Name
Abstract | ||
|---|---|---|
ABSTRACTThere are numerous systems for analyzing human mobility for outdoor environments at the scale of large regions or entire cities. Most of them use mobile phones to capture data information. However, these systems are agnostic to a specific business field, do not consider indoor movement, do not take into account the participants' employee or client category, and usually do not assign semantics to the visited locations. In this work, we first developed a low-cost tracking system to detect, collect, process, and store users' movement and presence data in indoor environments. Such an approach takes into consideration the users' category and location semantic. We collected these data in two university buildings used by faculty, staff, and students. We suggest that our work could monitor social distancing in indoor environments due to its human presence detection capability, whenever required, such as for the COVID-19 pandemic. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3412841.3442095 | Symposium on Applied Computing |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Flavia Schneider | 1 | 0 | 0.34 |
| Adriano Branco | 2 | 0 | 0.34 |
| Ariane M. B. Rodrigues | 3 | 0 | 0.34 |
| Felipe Oliveira Carvalho | 4 | 0 | 0.68 |
| Simone D. J. Barbosa | 5 | 0 | 0.34 |
| Markus Endler | 6 | 0 | 0.34 |
| Hélio Lopes | 7 | 0 | 0.34 |