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Record W3039328370 · doi:10.1142/s2424905x20410020

An Admittance-controlled Force-scaling Dexterous Assistive Robotic System

2020· article· en· W3039328370 on OpenAlexaff
Jakub Piwowarczyk, Jay Carriere, Kim Adams, Mahdi Tavakoli

Bibliographic record

VenueJournal of Medical Robotics Research · 2020
Typearticle
Languageen
FieldEngineering
TopicTeleoperation and Haptic Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHaptic technologyUsabilityHuman–computer interactionComputer sciencePerceptionScalingGRASPLift (data mining)AdmittanceSimulationEngineeringPsychologyMathematics

Abstract

fetched live from OpenAlex

Play has a vital role in a child’s development; it can affect everything from social and language to cognitive and perceptual skills. However, if a child has a physical disability, the fundamental limitations of their disability may prevent them from participating in all forms of play. Construction and block play is an example of play that may be difficult for children who have reduced upper body strength and are, therefore, unable to manipulate heavier objects in space. In this paper, we propose a novel 6 degree-of-freedom admittance-controlled, force-scaling robot that will allow for children to lift heavier objects than they would normally be able to, while still retaining the full range of motion of their upper body. This assistive system is designed to retain the user’s haptic perception, allowing the user to still partially feel the weight of the objects that they are manipulating. Two user studies are done to evaluate the usability of the system. First, to ensure that the force scaling of the system does not negatively affect a user’s haptic perception, 10 able-bodied individuals were asked to order a series of buckets with identical appearances but different masses from lightest to heaviest with three different force-scaling factors. It was shown that the force amplification ability of the system does not significantly detract from users’ ability to discriminate masses. Second, to evaluate the precision and the usefulness of the force scaling of the system, users were asked to perform a challenging peg-in-hole insertion task. Results indicate that the system has a positive effect on the ability of a user to perform the task when the assistance is necessary. However, increasing amounts of assistance, past those required for participants to complete the task without issues, do not have any significant effect. The effect of a modular reacher bar that can augment the workspace of users is investigated through a similar peg-in-hole insertion task. For the trials with the modular reacher bar attached, it is shown that the system’s force amplification has a very positive effect in assisting users in completing the task. It should be noted that although the target population for this paper is children with disabilities, there can also be uses for this system as a general assistive technology for adults with upper-body weakness in their daily lives.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.079
GPT teacher head0.363
Teacher spread0.285 · 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 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

Citations5
Published2020
Admission routes1
Has abstractyes

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