Activity Recognition for Smart-Lighting Automation at Home
Bibliographic record
Abstract
There is an undeniable movement to blur the line between everyday objects, infrastructure, and technology. We expect our daily interaction to be grounded in an intelligent system that adapts to its environment. Our homes are now becoming smarter through smart devices, such as televisions, speakers, light bulbs, and doors, connected through the Internet of Things, enabling increased home automation. In this paper, we describe a prototype system that analyzes an image of a living space to determine the activities of the occupants to control the lighting accordingly. Most available home-automation techniques require either explicit human control or rely on simple “if this then that” routines, based on basic environmental conditions, such as temperature or time of day. Other home-automation systems use ambient sensors, placed throughout the home to recognize what the user is doing. In this paper, we present a system that takes advantage of recent advancements in vision-based activity recognition to remove the explicit human control or multitude of sensors required by other systems. Knowing full well that no system will be perfect for every use, our system provides users the ability to change the lighting through explicit interaction; users' explicit lighting adjustments are recorded, to enable future system performance adjustments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".