ABSTRACT The purpose of this study is to present an exploratory analysis of the relationship between body responses, immediate environmental factors and stress-related events. Using an experimental setup for data collection and information fusion from wearable sensors, this work tests three Machine Learning Algorithms for supervised classification of stress detection. Body skin temperature and electrodermal activity are processed to identify patterns of stress reaction while walking. Immediate environmental features from continuous sensor data are found to be useful in identifying stress-related events. The experiment was carried out in Singapore, a city-state with hot tropical weather where the climate conditions of the city encourage urban planners to meet walkability needs of the residents as well as to ensure short walking trips.

中文翻译：

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在热带城市早晨散步时的身体反应

摘要 本研究的目的是对身体反应、直接环境因素和压力相关事件之间的关系进行探索性分析。使用可穿戴传感器数据收集和信息融合的实验设置，这项工作测试了三种机器学习算法，用于压力检测的监督分类。处理身体皮肤温度和皮肤电活动以识别步行时的压力反应模式。发现来自连续传感器数据的即时环境特征可用于识别与压力相关的事件。该实验在新加坡进行，新加坡是一个热带气候炎热的城邦，城市的气候条件鼓励城市规划者满足居民的步行需求，并确保短途步行。