A proof of concept approach to quantify body schema using local Shannon entropy

Abstract

Abstract Body schema is shaped by sensorimotor interactions with the environment and supports coordinated movement. Measures for quantifying body schema are limited, and little is known about how lifestyle factors influence body schema organization. We propose Shannon Entropy of digital drawing strokes as a quantitative data-driven measure of body schema complexity. Forty-four adults drew themselves (body drawing) then a flower (control drawing), and completed the International Physical Activity (PA) Questionnaire. Local Shannon Entropy was computed using a block-based, data-driven algorithm and residualized for stroke length. Body drawings showed lower entropy than control drawings, offering preliminary evidence that stroke entropy may capture body-specific differences in representational complexity, meriting further investigation with counterbalanced designs. Residual entropy decreased with age, suggesting stabilization of body schema in older adults. PA was evaluated via standard Physical Activity Levels (PAL) and data-driven tertiles. PALs showed no significant effects, likely due to sample disproportion. Tertile analysis revealed a significant interaction between PA and age: more physically active participants maintained stable drawing entropy with age, suggesting PA may be associated with age-related reductions in body representation. Findings suggest stroke entropy as an interesting method for future validation studies examining the role of PA in maintaining body representations across adulthood.