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Record W3139565802 · doi:10.1109/access.2021.3068877

Acti-DM1: Monitoring the Activity Level of People With Myotonic Dystrophy Type 1 Through Activity and Exercise Recognition

2021· article· en· W3139565802 on OpenAlexafffundabout
Kévin Chapron, Patrick Lapointe, Isabelle Lessard, Hans Darsmstadt-Belanger, Kévin Bouchard, Cynthia Gagnon, Mélissa Lavoie, Élise Duchesne, Sébastien Gaboury

Bibliographic record

VenueIEEE Access · 2021
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsUniversité de SherbrookeCégep de ChicoutimiUniversité du Québec à Chicoutimi
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsMyotonic dystrophyDiseasePhysical medicine and rehabilitationHealth professionalsComputer sciencePhysical therapyMedicineMedical educationHealth carePathology

Abstract

fetched live from OpenAlex

Myotonic dystrophy type 1 (DM1) is a rare disease where the highest prevalence is found in the small geographical region of Saguenay-Lac-St-Jean in Quebec, Canada. This disease impacts the quality of life and the ability of the affected people to pursue their normal day to day activities. To attenuate the impact of DM1, one could suggest that physiotherapists or other health professionals prescribe adapted physical exercise programs to carry out at home. It is, however, unpractical for the professionals to monitor every patient during their program and prohibitive in terms of cost. One alternative solution is to rely on the use of ambient technologies. To do so, in this research, our team developed an assistive system able to recognize simple mobility related activities (MA) and monitor each exercise performed during training sessions. The system can help the person by providing guidance and motivation throughout the training. The system was tested on 10 persons affected by the disease in their home for 10 weeks. The results obtained are encouraging and we discuss them in comparison with our previous work conducted in lab settings. Finally, to help further advances the research, the datasets are openly available online to the community.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.818
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
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 head0.164
GPT teacher head0.331
Teacher spread0.166 · 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 designBench or experimental
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

Citations6
Published2021
Admission routes3
Has abstractyes

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