A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition.
A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition.
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DOI：
10.3390/s17030529
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发表时间：
2017-03-07
期刊：
影响因子：
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通讯作者：
Zilic Z
中科院分区：
文献类型：
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作者：
Janidarmian M;Roshan Fekr A;Radecka K;Zilic Z
基于传感器的运动识别将新兴的可穿戴传感器领域与新型机器学习技术相结合，以理解底层传感器数据，并在现实生活中提供丰富的上下文信息。虽然人类活动识别（HAR）问题已经引起了研究人员的关注，但由于人类活动的多样性及其跟踪方法，它仍然是一个备受争议的主题。在考虑不同来源的异质性的同时找到这个问题的最佳预测模型可能很难从理论上分析，这强调了实验研究的必要性。因此，在本文中，我们首先创建最完整的数据集，重点是加速度计传感器，具有各种异质性来源。然后，我们对活动识别的特征表示和分类技术进行了广泛的分析（与293个分类器进行了最全面的比较）。采用主成分分析法降低特征向量的维度，同时保留必要的信息。8个传感器位置的平均分类准确度报告为96.44% ± 1.62%，10倍评价，而在受试者独立评价中达到79.92% ± 9.68%的准确度。这项研究提供了重要的证据，表明我们可以在更现实的条件下建立HAR问题的预测模型，并且仍然可以获得高度准确的结果。
Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.