Journal · 2022

A Method for Estimating Physician Stress Using Wearable Sensor Devices

Issei Imura , Yusuke Gotoh , Koji Sakai , Yu Ohara , Jun Tazoe , Hiroshi Miura , Tatsuya Hirota , Akira Uchiyama , Yoshinari Nomura

Sensors and Materials, Vol. 34, No. 8, pp. 2955-2971, 2022.

DOI: 10.18494/SAM3908

Abstract

The idea of Society 5.0 initiative has been proposed to solve various social problems by connecting virtual cyberspace and real physical space through information technology. When applying the idea to improve the work-life balance of physicians in the medical field, we must consider the increased stress owing to their long continuous working hours. Estimating the stress of physicians in their daily lives by the questionnaires is insufficient, because of the difficulty of accurate their activity recalling. By using bio-metric information such as heart rate, physical activity, and sleeping information, it was expected that the daily stress state of physicians with high accuracy. In this paper, we propose a method for estimating physician stress by analyzing bio-metric information acquired by wearing a wearable sensor device. The proposed method estimates the state of stress during daily activities by acquiring data on heart rate variability (HRV) during wakefulness as well as sleep depth during rapid eye movement (REM) and non-REM sleep. Up to seven physicians wore the wearable sensor device for the maximum of eight weeks and the sleep depth and low-/high-frequency (LF/HF) components of HRV were obtained. Our observation showed that physicians' root mean square of successive differences (rMSSDs) were constantly high in their healthy state. Therefore, the decreasing of this index can be used as an indicator of fatigue and stress. In addition, by combining LF/HF components to the rMSSDs, we may estimate the stress state of physicians and find personal stressors.

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