人类活动识别的无监督学习模糊有限状态机

Gadelhag Mohmed, Ahmad Lotfi, C. Langensiepen, A. Pourabdollah
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引用次数: 5

摘要

基于低级感知数据的人类活动识别(HAR)已成为一个活跃的研究课题,在许多应用领域受到关注。许多方法被用来处理和分析收集到的感官数据,以建模和表示日常工作活动(ADW)和/或日常生活活动(ADL)。本文提出了一种基于模糊有限状态机(FuFSM)的日常活动建模方法。该方法将FuFSM与模糊c均值(fcm)聚类算法相结合,克服了定义同步活动的挑战。因此,不同的活动状态可以用一定程度的模糊性来表示。实验结果证明了该方法的有效性。使用从办公环境中收集的一组数据对模型进行测试和评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised Learning Fuzzy Finite State Machine for Human Activities Recognition
Human Activities Recognition (HAR) based on low-level sensory data has become an active research topic and attracting attention in many application domains. Many approaches are employed to process and analyse the collected sensory data for modelling and representing Activity of Daily Working (ADW) and/or Activity of Daily Living (ADL). In this paper, a novel method based on Fuzzy Finite State Machine (FuFSM) is presented to model the daily activities. The proposed method is using FuFSM integrated with Fuzzy C-Means (FCMs) clustering algorithm to overcome the challenges of defining simultaneous activities. Therefore, different states of activities could be represented with a degree of fuzziness. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a set of data that has been collected from an office environment.
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