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Record W2036845658 · doi:10.1109/iembs.2011.6091299

Change-of-state determination to recognize mobility activities using a BlackBerry smartphone

2011· article· en· W2036845658 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsAccelerometerTimerComputer scienceContext (archaeology)Global Positioning SystemWearable computerElevatorIdentification (biology)Real-time computingEmbedded systemHuman–computer interactionMicrocontrollerEngineering

Abstract

fetched live from OpenAlex

A Wearable Mobility Monitoring System (WMMS) can be a useful tool for rehabilitation decision-making. This paper presents preliminary design and evaluation of a WMMS proof-of-concept system. Software was developed for the BlackBerry 9550, using the integrated three axes accelerometer, GPS, video camera, and timer to identify mobility changes-of-state (CoS) between static activities, walking-related activities, taking an elevator, bathroom activities, working in the kitchen, and meal preparation (five able-bodied subjects). This pilot project provides insight into new algorithms and features that recognize CoS and activities in real-time. Following features extraction from the sensor data, two decision trees were used to distinguish the CoS and activities. Real-time CoS identification triggered BlackBerry video recording for improved mobility context analysis during post-processing.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.950
Threshold uncertainty score0.664

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 communication0.0000.002
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 head0.176
GPT teacher head0.298
Teacher spread0.122 · 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