Gao Lei, Bourke Alan K, Nelson John
Department of Electronic and Computer Engineering, Faculty of Science and Engineering, University of Limerick, Ireland.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7869-72. doi: 10.1109/IEMBS.2011.6091939.
This paper proposes a system for activity recognition using multi-sensor fusion. In this system, four sensors are attached to the waist, chest, thigh, and side of the body. In the study we present two solutions for factors that affect the activity recognition accuracy: the calibration drift and the sensor orientation changing. The datasets used to evaluate this system were collected from 8 subjects who were asked to perform 8 scripted normal activities of daily living (ADL), three times each. The Naïve Bayes classifier using multi-sensor fusion is adopted and achieves 70.88%-97.66% recognition accuracies for 1-4 sensors.
本文提出了一种使用多传感器融合的活动识别系统。在该系统中，四个传感器分别附着于腰部、胸部、大腿和身体侧面。在本研究中，我们针对影响活动识别准确性的因素提出了两种解决方案：校准漂移和传感器方向变化。用于评估该系统的数据集是从8名受试者收集的，要求他们每人进行8种日常脚本化正常活动（ADL），每种活动进行三次。采用了使用多传感器融合的朴素贝叶斯分类器，对于1 - 4个传感器，识别准确率达到了70.88% - 97.66%。
