Intelligent sensing systems that integrate biological signals with machine learning open new opportunities to understand and replicate animal locomotion in natural environments. Conventional telemetry methods capture only limited variables and cannot reconstruct detailed kinematics or hydrodynamic context. An electromyography (EMG)‐driven intelligent telemetry framework is introduced that decodes both body pose and environmental conditions in freely swimming fish. A custom 16‐channel telemetry unit recorded intramuscular EMG synchronized with kinematics across laminar flows at multiple speeds, two Kármán vortex streets, a reverse Kármán vortex street, and free‐swimming trials. A deep neural network mapped feature‐augmented EMG to joint angles in a head‐fixed frame, enabling midline reconstruction with sub‐centimeter accuracy (∼3.8% body length) and joint angle prediction within 4° root mean squared erroFir (R ≈ 0.81). The same pipeline classified flow regimes and discrete flow speeds with high accuracy. Channel‐efficiency analysis identified mid‐body axial electrodes as sufficient to capture most flow‐relevant information, guiding minimizing electrode count and invasiveness. Predicted kinematics were validated through computational fluid dynamics simulations and robotic embodiment that replayed decoded swimming motions. These results establish EMG as a dual‐purpose bio‐signal for locomotor and environmental inference, demonstrating an AI‐driven telemetry framework that links muscle activity, kinematics, and fluid interactions.
Afridi et al. (Thu,) studied this question.