CN110991654A - 基于睡眠与饮食分析的个性化机器学习精神压分析算法 - Google Patents
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Abstract
基于睡眠与饮食分析的个性化机器学习精神压分析算法。一种个性化人体精神压力预测方法,其特征为:以普及与易得的人体参数为基础,利用机器学习算法来实现因人而异的人体精神压力大小预测。
Description
技术领域
本发明涉及一种人员精神压力检测算法,尤其涉及一种个性化的基于睡眠等参数的机器学习算法。
背景技术
众所周知,人的心理压力和饮食习惯对于人的睡眠质量有很大的影响,通过量化人的心理压力,饮食习惯,以及睡眠质量,并且寻找四对一的映射关系可以帮助人们监控自己精神状况以及生活状况。
生活中当人们压力大时,除了通过专业的心理测试如SAS(焦虑自评量表)可以量化自己的压力指标,人是很难具体知道自己的压力的。但是在不同压力状况下人的机体可能会有不同的反应,比如:暴饮暴食,睡眠质量差。当然也可以这么说,当人的睡眠质量差,或者开始暴饮暴食时,我们可以推测他的心理压力比较大。这种推测其实在公认的焦虑自评量表(SAS)中体现的非常明显,该量表会通过调查问卷,来记录一个人做的噩梦,或者头晕等身体症状,然后来预测人的压力指标。但是想要该表是针对于所有具有焦虑症状的成年人,而人和人之间是有差异的,如果能设计一个个性化的压力监控指标且能根据人的习惯进行自动学习,就可帮助人们实现个性化的压力监控。
现有的专利如“肌电压”法(专利CN109938756A.2019.06.28)是对人体生理信号的直接测量分析,缺乏对人主观意识的参考,精度欠佳;本专利综合参考主观精神状况指标(SAS评分)、人体参量中的饮食量与睡眠质量,是一种简便、高精度的精神压力检测法,在公司职员、学校师生心理健康测评方面有十分可观的前景。
发明内容
为实现上述目的,本发明采用的技术方案是:
第一步,通过公开数据集或问卷调查取得大量受访者的一周的饮食习惯以及每天的压力测试指标和睡眠质量。
1.饮食习惯可以量化为:吃三餐的时间以及菜的品种(甜食,荤,素,半荤)与量。方法一:通过健康软件填写计算;方法二:利用图像特征提取计算饮食卡路里数。
2.睡眠质量分为睡眠时间,深度睡眠和浅睡眠时间长短(可以通过心率式智能手环获得);
3.压力指标可通过(SAS)量化测得。
之后利用数学方法如:决策回归树,进行回归预测,在数据量允许的条件下可以得到一个比较好的预模型。
第二步,实现个人化,当模型提供给个人使用时,个人再计入其个人的饮食和睡眠,压力情况,再对模型进行微调。在一定时间后,模型能够称为其定制的专属压力监控手段。
附图说明
附图是决策树
具体实施方式
其中H(D)是数据集的信息熵,Gain(D,a)为某一属性的增益。
ID3决策树算法:
样例数据集:
1 | 荤 | 大量 | 深睡长 | SAS高 | 压力大 |
2 | 荤 | 适量 | 深睡长 | SAS高 | 压力大 |
3 | 荤 | 大量 | 深睡短 | SAS高 | 压力大 |
4 | 素 | 适量 | 深睡长 | SAS低 | 压力小 |
5 | 素 | 大量 | 深睡短 | SAS高 | 压力大 |
6 | 荤 | 大量 | 深睡长 | SAS低 | 压力大 |
7 | 素 | 适量 | 深睡长 | SAS高 | 压力小 |
8 | 荤 | 大量 | 深睡短 | SAS高 | 压力大 |
9 | 素 | 大量 | 深睡长 | SAS高 | 压力大 |
计算各个属性的信息增益:
∴第一决策采用增益最大的“睡眠”属性,分类结果为:1,2,4,6,7,9;3,5,8.对每一组重复运用ID3算法,得最终如图决策树。
Claims (5)
1.一种个性化人体精神压力预测方法,其特征为:以普及与易得的人体参数为基础,利用机器学习算法来实现因人而异的人体精神压力值预测。
2.根据权利要求1所述的精神压力预测方法,其特征为:采用的数据集为饮食量、睡眠质量与SAS测评值,并且区别与应用瞳孔图像特征提取来进行人体情绪分析,饮食量、睡眠质量与人的精神状况的关系更强;区别于单纯应用人体血压、心率参数,SAS的主观测评值能更加精准地预测人体精神状况。
3.根据权利要求1所述的精神压力预测方法,其特征为:使用机器学习决策树方法,首先做大数据集的模型训练来获得基础模型;在用户端适配时通过加入其本人一段时间一定的数据量,易获得更具个性化的模型:
H(D)为数据集的信息熵,Gain(D,a)为某属性的增益;
ID3决策树算法:
计算各个属性的信息增益:
∴第一决策采用增益最大的“睡眠”属性,分类结果为:1,2,4,6,7,9;3,5,8。
4.根据权利要求1所述的精神压力预测方法,其特征为:对每一组重复运用ID3算法,得最终的决策树。并使用预剪枝、后剪枝来缓解过拟合。
5.根据权利要求1所述的精神压力预测方法,其特征为:当获得个人数据后,此决策树可以加入个人数据重新生成,并具有缺失的容纳性,即:如果用户时间较紧或心情较差不远离填某项数据,决策树可以做缺失值处理,生成预测。
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