Activity Recognition for Smart-Lighting Automation at Home
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it