Exoskeleton locomotion mode prediction in construction using GPT-4o: Zero-shot learning from vision and speech
87.87% weighted F1, zero-shot GPT-4o
Research summary
87.87% weighted F1 from GPT-4o without task-specific training.
Research record
This article compares fine-tuned and zero-shot locomotion prediction from vision and speech. Its supplied PDF records acceptance; external publisher verification remains pending.
Evaluation results, cohort definitions, limitations, and source locators are maintained in the related project. This record does not duplicate the project metrics.
Selected finding
The headline result from the related case study, with its sample and source.
Article evaluation split
Study-defined held-out sample split; oversampling follows the split. Participant-held-out evaluation is not established.
Weighted F1
Reported in sourceHighest reported value: CLIP, fine-tuned, 90.05%.
Data table
| Condition | Value |
|---|---|
| CLIP, fine-tuned | 90.05% |
| GPT-4o, zero-shot | 87.87% |
| ImageBind, fine-tuned | 78.84% |
Denominator: Article evaluation samples; sample splitting does not establish participant-held-out validation.
Adapted from Exoskeleton locomotion mode prediction in construction using GPT-4o: Zero-shot learning from vision and speech, Article first page; Figure 6; evaluation protocol.
Evaluation Scope & Methodology
Samples, denominators, uncertainty, sources and limits
Approach
Compare weighted F1 within the article evaluation; retain fine-tuned versus zero-shot conditions and the source split definition.
Results and scope belong to the related case study, Zero-shot locomotion prediction from vision and speech.
Cohorts and samples
| Cohort | Sample | Definition |
|---|---|---|
| Article evaluation split | Not stated in a structured count | Study-defined held-out sample split; oversampling follows the split. Participant-held-out evaluation is not established. |
Reported measures
| Measure | Unit | Cohort | Denominator | Uncertainty | Evidence |
|---|---|---|---|---|---|
| Weighted F1 | Percent | Article evaluation split | Article evaluation samples; sample splitting does not establish participant-held-out validation | None reported | Reported in source |
Limitations
- The supplied article describes sample splitting and oversampling after the split.
- Participant-held-out validation has not been established.
- Offline prediction results do not certify an exoskeleton controller.
- Publisher metadata and final reuse terms need independent verification.
Sources
- Exoskeleton locomotion mode prediction in construction using GPT-4o: Zero-shot learning from vision and speech, Article first page; Figure 6; evaluation protocol (Supplied manuscript reviewed November 2026). Open approved PDF
Cite this record
Ehsan Ahmadi, Asif Faisal Chowdhury, Chao Wang, Mahmood Jasim. Exoskeleton locomotion mode prediction in construction using GPT-4o: Zero-shot learning from vision and speech. Journal of Intelligent Construction. Accepted, 2026. DOI 10.26599/JIC.2026.9180128.
Formatted from the supplied manuscript. Publisher metadata has not been verified, so no BibTeX is offered yet.