Name
Abstract | ||
|---|---|---|
One of the core challenges in open-plan workspaces is to ensure a good level of concentration for the workers while performing their tasks. Hence, being able to infer concentration levels of workers will allow building designers, managers, and workers to estimate what effect different open-plan layouts will have and to find an optimal one. In this article, we present an ambient-physical system to investigate the concentration inference problem. Specifically, we deploy a series of pervasive sensors to capture various ambient and physical signals related to perceived concentration at work. The practicality of our system has been tested on two large open-plan workplaces with different designs and layouts. The empirical results highlight promising applications of pervasive sensing in occupational concentration inference, which can be adopted to enhance the capabilities of modern workplaces. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JIOT.2020.2996219 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Ambient–physical sensing,concentration inference,open-plan workplace | Journal | 7 |
Issue | ISSN | Citations |
12 | 2327-4662 | 1 |
PageRank | References | Authors |
0.35 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Saiedur Rahaman | 1 | 15 | 8.41 |
| Jonathan Liono | 2 | 11 | 4.95 |
| Yongli Ren | 3 | 142 | 23.56 |
| Jeffrey Chan | 4 | 427 | 36.55 |
| Kudo Shaw | 5 | 1 | 0.35 |
| Rawling Tim | 6 | 1 | 0.35 |
| Flora Dilys Salim | 7 | 126 | 29.67 |