ArticleJournal of neuroengineering and rehabilitation2026
Reducing robotic upper-limb assessment time while maintaining precision: a time series foundation model approach.
Article in Journal of neuroengineering and rehabilitation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeVisually 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 agreement with full-session estimates of standard Kinarm parameters.
methodsWe 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).
resultsChronos forecasts increased ICC values for all parameters ([Formula: see text]) when combining only 8 recorded trials with forecasted trials, achieving agreement comparable to that obtained using 24-28 recorded reaches ([Formula: see text]). MOMENT yielded intermediate gains, while ARIMA improvements were minimal. Across cohorts and protocols, synthetic trials replaced reaches without significantly compromising feature reliability.
conclusionFoundation-model forecasting can greatly shorten Kinarm VGR assessment time. For the most impaired stroke survivors, sessions drop from 4-5 min to about 1 min while maintaining agreement with full-session Kinarm parameter estimates. This forecast-augmented paradigm promises efficient robotic evaluations for assessing motor impairments following stroke.
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