Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition
Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition
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DOI：
10.3390/s20082216
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发表时间：
2020-04-01
期刊：
影响因子：
3.9
通讯作者：
Kumar, Neeraj
中科院分区：
文献类型：
--
作者：
Javed, Abdul Rehman;Sarwar, Muhammad Usman;Kumar, Neeraj
从智能手机传感器的数据流中识别人类的身体活动对于成功实现智能环境至关重要。利用智能设备为用户提供自适应服务是当前研究的热点之一。现有的身体活动识别方法在提供快速准确的活动识别方面存在不足。本文提出了一种仅使用智能手机加速度计传感器的两个轴来识别身体活动的方法。它还研究了加速度计的每个轴在识别身体活动中的有效性和贡献。为了实现我们的方法，我们收集了12名参与者的日常生活活动数据，使用加速度计进行标记。此外，实现了三个机器学习分类器在收集的数据集上训练模型并预测活动。与现有技术相比，我们提出的方法提供了更有希望的结果，并为加速度计的每个轴对活动识别的有效性和贡献提供了强有力的理论基础。为了确保模型的可靠性，我们还评估了所提出的方法和在标准公开数据集WISDM上的观察结果，并提供了与最新研究的比较分析。该方法使用多层感知器（Multilayer Perceptron， MLP）分类器实现了93%的加权准确率，比现有方法提高了近13%。
Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.