Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion
Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion
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DOI：
10.1109/ccgrid.2012.144
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发表时间：
2012-05
期刊：
影响因子：
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通讯作者：
Wenfeng Li;Junrong Bao;Xiuwen Fu;G. Fortino;Stefano Galzarano
中科院分区：
文献类型：
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作者：
Wenfeng Li;Junrong Bao;Xiuwen Fu;G. Fortino;Stefano Galzarano
身体传感器网络（BSN）由于其在日常生活中支持人类的能力而受到关注。特别是，辅助生活的实时和非侵入性监测在许多应用领域具有巨大的潜力，例如医疗保健，运动/健身，电子娱乐，社交互动和电子工厂。这些系统的基本和关键特征是检测人类动作和行为的能力。提出了一种新的人体姿态识别方法。我们的BSN系统依赖于基于D-S证据理论的信息融合方法，该方法应用于来自多个可穿戴传感器的加速度计数据。实验结果表明，所开发的原型系统是能够达到98.5%和100%之间的基本姿势（站，坐，躺，蹲）的识别准确率。
Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting).