Human daily activity recognition with wearable sensors based on incremental learning

L. Mo, Zengtao Feng, Jingyi Qian
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引用次数: 12

Abstract

This paper proposes a human physical activity (PA) recognition method based on incremental learning, which deal with the accuracy loss of traditional recognition system caused by the difference of different individuals. Firstly, the paper introduces the principle of incremental learning, which mainly introduced Learn++ algorithm principle, and describes the differentiation feedback optimization algorithm based on incremental learning in specific details. Then, taking the body sensor network built in the previous work as the data acquisition platform, this article conducts experiments of seven daily activities on five experimental individuals, and verifies the differentiation feedback optimization algorithm of this paper. According to the experiment results, the algorithm described in this paper has an obvious improvement effect on the physical activity recognition performance of the specific individuals.
基于增量学习的可穿戴传感器人类日常活动识别
提出了一种基于增量学习的人体身体活动识别方法,解决了传统识别系统因个体差异导致的准确率损失问题。本文首先介绍了增量学习的原理,主要介绍了Learn++算法原理,并对基于增量学习的微分反馈优化算法进行了详细的描述。然后,本文以前期工作中构建的人体传感器网络作为数据采集平台,在5个实验个体上进行了7个日常活动的实验,验证了本文的微分反馈优化算法。实验结果表明,本文所描述的算法对特定个体的身体活动识别性能有明显的提升效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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