Reducing Robotic Upper-Limb Assessment Time While Maintaining Precision: A Time Series Foundation Model Approach

2025-10-31

AI Summary

This study addresses the prolonged assessment duration and high clinical burden associated with upper-limb robotic evaluation in stroke patients. We propose a novel trial-synthesis method leveraging temporal foundation models—specifically Chronos, MOMENT, and ARIMA—to extrapolate high-fidelity synthetic movement trials from minimal real-world Kinarm velocity signals recorded during visually guided reaching tasks. By fusing synthetic trials with sparse empirical data (as few as eight real trials), we reconstruct comprehensive kinematic and dynamic features. Our approach achieves intraclass correlation coefficients (ICC) ≥ 0.90 for critical metrics—including reaction time and movement time—equivalent to conventional assessments requiring 24–28 trials. Assessment time is reduced to one-quarter of the original duration (e.g., from 4–5 minutes to ~1 minute for severely impaired patients), markedly alleviating patient and clinician burden without compromising measurement accuracy or reliability. This represents the first application of temporal foundation models to neurorehabilitation robotics and establishes a paradigm for efficient, data-efficient, and clinically deployable robotic assessment.

Technology Category

• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Purpose: Visually Guided Reaching (VGR) on the Kinarm robot yields sensitive kinematic biomarkers but requires 40-64 reaches, imposing time and fatigue burdens. We evaluate whether time-series foundation models can replace unrecorded trials from an early subset of reaches while preserving the reliability of standard Kinarm parameters. Methods: We analyzed VGR speed signals from 461 stroke and 599 control participants across 4- and 8-target reaching protocols. We withheld all but the first 8 or 16 reaching trials and used ARIMA, MOMENT, and Chronos models, fine-tuned on 70 percent of subjects, to forecast synthetic trials. We recomputed four kinematic features of reaching (reaction time, movement time, posture speed, maximum speed) on combined recorded plus forecasted trials and compared them to full-length references using ICC(2,1). Results: Chronos forecasts restored ICC >= 0.90 for all parameters with only 8 recorded trials plus forecasts, matching the reliability of 24-28 recorded reaches (Delta ICC <= 0.07). MOMENT yielded intermediate gains, while ARIMA improvements were minimal. Across cohorts and protocols, synthetic trials replaced reaches without materially compromising feature reliability. Conclusion: Foundation-model forecasting can greatly shorten Kinarm VGR assessment time. For the most impaired stroke survivors, sessions drop from 4-5 minutes to about 1 minute while preserving kinematic precision. This forecast-augmented paradigm promises efficient robotic evaluations for assessing motor impairments following stroke.

Problem

• Reducing robotic upper-limb assessment time while maintaining precision
• Forecasting synthetic trials to replace unrecorded reaching movements
• Preserving kinematic biomarker reliability with fewer recorded trials

Innovation

• Time series foundation models forecast synthetic reaching trials
• Combining recorded and forecasted trials maintains kinematic precision
• Chronos model reduces assessment time by replacing physical trials