリーニング、映像から人の位置を追う AI によるコート上の対人距離のリアルタイム自動計測、心拍と加速度を測るウェアラブルセンサによる運動強度の評価、そして自記式のオンライン質問紙です。

結果として、合宿中に感染者は出ませんでした。健康モニタリングと結果が早く戻る PCR 検査の組み合わせは、体調の変化を早い段階で拾い上げるうえで有効に働きました。AI による対人距離の計測からは、プレー中の選手どうしの近接を時間の経過とともに定量化する指標が得られ、試合中と試合外のそれぞれで適切な距離の取り方を組み立てる材料になりました。練習中の心拍は最大値の 80% 前後で推移しており、狙った運動強度が確保できていたことが確認されています。質問紙からは、この管理体制のもとで選手が安心して練習に集中できていたことが読み取れました。

これらは、スポーツ庁のスポーツ研究イノベーション拠点形成事業における Japan Sports-Cyber Physical System の枠組みで実施されたものです。

Environment-Aware Distributed Scheduling for Emergency LoRa Networks

Yuto Inaba, Tatsuya Amano, Akihito Hiromori, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), SPT-IoT 2026, pp. 1366–1371

DOI 10.1109/PerComWorkshops68308.2026.11585469

Disaster CommunicationLoRa +4

A Lightweight Vision-Language Model for Disaster Image Summarization

Hibiki Yoshizaki, Akira Uchiyama, Akihito Hiromori, Mineo Takai, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerconAI 2026, pp. 1203–1208

DOI 10.1109/PerComWorkshops68308.2026.11585419

Semantic CommunicationDisaster Response +4

Physics-Integrated Deep Learning for Urban Landslide Prediction

Ren Ozeki, Hamada Rizk, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), URBSENSE 2026, pp. 1094–1099

DOI 10.1109/PerComWorkshops68308.2026.11585337

Landslide PredictionPhysics-Integrated Learning +3

A Simulation Framework for Precision Formation Flying of Massive Satellite Swarms

Tatsuya Amano, Akihito Hiromori, Hirozumi Yamaguchi, Sumio Morioka

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerVehicle , pp. 230–235

DOI 10.1109/PerComWorkshops68308.2026.11585321

Satellite Formation FlyingDistributed Simulation +4

Ray-Tracing-Driven Pattern-Based Vehicle Recognition in ISAC Radar

Heetae Jin, Akira Uchiyama

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerRad 2026, pp. 328–333

DOI 10.1109/PerComWorkshops68308.2026.11585327

ISACBeyond 5G +4

A Questionnaire-Only Counterfactual Machine Learning Approach to Assess the Spatial Impact of Green Mobility Vehicles in Urban Parks

Rami Naeem, Srikant Manas, Tatsuya Amano, Hirozumi Yamaguchi

ICDCN 2026 Workshop: IWNDSC2026

DOI 10.1145/3737611.3776620