voyeurism

the Life2Well Project to be shared during the 10th International Conference on Human Interaction & Emerging Technologies

we are very happy to share that we have been given the opportunity to share our work on Learning at the intersection of AI, physiology, EEG, our environment and well-being (the Life2Well Project) during the 10th International Conference on Human Interaction & Emerging Technologies in the track on Human-computer interaction. IHIET 2023 will be held in Nice from the 22nd till the 24th of August.

Designing for the investigation of microclimate stressors and physiological and neurological responses from the perspective of maker culture is co-authored with Nguyen Duc Minh Anh and Nguyen Thien Minh Tuan. its abstract reads:

The 2021 United Nations Climate Change Conference (COP26) resulted in the Glasgow Climate Pact. Initial work in the study reported in this paper investigated relationships between environment and physiological measurements using smartwatches, and self-designed bespoke environmental modules which are wearable around the waist. Data from this initial phase was analysed with a Random Forest regression model. The next phase of this project involves neurophysiological measurement, specifically electroencephalography (EEG). EEG was introduced to the model to explore how the changes in environmental or biometric measurements correlate with changes in neurophysiological measurements. In this latter phase, EEG data is viewed as an independent data type that is distinct from environmental and other physiological data. The headset model used to record EEG data is again a bespoke hand-made design, comprising a combination of biosensing board and electrodes from OpenBCI and widely available items like adhesive tapes and staples. A subsequent step involved validation of this DIY EEG headset data against research grade equipment, of which the analysis of different features of EEG data have shown to be of statistically comparable trends. For data collection, all data recorded is stored in Google Drive; Python is used to synchronize, pre-process data and train regression models. The first headset prototype was assembled in mid-October 2021, and was tested and developed in early November. From mid-November to late January 2022, the authors wore the devices for one to two hours per day to collect data. For EEG data, eight channels were recorded, basic filters (bandpass and notch) and REST re-referencing are applied. In this project, EEG time-series are used as input in regression models with other data types as output. Two regression models were trained then compared, the first being convolutional neural network with pre-built architecture and the other being a Random Forest model with features extracted from EEG time-series. Inferences are made from the models using open-source interpreters, with an eventual aim to infer how one’s local environment might impact one’s emotions and health. The results suggest that sound level, carbon dioxide concentration, and dust concentration feature more importantly in the regression models trained on collected datasets. These factors were continually associated with high feature importance scores in the EEG data signal and in both the objective scores recorded from the electronic instruments and the more subjective self-report forms. Furthermore, it was found that visual stimulus and problem processing, in terms of information, touch, and spatial relationships, are the most influential factors affecting the participants’ physiological well-being in this research. Most recently, one aspect that is currently being investigated is electrodermal activity (EDA). EDA is marker of sympathetic network activity (Zangróniz et al., 2017). As such, it is an indication of human stress and emotion arousal, (Rahma et al., 2022). It is hoped that analyses of EDA data will further strengthen the emerging model describing the intersections between local microclimate and physiological and neurological stress. Early validation experiments comparing DIY EDA devices against research-grade Empactica E4 sets have shown promising results.

the Life2Well Project as part of the UNESCO's first online interactive learning webinar, themed on ESD-Net 2030

my team and i are thrilled to share that - by God's grace - we have been afforded the rare opportunity to share our work on Learning at the intersection of AI, physiology, EEG, our environment and well-being (the Life2Well Project) as part of UNESCO's first online interactive learning webinar, for the benefit of its global network of education stakeholders: ESD-Net 2030. The webinar will be held on the 30th of March, and our work will be shared alongside parallel sessions facilitated by the Association for Learning Design and Sustainable Development (represented by Maria Toro-Troconis and Norita Ahmad), the OPEDUCA Project (represented by Jos Eussen), and the International Association of Universities (represented by Hilligje van't Land and Isabel Toman).

ESD-Net 2030 was set up in October 2022 to take the lessons learned from the Global Action Programme and subsequently facilitate the implementation of the Education for Sustainable Development (ESD) for 2030 framework and its roadmap. To this end, the webinar series is designed with a focus on sharing and strengthening pedagogical approaches and key competencies in ESD. The inaugural March webinar will be opened by the Chief of Section of ESD, Education Sector, UNESCO - Jun Morohashi - and facilitated by Mark Manns, Sarah Margono, and Takanori Kusaka.

for our particular project, we describe the scope of our session thus:

Have you wondered how we may nurture values among our students in support of sustainable development? What about how curricula may be designed to develop climate literacy among youth and the leaders of tomorrow? In this workshop, you will hear the voices of young people themselves, as they share four examples of how they have conceptualised, designed, and enacted citizen science investigations inspired by observations from their own respective local environments. From promoting appreciations of ecological biodiversity, to understanding sleep and concentration better, to exploring the relationships between microclimatic stressors and our physiological and neurological responses - the examples all have in common that they do not rely on expensive and fragile industrial-grade equipment which cannot be taken out of the lab. Be inspired by the stories of the students as they share their ups and downs through their respective investigative journeys, and their hopes as young makers for the future.

