Beiyu Lin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Beiyu Lin has a total of 17 co-authors. Here are the top co-authors by number of shared publications:
- Diane Joyce Cook
Washington State University
2 shared publications - Alireza Ghods
Department of Computer Science Washington State University Pullman 99164-1227 WA USA
1 shared publication - Bertram Tom Jobson
Department of Civil and Environmental Engineering, Washington State University, Pullman, WA 99164, USA
1 shared publication - Brian Lamb
Washington State University
1 shared publication - Christopher Hundhausen
School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, USA
1 shared publication Diane J. Cook
The School of Electrical Engineering & Computer Science, Washington State University, Pullman, WA 99164, USA. s:
1 shared publication
Latest Publications
- 22 May 2020
- Published by SAGE Publications in Journal of Ambient Intelligence and Smart Environments
- Vol. 12 (3) , 183-201
Using continuous sensor data to formalize a model of in-home activity patterns
Formal modeling and analysis of human behavior can properly advance disciplines ranging from psychology to economics. The ability to perform such modeling has been limited by a lack of ecologically-valid data collected regarding human daily activity.
- 22 May 2020
- Published by SAGE Publications in Journal of Ambient Intelligence and Smart Environments
- Vol. 12 (3) , 183-201
Using continuous sensor data to formalize a model of in-home activity patterns
Formal modeling and analysis of human behavior can properly advance disciplines ranging from psychology to economics. The ability to perform such modeling has been limited by a lack of ecologically-valid data collected regarding human daily activity.
- 8 August 2018
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Biomedical and Health Informatics
- Vol. 23 (4) , 1742-1748
Iterative Design of Visual Analytics for a Clinician-in-the-Loop Smart Home
In order to meet the health needs of the coming “age wave”, technology needs to be designed that supports remote health monitoring and assessment. In this study we design CIL, a clinician-in-the-loop visual interface, that provides clinicians with patient behavior patterns, derived from smart home data. A total of 60 experienced nurses participated in an iterative design of an interactive graphical interface for remote behavior monitoring. Results of the study indicate that usability of the system improves over multiple iterations of participatory design. In addition, the resulting interface is useful for identifying behavior patterns that are indicative of chronic health conditions and unexpected health events. This technology offers the potential to support self-management and chronic conditions, even for individuals living in remote locations.
- 8 August 2018
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Biomedical and Health Informatics
- Vol. 23 (4) , 1742-1748
Iterative Design of Visual Analytics for a Clinician-in-the-Loop Smart Home
In order to meet the health needs of the coming “age wave”, technology needs to be designed that supports remote health monitoring and assessment. In this study we design CIL, a clinician-in-the-loop visual interface, that provides clinicians with patient behavior patterns, derived from smart home data. A total of 60 experienced nurses participated in an iterative design of an interactive graphical interface for remote behavior monitoring. Results of the study indicate that usability of the system improves over multiple iterations of participatory design. In addition, the resulting interface is useful for identifying behavior patterns that are indicative of chronic health conditions and unexpected health events. This technology offers the potential to support self-management and chronic conditions, even for individuals living in remote locations.
- Open Access
- 2 August 2017
- Published by MDPI AG in Journal of Sensor and Actuator Networks
- Vol. 6 (3) , 13
Analyzing the Relationship between Human Behavior and Indoor Air Quality
In the coming decades, as we experience global population growth and global aging issues, there will be corresponding concerns about the quality of the air we experience inside and outside buildings. Because we can anticipate that there will be behavioral changes that accompany population growth and aging, we examine the relationship between home occupant behavior and indoor air quality. To do this, we collect both sensor-based behavior data and chemical indoor air quality measurements in smart home environments. We introduce a novel machine learning-based approach to quantify the correlation between smart home features and chemical measurements of air quality, and evaluate the approach using two smart homes. The findings may help us understand the types of behavior that measurably impact indoor air quality. This information could help us plan for the future by developing an automated building system that would be used as part of a smart city.
- 2 August 2017
- Published by MDPI AG in Journal of Sensor and Actuator Networks
- Vol. 6 (3) , 13
Open AccessAnalyzing the Relationship between Human Behavior and Indoor Air Quality
In the coming decades, as we experience global population growth and global aging issues, there will be corresponding concerns about the quality of the air we experience inside and outside buildings. Because we can anticipate that there will be behavioral changes that accompany population growth and aging, we examine the relationship between home occupant behavior and indoor air quality. To do this, we collect both sensor-based behavior data and chemical indoor air quality measurements in smart home environments. We introduce a novel machine learning-based approach to quantify the correlation between smart home features and chemical measurements of air quality, and evaluate the approach using two smart homes. The findings may help us understand the types of behavior that measurably impact indoor air quality. This information could help us plan for the future by developing an automated building system that would be used as part of a smart city.