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Speech-Based Human-Exoskeleton Interaction for Lower Limb Motion Planning

2024· article· en· W4399801076 on OpenAlexaff
Eddie Guo, Christopher Perlette, Mojtaba Sharifi, Lukas Grasse, Matthew S. Tata, Vivian K. Mushahwar, Mahdi Tavakoli

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsUniversity of AlbertaUniversity of LethbridgeUniversity of Calgary
Fundersnot available
KeywordsExoskeletonComputer scienceMotion (physics)Motion planningMotion captureHuman–computer interactionArtificial intelligenceSimulationRobot

Abstract

fetched live from OpenAlex

This study presents a speech-based motion planning strategy (SBMP) developed for lower limb exoskeletons to facilitate safe and compliant human-robot interaction. A speech processing system, finite state machine (FSM), and central pattern generator (CPG) are the building blocks of the proposed strategy for online planning of the exoskeleton’s trajectory. A novel set of CPG dynamics is proposed to synchronize time-continuous transitions between exoskeleton locomotion states (e.g., sit, stand, walk) in response to discrete user inputs, while speech inputs are processed through an FSM. According to experimental evaluations, this speech-processing system achieved low levels of word and intent errors. Regarding locomotion, the completion time for users with voice commands was 54% faster than that using a mobile app interface. With the proposed SBMP, users are able to maintain their postural stability with both hands free. This supports its use as an effective motion planning method for the assistance and rehabilitation of individuals with lower-limb impairments.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.296
Teacher spread0.276 · 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 designSimulation or modeling
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

Citations2
Published2024
Admission routes1
Has abstractyes

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