IEEE J Biomed Health Inform. 2018 May;22(3):678-685. doi: 10.1109/JBHI.2017.2705036. Epub 2017 May 17.
This paper proposes the use of posterior-adapted class-based weighted decision fusion to effectively combine multiple accelerometer data for improving physical activity recognition. The cutting-edge performance of this method is benchmarked against model-based weighted fusion and class-based weighted fusion without posterior adaptation, based on two publicly available datasets, namely PAMAP2 and MHEALTH. Experimental results show that: 1) posterior-adapted class-based weighted fusion outperformed model-based and class-based weighted fusion; 2) decision fusion with two accelerometers showed statistically significant improvement in average performance compared to the use of a single accelerometer; 3) generally, decision fusion from three accelerometers did not show further improvement from the best combination of two accelerometers; and 4) a combination of ankle and wrist located accelerometers showed the best overall performance compared to any combination of two or three accelerometers.
本文提出使用基于后验适应的基于类别的加权决策融合来有效组合多个加速度计数据，以提高体力活动识别。该方法的前沿性能与基于模型的加权融合和无后验适应的基于类别的加权融合进行了基准测试，基于两个公开可用的数据集，即 PAMAP2 和 MHEALTH。实验结果表明：1）基于后验适应的基于类别的加权融合优于基于模型和基于类别的加权融合；2）使用两个加速度计进行决策融合在平均性能方面与使用单个加速度计相比有统计学意义上的显著提高；3）通常，三个加速度计的决策融合并没有从两个加速度计的最佳组合中进一步提高；4）与两个或三个加速度计的任何组合相比，脚踝和手腕位置的加速度计组合表现出最佳的整体性能。
