基于时间序列分析的独居老人活动量研究

吉林大学学报(信息科学版) ›› 2016, Vol. 34 ›› Issue (3): 427-433.

基于时间序列分析的独居老人活动量研究

崔广才¹, 赵鑫焱¹, 左思源², 刘晓强³

1. 长春理工大学计算机科学技术学院, 长春130012; 2. 长春大学计算机科学技术学院, 长春130012;3. 吉林真才信息技术有限公司工程研究中心, 长春130012

收稿日期:2015-11-26 出版日期:2016-05-25 发布日期:2016-12-21
作者简介:崔广才(1964—), 男, 黑龙江富锦人, 长春理工大学教授, 博士生导师, 主要从事人工智能、数据挖掘研究, (Tel)86- 13504330168(E-mail)34359120@ qq. com; 通讯作者: 赵鑫焱(1979—), 女, 吉林省吉林市人, 长春理工大学硕士研究生, 主要从事数据挖掘和大数据研究, (Tel)86-13756952537(E-mail)35047253@ qq. com。
基金资助:
吉林省科技发展计划基金资助项目(20140204059SF)

Study of Activity Amount of Old People Living Alone Based on Time Series Analysis

CUI Guangcai¹, ZHAO Xinyan¹, ZUO Siyuan², LIU Xiaoqiang³

1. School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130012, China;2. School of Computer Science and Technology, Changchun University, Changchun 130012, China;3. Engineering Research Center, Jilin Zhencai Information Technology Company Limitd, Changchun 130012, China

Received:2015-11-26 Online:2016-05-25 Published:2016-12-21

摘要/Abstract

摘要：

为解决独居老人日常生活的监护问题, 提出一种基于时间序列分析与模糊模式识别的独居老人活动量研究方法。该方法将独居老人的日常活动量作为对独居老人生活状况监测的有效指标, 并利用时间序列分析对活动量时间序列建立预测模型, 利用模糊模式识别法鉴别活动量监测值与活动量预测值之间的差距, 当两者之间的差距超过正常范围时, 将预警信息反映给老人的监护人, 以便监护人对老人的生活状态是否异常予以关注。实验结果表明, 该方法对老人异常状态的识别准确率为96. 97%。从而为老人生活状况的研究提供了一种新的方法和途径。

关键词: 时间序列分析, 模糊模式识别, ARIMA 模型, 隶属度

Abstract:

To monitor the daily living of the old people living alone, a study of their activity amount based on time series analysis and fuzzy pattern recognition is proposed, in which the daily activity of the old people living alone is used as the effective index to monitor their living state. The predictive model for the activity amount time series is established by using random time series analysis. Using the fuzzy pattern recognition method, the difference between the amount of monitoring activity and the amount of forecast activity is identified. When the gap is more than the normal range, the warning information will be reflected to the guardian of the elderly. The result shows the accuracy rate of abnormal behavior recognition was 96. 97%. The study provides an alternative to activity amount monitoring of the old people.

Key words: time series analysis, fuzzy pattern recognition, autoregressive integrated moving average(ARIMA) model, membership degree

中图分类号:

TP391

崔广才, 赵鑫焱, 左思源, 刘晓强. 基于时间序列分析的独居老人活动量研究[J]. 吉林大学学报(信息科学版), 2016, 34(3): 427-433.

CUI Guangcai, ZHAO Xinyan, ZUO Siyuan, LIU Xiaoqiang. Study of Activity Amount of Old People Living Alone Based on Time Series Analysis[J]. Journal of Jilin University(Information Science Ed, 2016, 34(3): 427-433.

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