On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition
On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition
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DOI：
10.3390/s120608039
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发表时间：
2012-06-01
期刊：
影响因子：
3.9
通讯作者：
Rojas, Ignacio
中科院分区：
文献类型：
--
作者：
Banos, Oresti;Damas, Miguel;Rojas, Ignacio
融合机制的主要目标是通过使用集体知识来提高系统的个体可靠性。此外，融合模型还旨在保证一定程度的鲁棒性。这是特别需要的问题，如人类活动的识别，在传感器设置的运行时的变化严重干扰的初始部署的系统的可靠性。对于常用的基于惯性传感器的识别系统，这些变化主要表征为传感器旋转、位移或与电池或校准相关的故障。在这项工作中，我们展示了传感器加权融合模型在不同情况下处理这种干扰时的鲁棒性。使用所提出的方法，获得高达60%的优异性能时，少数传感器被人为旋转或退化，独立的干扰（噪声）的水平。这些鲁棒性功能也适用于受低到中等噪声水平影响的任何数量的传感器。所提出的融合机制补偿了性能不佳，否则将获得当只考虑一个传感器。
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.