Human motion detection in daily activity tasks using wearable sensors

In this article we present a human motion detection frame-work, based on data derived from a single tri-axial accelerometer. The framework uses a set of different pre-processing methods that produce data representations which are respectively parameterized by statistical and physical features. These features are then concatenated and classified using well-known classification algorithms for the problem of motion recognition. Experimental evaluation was carried out according to a subject-dependent scenario, meaning that the classification is performed for each subject separately using their own data and the average accuracy for all individuals is computed. The best achieved detection performance for 14 everyday human motion activities, using the USC-HAD database, was approximately 95%. The results compare favorably are competitive to the best reported performance of 93.1% for the same database.