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An effective approach for human activity recognition on smartphone

机译：智能手机人类活动认可的有效方法

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Activity recognition, which takes the sensor reading from mobile sensors as inputs and predicts a human motion activity using data mining and machine learning techniques. In this paper, we analyze the performance of two classification algorithm in an on-line activity recognition system working on Android platforms that supports on-line training and classification using only the accelerometer data. First we use the KNN classification algorithm and next we utilize an improvement of Minimum Distance and K-Nearest Neighbor classification algorithms, called Clustered KNN. For the purpose of on-line activity recognition, clustered KNN eliminates the computational complexity of KNN by creating clusters, i.e., creating smaller training sets for each activity and classification is performed based on these reduced sets. We evaluate the performance of these classifiers on four test subjects for activities of walking, running, sitting and standing in on-line activity recognition system. In this paper, we are also interested in the performance of classifiers with limited training data and the limited memory available on the phones compared to off-line.
机译：活动识别，它将传感器从移动传感器读取为输入，并使用数据挖掘和机器学习技术预测人类运动活动。在本文中，我们分析了两个分类算法在用于在android平台上工作的在线活动识别系统中的性能，仅支持加速度计数据在线培训和分类。首先，我们使用KNN分类算法和接下来，我们利用称为集群KNN的最小距离和k最近邻分类算法的改进。为了在线活动识别的目的，聚集的KNN通过创建群集来消除KNN的计算复杂度，即，为每个活动创建较小的训练集，并且基于这些减少的集合执行分类。我们在四个测试对象中评估这些分类器的性能，以便在线活动识别系统的行走，跑步，坐着和站立的活动。在本文中，我们也对分类器的性分类有关的培训数据和电话上可用的有限内存与离线相比感兴趣。

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