Yurtman, A.Barshan, B.2018-04-122018-04-1220171424-8220http://hdl.handle.net/11693/37003Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.EnglishAccelerometerArtificial neural networksBayesian decision makingHuman activity recognitionInertial sensorsK-nearest-neighbor classifierMachine learningMagnetometerMotion sensorsOrientation-invariant sensingSensor orientationSingular value decompositionSupport vector machinesWearable sensingAccelerometersBayesian networksClassification (of information)Decision makingGyroscopesLearning systemsMagnetometersMetadataNearest neighbor searchNeural networksPattern recognitionSingular value decompositionSupport vector machinesTime domain analysisWearable technologyBayesian decision makingsHuman activity recognitionInertial sensorK-nearest neighbor classifierSensor orientationWearable sensingWearable sensorsArticle10.3390/s17081838