We measure and predict states of Activation and Happiness using a body sensing application connected to smartwatches. Through the sensors of commercially available smartwatches we collect individual mood states and correlate them with body sensing data such as acceleration, heart rate, light level data, and location, through the GPS sensor built into the smartphone connected to the smartwatch. We polled users on the smartwatch for seven weeks four times per day asking for their mood state. We found that both Happiness and Activation are negatively correlated with heart beats and with the levels of light. People tend to be happier when they are moving more intensely and are feeling less activated during weekends. We also found that people with a lower Conscientiousness and Neuroticism and higher Agreeableness tend to be happy more frequently. In addition, more Activation can be predicted by lower Openness to experience and higher Agreeableness and Conscientiousness. Lastly, we find that tracking people’s geographical coordinates might play an important role in predicting Happiness and Activation. The methodology we propose is a first step towards building an automated mood tracking system, to be used for better teamwork and in combination with social network analysis studies. 相似文献
This article presents a community learning model formulated by Engineers Without Borders Colombia with the aim of providing communities with tools to create sustainable productive solutions which have relevancy for members and for potential customers. The goal of this formulation is to promote learning processes that are guided by decisions made by community members to propose sustainable and replicable initiatives. The model applicability is evidenced through a case study devoted to strengthening community-led green businesses in the Guavio Province, Colombia by collecting lessons and conclusions. Ultimately, this collection will prove useful in replicating the learning model in other similar rural communities.
投资组合的资产联合违约概率(joint,probability of default,JPoD)是一种有效的系统风险测度工具.基于JPoD分析,提出了一种新的资产组合选择优化方法,即通过计算资产池中每两种资产的JPoD值得到JPoD矩阵,利用遍历算法逐次筛选,得出具有最小系统风险的多资产组合.实证分析首先利用2014 2015年的上证综指和深证成指数据验证了JPoD方法的有效性;其次,分别利用中国股票市场数据和美国股票市场数据将所提出的资产组合选择方法与马科维茨均值-方差组合理论、随机组合方法进行比较,结果表明无论是在中国股票市场还是美国股票市场,JPoD方法都明显优于其他两种方法. 相似文献