On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition
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Activity recognitionRotational noiseAdditive noiseMetaclassifierAccelerometerSensor fusion
Baños, O.; et al. On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition. Sensors, 12(6): 8039-8054 (2012). [http://hdl.handle.net/10481/28450]
PatrocinadorThis work was supported in part by the Spanish CICYT Project TIN2007-60587, Junta de Andalucia Projects P07-TIC-02768 and P07-TIC-02906, the CENIT project AmIVital, of the “Centro para el Desarrollo Tecnol´ogico Industrial” (CDTI- Spain), the FPU Spanish grant AP2009-2244 and the UGR Spanish grant “Iniciaci´on a la Investigaci´on 2010/2011”.
The main objective of fusion mechanisms is to increase the individual reliability of the systems through the use of the collectivity knowledge. Moreover, fusion models are also intended to guarantee a certain level of robustness. This is particularly required for problems such as human activity recognition where runtime changes in the sensor setup seriously disturb the reliability of the initial deployed systems. For commonly used recognition systems based on inertial sensors, these changes are primarily characterized as sensor rotations, displacements or faults related to the batteries or calibration. In this work we show the robustness capabilities of a sensor-weighted fusion model when dealing with such disturbances under different circumstances. Using the proposed method, up to 60% outperformance is obtained when a minority of the sensors are artificially rotated or degraded, independent of the level of disturbance (noise) imposed. These robustness capabilities also apply for any number of sensors affected by a low to moderate noise level. The presented fusion mechanism compensates the poor performance that otherwise would be obtained when just a single sensor is considered.