A methodology for integrating AI into embodied human intelligence for the performance of complex tasks

Abstract

processes action-conditioned computational observations and filters them through a gated pipeline to produce new candidates for four-layer connectivity (c4). After expert validation, candidates update the network, sharpening DBN posteriors, reducing entropy, and thereby enhancing human performance. We instantiated this framework in automated physical rehabilitation assessment through a 12-month deployment with 10 clinicians and 105 stroke survivors. Co-design cycles developed and enriched a four-layer DBN representation of rehabilitation assessment and informed the design of a computational ensemble for automated assessment. The computational ensemble achieved 90.8% agreement with clinicians at the exercise level, 93.1% at the segment level, and 90.6% at the movement quality level. Clinicians validated automated assessments at high rates and reported improved confidence and efficiency when leveraging ensemble insights for therapy assessment and planning. This portable methodology and theory can be applied to the embodied performance of complex tasks across multiple applications.

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