the Life2Well Project to be shared during the FOSS Asia summit

on the 15th of April my team and i are privileged to be given the opportunity to share Learning at the intersection of AI, physiology, EEG, our environment and well-being (The Life2Well Project) at the Free and Open Source Software (FOSS) Asia summit to be held at the Lifelong Learning Institute, Singapore.

we would like to thank the hardworking team from FOSS Asia for kindly extending the invitation to us :-)

Papers on AI and well-being to be read at AHFE 2023 :-)

we are very happy to share that we have been given the opportunity to read the following papers during the 14th international conference on Applied Human Factors and Ergonomics, in the track on Cognitive Computing and the Internet of Things. AHFE 2023 will be held in San Francisco from the 20th till the 24th of July.

Development of an Automated Microclimate Adjustment System based on Concentration Levels of Students is co-authored with Jasper Koh and Prasanna Thangaraja. its abstract reads:

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.

Exploring The Implementation of AI in a Cost-effective Device for Predicting Sleep Quality is co-authored with Jing Peng Lee and Bruce L Yu. its abstract reads:

This report presents the development and effectiveness of an Arduino-based sleep tracking device that can accurately measure various parameters of sleep, including movement, temperature, sound, light intensity, and humidity. The device was designed to be low-cost and easy to use, while not compromising on its ability to accurately measure sleep activity. The effectiveness of the device was evaluated by collecting data from test subjects and comparing it to the data collected by other sleep tracking devices. The collected data was then processed and used to train Artificial Intelligence (AI) models such as Backward Propagation Neural Network, Linear Regression Model, and Grey Relation Analysis, to predict the sleep quality rating from 0% to 100% and to identify the main cause of poor sleep. The results of the study demonstrated that the Arduino-based sleep tracking device is an effective and cost-efficient tool for measuring various parameters of sleep. However, the pressure sensor may sometimes result in inaccurate readings, which can be addressed through data cleaning and filtering. Furthermore, the use of AI models was able to predict the sleep quality rating and identify the main causes of poor sleep with high accuracy. Further research is needed to evaluate the device's performance over a longer period of time and in a larger sample of participants.

the Life2Well Project: invited to share during the NIE Innovations in Teaching and Learning seminar 2023

my team and i were thrilled to receive today an invitation to share our work on Learning at the intersection of AI, physiology, EEG, our environment and well-being (the Life2Well Project) during the Innovations in Teaching and Learning (iTL) seminar, organised by the Centre for Innovation in Learning, National Institute of Education.

the seminar will take place on the 3rd of March 2023. It provides opportunities for participants to collaborate and cross-pollinate ideas, learn about new pedagogical innovations, and share best practices across the teaching community.

iTL 2023 will be held in a so-called Design-a-thon format, facilitated by the Design Thinking Framework, enabling participants to come together and discuss problems in education collectively, which in turn, brings a variety of perspectives into the discussion. it allows participants to co-create innovative ideas that lead to enriching discussions where they can more deeply understand their learners.

concurrent with the Design-a-thon, there will be a Human Library segment, which will serve as an educational platform for participants to learn more about NIE projects related to teaching and learning. this segment allows participants to listen, relate, and ask questions about the projects shown in order to be inspired to come up with ideas for their design solutions to the design-a-thon problem statements. it is in this context that the Life2Well Project will be shared.

thanks be to God for so graciously granting us this opportunity to share and learn together.

The Life2Well Project: new student-initiated investigations under its aegis

for about a year, we have been sharing our work on Learning at the intersection of AI, physiology, EEG, our environment and well-being (The Life2Well Project). 

its mandate is deliberately framed broadly to encompass student-initiated inquiry in to their own health and well-being.

this year, we are excited and happy to share that students whom we mentor are currently working on the next suite of projects; these include:

- Implementing Artificial Intelligence in Sleep Analysis systems;

- An investigation on the effects of different types of fragrance on stress level of junior college students;

- Design and prototyping of an AI-based real-time detector for the calisthenic push up exercise;

- Investigation of the effects of microclimate on concentration levels; and

- Mitigating anxiety and stress: Investigating features of Lo-fi Music as attributed to its calming effects.

Disciplinary Intuitions: welcoming college students to NTU's Undergraduate Research Experience on Campus (URECA) AY 2022 / 2023

further to my post two months’ ago, my team and i are really happy and privileged to welcome four college students (from a total of twenty-three applicants) to our projects offered under NTU’s Undergraduate Research Experience on Campus (URECA) programme for the Academic Year 2022 / 2023.

 

three of our students are from the School of Electrical and Electronic Engineering, and the fourth is pursuing a Double Major from the School of Physical and Mathematical Sciences and the School of Computer Science and Engineering.

