Interdigitated capacitive strain sensor enables precise yoga-inspired motion tracking.

Abstract

We report a fully textile-based interdigitated capacitive (IDC) strain sensor for wearable posture recognition. The sensor is lightweight, comfortable, and washable, and maintained stable electromechanical performance during 6,500 strain cycles, with absolute value of the gauge factor ranging from 0.67 to 0.81 across the tested strain conditions. Integrated into clothing, the sensor captured multidimensional electrical responses, including capacitance-, resistance-, and phase-related signals, during four biomechanically distinct yoga postures: High Lunge, Lunge Forward, Squat, and Tree. Using a supervised machine-learning pipeline combining handcrafted sensor-channel features and MiniROCKET time-series features, record-level classification achieved an accuracy of 94.4% and a macro-averaged F1 score of 94.2%. Record-level five-fold cross-validation further showed stable performance across folds, supporting the robustness of the classification framework within the present controlled cohort. One-vs-rest receiver operating characteristic analysis yielded area under the curve values of 90%, 94%, 99%, and 99% for High Lunge, Lunge Forward, Squat, and Tree, respectively. Channel ablation analysis identified capacitance-derived features as the most informative predictors. Statistical and temporal analyses further showed posture-dependent differences in signal magnitude, normalized response, and waveform dynamics, while supervised low-dimensional projection revealed distinct class structure. These results demonstrate the feasibility of textile-based IDC sensing for wearable movement monitoring applications such as such digital fitness.