Highly accurate classification of postures and activities by a shoe-based monitor through classification with rejection

Monitoring human beings' major daily activities is important for many biomedical studies. Some monitoring applications may require highly reliable identification of certain postures and activities with desired accuracies well above 99% mark. This paper suggests a method for performing highly accurate classification of postures and activities from data collected by a wearable shoe monitor (SmartShoe) through classification with rejection. The classifier used in this study is support vector machines that uses posterior probability based on the distance of an observation to the separating hyperplane to reject unreliable observations. The results show that a significant improvement (from 95.2% ± 3.5% to 99% ± 1%) of the classification accuracy has been reached after the rejection, as compared to the accuracy reported previously. Such an approach will be especially beneficial in application where high accuracy of recognition is desired while not all observations need to be assigned a class label.