 

the opportunity to participate in URECA is extended through invitation only to select second- and third-year undergraduates, who will subsequently be conferred as NTU President Research Scholars.

 

the projects we have offered are:

 

NIE22349 – Investigating local micro-climate and thermal comfort with open-source

 

NIE22355 – Designing a neural network-based approach for generating custom feedback based on stakeholder specifications for novice coders

 

NIE22356 – Designing a tool to analyse and score the readability of source code with a view towards encouraging programmers to improve the readability of their code

 

NIE22357 – Training Natural Language Processing (NLP) models to parse and analyse Python code in order to predict logic errors with the aim of improving the process of debugging code

The Life2Well Project: Investigating the Relationship between Physiological Stress and Environmental Factors through Data Science, the Internet of Things and Do-it-Yourself Wearables

my team and i are very happy to share news of our second publication in the field of the Science of Learning :-) our chapter - The Life2Well Project: Investigating the Relationship between Physiological Stress and Environmental Factors through Data Science, the Internet of Things and Do-it-Yourself Wearables - is published in the book 'Citizen Science - Methods, Approaches and New Perspectives' edited by Alessio Vovlas.

since April 2021, we have been working hard on Learning at the intersection of AI, physiology, EEG, our environment and well-being (the Life2Well Project), in which we aim to afford learners opportunities to learn about their own learning, while they are learning, from a citizen science perspective.

the abstract of our chapter reads:

This chapter reports a study conducted by students as an independent research project under the mentorship of a Research Scientist at the National Institute of Education, Singapore. In the Life2Well Project (Learning at the intersection of AI, physiology, EEG, our environment and well-being) identical units of a wearable device containing environmental sensors (such as ambient temperature, air pressure, infrared radiation and relative humidity) were designed and worn respectively by five adolescents from July to December 2021. Over the same period, data from these sensors was complemented by that obtained from smartwatches (namely blood oxygen saturation, heart rate and its variability, body temperature, respiration rate and sleep score). More than 40,000 data points were eventually collected, and were processed through a random forest regression model, which is a supervised learning algorithm that uses ensemble learning methods for regression. Results showed that the most influential microclimatic factors on biometric indicators were noise, and the concentrations of carbon dioxide and dust. Subsequently, more complex inferences were made from Shapley value interpretation of the regression models. Such findings suggest implications for the design of living conditions with respect to the interaction of microclimate and human health and comfort.

the Life2Well Project at GeekcampSG

i am very happy and humbled to share that our team has been given an opportunity to share our work on Learning at the intersection of AI, physiology, EEG, our environment and well-being (the Life2Well Project) as part of the Headline Track at this year's GeekcampSG.

as the name suggests, GeekcampSG is a community tech conference. this year's event is taking place on the 29th of October.

my team and i are excited to have been given this opportunity, and we hope to see you at this year's event :-)

The Life2Well Project: Investigating the Effects of Microclimate on Physiological Stress and Brain Function with Data Science and Wearables

my team and i are very happy to share news of our very first publication in the field of the Science of Learning, with specific reference to neuroscience :-) our paper is published in a special issue of Sustainability, themed on 'Aspirations within Interdisciplinary STEM/STEAM Education under the Education for Sustainable Development (ESD)'.

since April 2021, we have been working hard on Learning at the intersection of AI, physiology, EEG, our environment and well-being (the Life2Well Project), in which we aim to afford learners opportunities to learn about their own learning, while they are learning, from a citizen science perspective.

an important part of our work is the validation of our maker-centric electroencephalographic headsets against industrial grade headsets. 

with the help of Dr Yuvaraj Rajamanickam and Dr Jack Fogarty of the Science of Learning in Education Centre at the National Institute of Education, we report our work in Volume 14 Issue 17 of the journal, as Investigating the Effects of Microclimate on Physiological Stress and Brain Function with Data Science and Wearables.

the abstract of our paper reads:

This paper reports a study conducted by students as an independent research project under the mentorship of a research scientist at the National Institute of Education, Singapore. The aim of the study was to explore the relationships between local environmental stressors and physiological responses from the perspective of citizen science. Starting from July 2021, data from EEG headsets were complemented by those obtained from smartwatches (namely heart rate and its variability and body temperature and stress score). Identical units of a wearable device containing environmental sensors (such as ambient temperature, air pressure, infrared radiation, and relative humidity) were designed and worn, respectively, by five adolescents for the same period. More than 100,000 data points of different types—neurological, physiological, and environmental—were eventually collected and were processed through a random forest regression model and deep learning models. The results showed that the most influential microclimatic factors on the biometric indicators were noise and the concentrations of carbon dioxide and dust. Subsequently, more complex inferences were made from the Shapley value interpretation of the regression models. Such findings suggest implications for the design of living conditions with respect to the interaction of the microclimate and human health and comfort.