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Record W2761709868 · doi:10.1093/pch/20.5.e95b

171: Developmental Milestones of Assistive Technology: From Wood Walking Sticks to Virtual Reality

2015· article· en· W2761709868 on OpenAlexaff
Verónica Schiariti, Gustavo Pelligra

Bibliographic record

VenuePaediatrics & Child Health · 2015
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAssistive technologyMilestoneComputer scienceAdaptation (eye)BrailleHuman–computer interactionPsychology

Abstract

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Assistive technology (also known as adaptive technology) refers to any product, device, or equipment, whether acquired commercially, modified or customized, that is used to maintain, increase, or improve the functional abilities of individuals with disabilities. Assistive technology promotes greater independence by enabling people to perform tasks that they had great difficulty accomplishing. The evolution of assistive products has relied on state-of-the art materials and technologies. Improving functional outcomes has been always the main purpose of assistive technologies. Some of the important developmental milestones of assistive technology are worth describing. The objective of this paper is to describe the fascinating developmental trajectory of assistive technology over time. Historical review. Assistive products and devices, like the cane, have been around since the Stone Age. During the Renaissance, prosthetics developed with the use of iron, steel, copper, and wood. Functional prosthetics began to make an appearance in the 1500s. In the 1800s modern prostheses, especially for the lower limbs, were developed to restore mobility to war combats. Hearing aids were first patented in 1890s, followed by the first Braille typewrite in 1892 and the first speech in 1936. The first electric wheelchair was developed in 1950. But a major milestone of assistive technology occurred with the development of the microprocessor electronic circuit the “chip” in 1958. Microcomputer advances have been used for the design and manufacture of several assistive technologies such as speech recognition programs, robotic aids, seating and positioning, vision adaptation softwares and mobility devices. Legislations promoting the development and application of assistive technologies in 1970s, and the publication of the International Classification of Functioning, Disability and Health (ICF) in 2001, were major contributions. The ICF highlights the role of environmental factors like assistive technologies as facilitators of functional abilities. Consequently, professionals are encouraged to systematically consider environmental modifications when planning interventions for individuals with functional limitations, for example the introduction of virtual reality in pediatric neurorehabilitation interventions. In conclusion, assistive technologies provide significant enhancement to inclusive education, social participation and employment. History shows that technology has revolutionized rehabilitation interventions and the care of individuals with chronic conditions, and promises to deliver even more innovative products for years to come.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.004

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 head0.074
GPT teacher head0.401
Teacher spread0.327 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations1
Published2015
Admission routes1
Has abstractyes

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