# Synheart Capacity: A Theory-Driven Physiological Representation of Cognitive Capacity Dynamics from Wearable Signals Source: [Synheart Capacity: A Theory-Driven Physiological Representation of Cognitive Capacity Dynamics from Wearable Signals]() Collection index: [llms.txt]() Data updated: 2026-05-27T05:00:05.520Z Authors: Yisak Debele, Henok Ademtew, Israel Goytom Published: 2026-05-23T05:58:56.000Z ## Abstract Human cognitive performance is constrained by limited mental resources, yet continuous computational estimation of cognitive capacity dynamics remains an open challenge. We propose a theory-driven multimodal learning framework that models capacity-related cognitive state as a two-dimensional physiological representation defined by voluntary resource allocation (mental effort) and overload-related strain (stress). The proposed architecture combines dual-stream encoding of cardiac (IBI/HRV) and electrodermal (EDA) signals with late fusion and task-specific output heads that independently estimate probabilistic effort and stress states. Evaluation on the SWELL-KW dataset using strict leave-one-subject-out cross-validation demonstrates cross-individual generalization (stress: 70.0\\% balanced accuracy; effort: 72.2\\%), with significant gains from multimodal integration and theory-guided supervision. Rather than collapsing physiological dynamics into a single workload label, the proposed effort--stress state-space enables structured differentiation between distinct cognitive regimes, including productive engagement and overload-related strain. Predicted state trajectories exhibit significant demand-sensitive shifts under controlled workload manipulations, with effort and stress responding differentially across interruption and time-pressure conditions. These results suggest that physiologically grounded multidimensional state representations may provide a foundation for adaptive systems capable of continuous capacity-aware monitoring and human-centered interaction. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Effort Classification on SWELL-KW (LOSO-CV)](): Effort Classification; SWELL-KW (LOSO-CV); Mean BA: 72.2. - [Effort Classification on SWELL-KW](): Effort Classification; SWELL-KW; Balanced Accuracy: 72.2. - [Stress Classification on SWELL-KW (LOSO-CV)](): Stress Classification; SWELL-KW (LOSO-CV); Mean BA: 70. - [Stress Classification on SWELL-KW](): Stress Classification; SWELL-KW; Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()