voyeurism

commemorating the twentieth anniversary of the Centre for Research in Pedagogy and Practice

i am very happy to share that a commemorative book celebrating the twentieth anniversary of the Centre for Research in Pedagogy and Practice has just been made available for download :-)

do feel free to browse Two decades and beyond: growing education research in Singapore :-)

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

since 2021, we have been sharing our work in neuroergonomics 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:

- Integration of Deep Learning in digital Clock Drawing Tests onto consumer platforms for enhanced detection of mild cognitive impairment;
- Machine Learning implementation in robotics for emotional support companions;
- Investigating the relationship between microclimate factors and human exercise performance;
- Investigating the effect of microclimate on human's physiological health using Electrodermal activity; and
- Optimizing Learning Environments: A Microclimate Study of a School Campus in Singapore using the Integrated Environment Modeller Simulation Tool (IEMsim).

the Life2Well Project to be shared with visiting doctoral students from the National Tsing Hua University

my students and i are very happy to be presented with the opportunity to share our work in neuroergonomics on Learning at the intersection of AI, physiology, EEG, our environment and well-being (the Life2Well Project) to a small team of visiting doctoral students in cognitive neuroscience from the Department of Education and Learning Technology, National Tsing Hua University, on the 16th of August.

we thank God for this opportunity. :-)

students' sharing to members of the Learning Sciences & Innovation group

today my students Jing Peng Lee and Bruce Yu were given the opportunity to share their work on their development of a maker-centric hardware prototype and deep learning model sleep analysis system to members of the Learning Sciences & Innovation group at the Centre for Research in Pedagogy and Practice, National Institute of Education, Singapore.

Bruce also took the opportunity to share the inspiring story behind his experiences in ESpero: founding a youth-led nonprofit in the 21st century.

two papers to be read during the International Conference on Health and Social Care Information Systems and Technologies

i am thrilled to share that we have been given the opportunity to share our work through two papers to be read at this year's International Conference on Health and Social Care Information Systems and Technologies (HCist) :-)

this year's conference will take place as a hybrid event in Porto, from the 8th till the 10th of November.

An investigation on the effects of different types of odours on stress level of high school students when studying is co-authored with Wu Xinyue. its abstract reads:

In view of the mental health issues among adolescents in Singapore, aromatherapy is proposed to mitigate their stress level when studying. An experiment was conducted both in the classroom and home setting, to test the effectiveness of the give odours, lavender, rosemary, ylang-ylang, lemon and bergamot on reducing stress level, using the no odour scenario as control. Objective data (SpO2, heart rate and stress score) is collected using the Huawei Band 6 smartwatch. Subjective data was self-reported by the participants through Google forms, rating their emotional health on a 10-point scale and elaborating in prose. It was found that the anti-anxiety effects of the stimulants (lemon, bergamot, rosemary) were much larger than that of the sedatives (lavender, ylang-ylang). In particular, lemon showed the best objective anxiolytic effect, while bergamot was the best in terms of self-perceived effect. Rosemary relieved stress through raising productivity, but some effects of overworking were observed. On the other hand, ylang-ylang showed inconsistent effects, while lavender was not suitable to relieve stress when studying.

Designing and prototyping of AI-based real-time mobile detectors for calisthenic push-up exercise is co-authored with Zhang Xiyuan and Shawn Han. its abstract reads:

Fitness exercises, including push-ups, are very beneficial to personal health. