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首页> 外文期刊>Procedia Computer Science >Frequent Bit Pattern Mining Over Tri-axial Accelerometer Data Streams for Recognizing Human Activities and Detecting Fall
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Frequent Bit Pattern Mining Over Tri-axial Accelerometer Data Streams for Recognizing Human Activities and Detecting Fall

机译：在三轴加速度计数据流上频繁进行位模式挖掘，以识别人类活动并检测跌倒

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Human fall causes injuries and may even lead to death in the case of older age. Due to increasing elderly population every year to the total population and the health problems and risks caused by fall especially among the age group of 60 and above, detecting fall at the earliest is essential in order to avoid human loss. Basically, fall detection is considered as a classification problem which requires developing a classifier model that recognizes and classifies normal human activities and abnormal activity like fall. Most of the existing fall detection methods are based on classifiers constructed using traditional methods such as decision trees, Bayesian Networks, Support Vector Machine etc. These classifiers may miss to cover certain hidden and interesting patterns in the data and thus suffer high false positives rates. This paper presents a novel algorithm called Frequent Bit Pattern based Associative Classification (FBPAC) that maps the tri-axial accelerometer data streams to bit patterns and mines the frequent bit pattern occurring for normal activities like sitting/standing, lying and walking within a time-sensitive sliding window. Unlike normal activities, fall have significant peak acceleration and it is detected by setting most significant bit of bit pattern and thus clearly distinguishes fall from lying activity, thereby reducing false positive rates. Empirical studies are conducted by collecting real time tri-axial accelerometer data from a wearable and unobtrusive sensing device. Experimental results show that within a time-sensitive sliding window of 10seconds, the proposed algorithm achieves up to 92% overall accuracy.
机译：人为摔倒会造成伤害，如果年纪大了，甚至可能导致死亡。由于每年的老年人口相对于总人口的增加，以及由于跌倒引起的健康问题和风险，尤其是在60岁以上的年龄组中，因此尽早发现跌倒对于避免人员流失至关重要。基本上，跌倒检测被认为是分类问题，需要开发一个分类器模型来识别和分类正常的人类活动和诸如跌倒的异常活动。现有的大多数跌倒检测方法都是基于使用传统方法（例如决策树，贝叶斯网络，支持向量机等）构建的分类器。这些分类器可能会错过覆盖数据中某些隐藏且有趣的模式，因此遭受较高的误报率。本文提出了一种称为基于频繁位模式的关联分类（FBPAC）的新颖算法，该算法将三轴加速度计数据流映射到位模式，并挖掘在一段时间内针对正常活动（例如坐/站，躺和走）发生的频繁位模式。敏感的滑动窗口。与正常活动不同，跌倒具有明显的峰值加速度，可以通过设置最高有效位模式来检测跌倒，从而将跌倒与躺卧活动区分开来，从而降低误报率。通过从可穿戴且不显眼的传感设备中收集实时三轴加速度计数据来进行实证研究。实验结果表明，该算法在10秒的时间敏感滑动窗口内，可实现高达92％的整体精度。

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