Multimodal LLM Framework for Context-Aware Locomotion Prediction in Construction Exoskeletons
94.3% reported classification accuracy
Research summary
94.3% reported accuracy with local perception, API reasoning, and a clarification gate.
Research record
This record preserves the title and author order of the supplied manuscript. Its research stage remains separate from the existence of a PDF or conference-formatted layout.
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.
Representative command-event run
35 command events. Seven classes with five events each; 33/35 reported correct; paper describes three runs and five scenarios.
Reported classification accuracy
Reported in sourceReported classification accuracy, Representative run: 94.3%.
Data table
| Condition | Value |
|---|---|
| Representative run | 94.3% |
Denominator: 33 correct predictions out of 35 command events in the representative run.
Adapted from Multimodal LLM Framework for Context-Aware Locomotion Prediction in Construction Exoskeletons, Table 1; Figure 3 caption; evaluation text.
Evaluation Scope & Methodology
Samples, denominators, uncertainty, sources and limits
Approach
Keep the representative 35-event result separate from multi-run claims and distinguish model output from clarification-corrected output.
Results and scope belong to the related case study, Context-aware locomotion prediction.
Cohorts and samples
| Cohort | Sample | Definition |
|---|---|---|
| Representative command-event run | 35 command events | Seven classes with five events each; 33/35 reported correct; paper describes three runs and five scenarios. |
| Reported latency evaluation | Not stated in a structured count | Figure 3 caption reports latency quantiles. Event-level timing export and exact event denominator are not supplied. |
Reported measures
| Measure | Unit | Cohort | Denominator | Uncertainty | Evidence |
|---|---|---|---|---|---|
| Reported classification accuracy | Percent | Representative command-event run | 33 correct predictions out of 35 command events in the representative run | None reported | Reported in source |
| Median end-to-end latency | Seconds | Reported latency evaluation | Event-level latency observations summarized in Figure 3; exact count requires export | None reported | Reported in source |
| 95th-percentile end-to-end latency | Seconds | Reported latency evaluation | Event-level latency observations summarized in Figure 3; exact count requires export | None reported | Reported in source |
Limitations
- Simulated live evaluation from recorded scenarios, not a field safety trial.
- The reasoning step uses an external API; the whole system is not offline.
- A 35-event representative run does not establish calibrated uncertainty.
- Event-level timings are needed to reconstruct a latency distribution.
Sources
- Multimodal LLM Framework for Context-Aware Locomotion Prediction in Construction Exoskeletons, Table 1; Figure 3 caption; evaluation text (Supplied manuscript reviewed November 2026). Open approved PDF
Cite this record
Asif Faisal Chowdhury, Ehsan Ahmadi, Chao Wang. Multimodal LLM Framework for Context-Aware Locomotion Prediction in Construction Exoskeletons. Manuscript.
Formatted from the supplied manuscript. Publisher metadata has not been verified, so no BibTeX is offered yet.