In this article we present a human motion detection frame-work, based on data derived from a single tri-axial accel-erometer. 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 indi-viduals is computed. The best achieved detection perfor-mance for 14 everyday human motion activities, using the USC-HAD database, was approximately 95%. The results compare favorably are competitive to the best reported per-formance of 93.1% for the same database.