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

TL;DR

This study demonstrates that foundation models can accurately forecast additional reaching trials in robotic assessments, significantly reducing testing time while maintaining measurement reliability, especially for stroke patients.

Contribution

We introduce a novel application of time-series foundation models to forecast robotic reaching trials, enabling shorter assessments without losing data precision.

Findings

Chronos forecasts restored high reliability (ICC >= 0.90) with fewer recorded trials.

Assessment time reduced from 4-5 minutes to about 1 minute for stroke patients.

Synthetic trials effectively replaced additional recorded reaches without compromising feature reliability.

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.