Search

Assessing trunk coordination has many potential applications in health promotion. However, traditional bio-mechanical approaches are not suited for daily use as they require expensive devices and manual analysis. This study aimed to develop an approach for automatic classification of good and poor trunk coordination using widely available mobile devices. Weinvestigated different combinations of sensor locations (i.e. chest and pelvis), sensing modalities (i.e. accelerometer and gyroscope) and classification techniques (i.e. SVM, KNN, and decision tree). Results showed that using both sensing modalities at chest and pelvis with SVM produced the best classification accuracy: 96% for chest rotation and 100% forpelvis rotation. In practice, however, using one device with both sensing modalities (i.e. accelerometer and gyroscope) will achieve a better trade-off between feasibility and accuracy. In this case, the device should be fixed on the chest. KNN should be selected as the classification technique for chest rotation (best accuracy 95%), and SVM should be selected asthe classification technique for pelvis rotation (best accuracy 79%). Post hoc analysis found that poor coordination during chest rotation was associated to weak cross-correlation of angular velocity between chest and pelvis in the frontal plane, while poor coordination during pelvis rotation was associated to weak correlations of angular velocity between the threeorthogonal components at chest. This study demonstrated how simple mobile devices can capture relevant motion data and extract key features that help construct computational models for automatic assessment of trunk coordination.