BIM Sim/3D: Multi-Agent Human Activity Simulation in Indoor Spaces
Bibliographic record
Abstract
Smart buildings are a prevalent example of cyberphysical systems: embedded with sensors, they emit a continuous data stream based on which algorithms are being developed to infer the occupants' activities in order to control the building's ambience to improve the occupants' comfort and safety, and to reduce the building's energy consumption. This type of sensor-fusion-for-occupant-activity-analysis research requires large data sets; however, the security and privacy concerns around sharing data about people's activities impedes the collection, curation, and sharing of such data sets. One solution to this issue would be the creation of a human-activity simulator for generating synthetic, yet realistic, data sets. In this paper, we describe our human-activity simulator as a component in our general framework for evaluating activity-recognition methods for indoor spaces. Our simulator, developed in Unity3D, uses the Building Information Model (BIM) of the space as the context in which to simulate multiple agents, with different abilities and tasks. We conclude with a reflection of the pros and cons of our simulator design and implementation and discuss areas for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".