论文标题
基于智能手机传感器的人类活动识别的熵决策融合
Entropy Decision Fusion for Smartphone Sensor based Human Activity Recognition
论文作者
论文摘要
人类活动识别是建立连续行为监测系统的重要组成部分，可用于视觉监视，患者康复，游戏甚至个人倾斜的智能家居。本文展示了我们为开发一种协作决策融合机制的努力，以整合基于智能手机传感器的人类活动数据的多种学习算法的预测分数。我们提出了一种通过根据Tsallis熵来计算和融合每个分类器的相对加权分数来融合卷积神经网络，循环卷积网络和支持向量机的方法，以改善人类活动识别性能。为了评估这种方法的适用性，在两个基准数据集（UCI-HAR和WISDM）上进行了实验。使用拟议方法获得的识别结果可与现有方法相媲美。
Human activity recognition serves an important part in building continuous behavioral monitoring systems, which are deployable for visual surveillance, patient rehabilitation, gaming, and even personally inclined smart homes. This paper demonstrates our efforts to develop a collaborative decision fusion mechanism for integrating the predicted scores from multiple learning algorithms trained on smartphone sensor based human activity data. We present an approach for fusing convolutional neural network, recurrent convolutional network, and support vector machine by computing and fusing the relative weighted scores from each classifier based on Tsallis entropy to improve human activity recognition performance. To assess the suitability of this approach, experiments are conducted on two benchmark datasets, UCI-HAR and WISDM. The recognition results attained using the proposed approach are comparable to existing methods.
