Heart activity, or at least heart rate variability, is associated with body position. Our previous studies have confirmed that impedance pneumography may be used to record respiratory function, but the calibration coefficients for this method depend on position. Data were collected from 24 students (12 male, 12 female), who alternated positions between lying (on front, back, and right side), sitting and standing. Signals from an attached iPhone's internal sensors (accelerometer, gyroscope, magnetometer) were recorded and attitude relative to gravity was calculated. The signals were subsequently segmented and marked. Five algorithms were trained and cross-validated for different sensor combinations. Without differentiation of sitting and standing, 100% accuracy was achieved using all algorithms. The classifier best differentiating these two states was based on random forests, with overall accuracy of 90%. Simple methods based on a proposed hybrid classifier were tested for online measurement without the need for signal segmentation, with 99% accuracy. The prospect of the algorithms' use in long-term studies (particularly cardiorespiratory monitoring) was assessed.