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Record W2294295275 · doi:10.2196/iproc.4700

Assistive Dressing System: A Capabilities Study for Personalized Support of Dressing Activities for People Living with Dementia

2015· article· en· W2294295275 on OpenAlex
Winslow Burleson, Cecil Lozano, Vijay Ravishankar, Jeremy Rowe, Edward Mahoney, D. F. Mahoney

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIproceedings · 2015
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsClothingProcess (computing)Wearable computerInternet privacyHuman–computer interactionPsychologyComputer scienceEmbedded system

Abstract

fetched live from OpenAlex

People living with advanced stages of dementia (PWD) or other cognitive disorders do not have the luxury of remembering how to perform basic day-to-day activities, making them increasingly dependent on the assistance of caregivers. Dressing is one of the most common activities provided by caregivers. It is also one of the most stressful for both parties due to its complexity and privacy challenges posed during the process. In this paper, we present the first of its kind system (DRESS) that aims to provide much needed independence and privacy to individuals with PWDs, and afford additional freedom to their caregivers. The DRESS system is designed to deliver continuous, automated, personally tailored feedback to support PWD’s during the process of dressing. The core of DRESS consists of a computer vision based detection system that continuously monitors the dressing state of the user, identifies and prompts correct and incorrect dressing states, and provides corresponding cues to help complete the dressing process adequately with minimal, or ideally no, caregiver intervention. The DRESS system detects clothing location and orientation and status with respect to the dressing process by identifying and tracking fiducial markers (visual icons) attached to clothes. In preparation for in-home trials with PWDs, we evaluated the system’s ability to detect dressing events by asking 11 healthy participants to simulate common correct and incorrect dressing scenarios, such as donning shirt and pants inside out, back in front, and partial dressing, in a laboratory setting. We found that although the fiducial tracking system missed a few expected detections, it was generally capable of detecting dressing phases for both pants and shirt. Our study suggests that the use of a fiducial tracking system in the context of detecting dressing processes has the potential to automatically recognize, and generate prompts and feedback to assist PWDs or related cognitive disorders to correctly dress themselves with little or, ideally no assistance from their caregivers.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.040
GPT teacher head0.330
Teacher spread0.290 · 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