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

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 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% of participants, to forecast synthetic trials. We recomputed four kinematic features of reaching (reaction time, movement time, posture speed, max speed) on combined recorded plus forecasted trials and compared to full-length references using ICC(2,1). Results: Chronos forecasts restored ICC (\((\geq 0.90)\)) for all parameters with only 8 real trials plus forecasts, matching the reliability of 24–28 recorded reaches (\((\Delta ICC \leq 0.07)\)). MOMENT yielded intermediate gains, while ARIMA improvements were minimal. Across cohorts and protocols, synthetic trials replaced reaches without significantly 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.