监测日常智能手机使用的日常变化

Anita de Mello Koch, Nicholas Kastanos, V. Aharonson
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引用次数: 0

摘要

本研究探讨了从智能手机传感器数据中检测用户日常变化。智能手机应用程序用于记录多模态传感器数据。来自60个用户的数据集用于活动分类。对这些活动进行异常检测,以检测和描述异常的行为变化。采用多任务多层感知器神经网络进行活动分类。使用两周的数据进行训练,比较了四种不同的异常检测体系结构。对14种最常见的人类活动进行分类,准确率达到65.7%。一类支持向量机产生了异常检测的最佳结果,准确率为76.8%。这些初步结果表明,所提出的方法在检测和表征人类日常变化方面具有潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring Routine Changes From Daily Smartphone Usage
The detection of user routine changes from smartphone sensor data is investigated in this study. A smartphone application is used to record multi-modal sensor data. A dataset from 60 users was used for activity classification. Anomaly detection was performed on these activities to detect and characterise abnormal behavioural changes. A Multi-task Multilayer Perceptron Neural Network was used for activity classification. Four different anomaly detection architectures were compared, using two weeks of data for training. An accuracy of 65.7 percent was achieved for activity classification of the 14 most common human activities. A One-class Support Vector Machine yielded the best results for the anomaly detection, with an accuracy of 76.8 percent. These preliminary results show a potential of the proposed methods to detect and characterise changes in human routine.
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