A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer
A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer
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DOI：
10.1109/titb.2010.2051955
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发表时间：
2010-09-01
影响因子：
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通讯作者：
Kim, Tae-Seong
中科院分区：
文献类型：
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作者：
Khan, Adil Mehmood;Lee, Young-Koo;Kim, Tae-Seong
通过可穿戴传感器进行的身体活动识别可以提供有关个人功能能力和生活方式程度的宝贵信息。在本文中，我们提出了一种基于加速度计传感器的人体活动识别方法。我们提出的识别方法采用层次结构。在较低的层次上，通过统计信号特征和人工神经网络（ann）来识别活动所属的状态，即静态、过渡或动态。上层识别使用加速度信号的自回归（AR）建模，因此，将导出的AR系数与信号大小面积和倾斜角结合起来形成增强特征向量。得到的特征向量通过线性判别分析和人工神经网络进一步处理，以识别特定的人类活动。我们提出的活动识别方法仅使用附着在受试者胸部的单个三轴加速度计即可识别三种状态和15种活动，平均准确率为97.9%。
Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.