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Comparison of Spatial Coverage of LiDAR Systems for In Home Activity of Daily Living Applications

2023· article· en· W4386920282 on OpenAlexaff
Philippe Masson, Bruce Wallace, James R. Green, Rafik Goubran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Surveillance and Tracking Methods
Canadian institutionsCarleton University
Fundersnot available
KeywordsLidarComputer scienceRemote sensingEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

LiDAR devices have potential for assessing Activities of Daily Living (ADL) within a residence. As LiDAR becomes ubiquitous in smartphones and autonomous vehicles, these devices are reducing in cost and becoming available with diverse performance and function. This paper models the coverage for 6 different commercially available LiDARs, including 3 rotational devices and 3 fixed systems, to assess the coverage provided for typical residential rooms. Field of view and range are key factors in determining coverage across several residential monitoring scenarios examined. The paper shows that one rotational LiDAR with the largest vertical field of view can provide coverage for 100% of each of the scenarios through an elevated deployment with a tilt to maximize the field of view within the lower 2m of the room.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.698
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.361
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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