Development of an Automated Microclimate Adjustment System based on Concentration Levels of Students

Jasper Koh, Prasanna Thangaraja, Kenneth Y T Lim
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Abstract

The microclimate of a classroom can significantly impact the students' concentration. As students ourselves, we have noticed this. For the same subject, we are concentrated in one period, then in another, we lose our attention very easily. This prompted us to investigate the relationship between students' concentration levels in relation to the microclimate. Through machine learning, specifically through facial recognition and computer vision, we aimed to investigate the students' concentration levels based on the number of blinks per minute. While researching ways to analyse concentration levels, we found multiple studies which found a correlation between blink frequency and concentration level. We found that when people are concentrated, they tend to blink less and vice versa. The relationship between microclimate climate and concentration was analysed by measuring the blinks per minute while changing the microclimate at the same time. The microclimate conditions were varied using an air conditioner where the temperature set varied from 19°C to 31°C. The microclimate was measured using the NodeMCU microcontroller board paired with SCD-30 and HM-3301 sensors from Grove. This allowed us to gather data such as temperature, relative humidity, carbon dioxide concentration, PM2.5 and PM10 readings. From this, it was found that the most significant microclimate condition that affects concentration levels is carbon dioxide concentration. As the concentration of carbon dioxide increases, the concentration of the participants decreases. The observed trend is supported by various studies as well. Simultaneously, as the microclimate conditions were being varied, we sent out a survey to find the thermal comfort level of the students, allowing us to gauge how they felt according to the environment. Thermal comfort is when a person feels comfortable with the thermal environment. Participants were tasked to do their work for a fixed duration while a sensor recorded the temperature and relative humidity of the place. For every hour, the participants were required to rank their thermal comfort by using the ASHRAE scale. This survey identified the comfort zone at an upper limit temperature of 28.9°C and relative humidity of 68.0% and a lower limit temperature of 26.2°C and relative humidity of 83.1%. With these findings, we created an automated system to alter microclimate conditions. The place will be a conducive environment for the students based on their concentration and the environmental data obtained from the sensors. The alteration of microclimate conditions was done by controlling the air conditioner using an infrared LED module connected to the NodeMCU that sent out the infrared codes according to the conditions of the room, allowing us to adjust the microclimate efficiently while fully utilising the various modes of the air conditioning system to save energy.
基于学生集中程度的自动小气候调节系统的开发
教室的小气候对学生的注意力有显著影响。作为学生,我们也注意到了这一点。对于同一门学科,我们在一个时期集中注意力,然后在另一个时期,我们很容易失去注意力。这促使我们研究学生注意力集中水平与小气候之间的关系。通过机器学习,特别是面部识别和计算机视觉,我们的目标是根据每分钟眨眼的次数来调查学生的集中程度。在研究分析注意力集中水平的方法时,我们发现了多项研究,发现眨眼频率与注意力集中水平之间存在相关性。我们发现,当人们注意力集中时,他们眨眼的次数往往更少,反之亦然。通过测量微气候变化的同时每分钟眨眼次数,分析了微气候与浓度的关系。使用空调改变小气候条件,温度设置在19°C到31°C之间。微气候测量使用NodeMCU微控制器板与Grove的SCD-30和ham -3301传感器配对。这使我们能够收集温度、相对湿度、二氧化碳浓度、PM2.5和PM10读数等数据。由此发现,影响浓度水平最显著的小气候条件是二氧化碳浓度。随着二氧化碳浓度的增加,参与者的浓度降低。观察到的趋势也得到了各种研究的支持。同时,随着小气候条件的变化,我们进行了一项调查,以找到学生的热舒适水平,使我们能够根据环境来衡量他们的感受。热舒适是指一个人在热环境中感到舒适。参与者的任务是在固定的时间内完成他们的工作,同时传感器记录下这个地方的温度和相对湿度。每小时,参与者被要求使用ASHRAE量表对他们的热舒适进行排名。本次调查确定的舒适区上限温度为28.9°C,相对湿度为68.0%;下限温度为26.2°C,相对湿度为83.1%。根据这些发现,我们创建了一个自动化系统来改变小气候条件。根据学生的注意力和从传感器获得的环境数据,这个地方将是一个有利于学生的环境。微气候条件的改变是通过使用一个与NodeMCU相连的红外LED模块来控制空调,该模块根据房间的情况发出红外编码,使我们能够有效地调节微气候,同时充分利用空调系统的各种模式来节省能源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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