Robust classification of hand posture to arm posture change using inertial measurement units

There have been many reports about misclassification generating factors during hand posture classification. Among them, arm posture change for a classifier which employs a physical change recording sensor is expected to lower the classification success rate. This work reports an robust classification of hand posture to arm posture change by adding an arm orientation feature to the classifier to overcome the factor. Two inertial measurement units and a forearm perimeter sensor were employed to measure the arm orientation and perimeter change of the forearm respectively. Two classes of hand postures were paired with continuous arm postures and classified with k-NN classifier. The results show that the suggested method improves 5% of classification success rate compared to a classifier without the arm orientation feature for two subjects.