HuMAn: Complex Activity Recognition with Multi-Modal Multi-Positional Body Sensing
HuMAn: Complex Activity Recognition with Multi-Modal Multi-Positional Body Sensing
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DOI：
10.1109/tmc.2018.2841905
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发表时间：
2019-04-01
影响因子：
7.9
通讯作者：
Das, Sajal K.
中科院分区：
文献类型：
--
作者：
Bharti, Pratool;De, Debraj;Das, Sajal K.
文献中使用可穿戴设备的当前最先进的系统不能够区分大量细粒度和/或复杂的人类活动，这些活动可能看起来相似但在上下文中具有重要差异，例如躺在地板上与躺在床上与躺在沙发上。本文填补了差距，提出了一种新的系统，称为人，识别和分类复杂的人类在家里的活动与可穿戴传感。具体而言，HuMAn通过利用来自可穿戴设备的选择性多模态传感器套件来使这种分类可行，并且通过仔细地利用可穿戴设备在人体上的多个位置上的放置来增强用于活动分类的感测信息的丰富性。HuMAn系统由以下组件组成：（a）从选定的多模态传感器套件中提取实用特征集的方法;以及（B）通过利用多个身体位置中的传感器来提高准确性的新型两级结构化分类算法;以及（c）在具有最小外部基础设施支持（例如，仅几个蓝牙信标用于位置上下文）。建议的系统进行了评估与10个用户在真实的家庭环境。实验结果表明，HuMAn系统可以检测到21个复杂的家庭活动，具有较高的准确度。对于同一用户评价策略，在所有21个活动中，平均活动分类准确率高达95%。对于10折交叉验证评估策略的情况，平均分类准确率为92%，对于留一交叉验证策略的情况，平均分类准确率为75%。
Current state-of-the-art systems in the literature using wearables are not capable of distinguishing a large number of fine-grained and/or complex human activities, which may appear similar but with vital differences in context, such as lying on floor versus lying on bed versus lying on sofa. This paper fills the gap by proposing a novel system, called HuMAn, that recognizes and classifies complex at-home activities of humans with wearable sensing. Specifically, HuMAn makes such classifications feasible by leveraging selective multi-modal sensor suites from wearable devices, and enhances the richness of sensed information for activity classification by carefully leveraging placement of the wearable devices across multiple positions on the human body. The HuMAn system consists of the following components: (a) a practical feature set extraction method from selected multi-modal sensor suites; and (b) a novel two-level structured classification algorithm that improves accuracy by leveraging sensors in multiple body positions; and (c) improved refinement in classification of complex activities with minimal external infrastructure support (e.g., only a few Bluetooth beacons used for location context). The proposed system is evaluated with 10 users in real home environments. Experimental results demonstrate that the HuMAn system can detect 21 complex at-home activities with high degree of accuracy. For same-user evaluation strategy, the average activity classification accuracy is as high as 95 percent over all of the 21 activities. For the case of 10-fold cross-validation evaluation strategy, the average classification accuracy is 92 percent, and for the case of leave-one-out cross-validation strategy, the average classification accuracy is 75 percent.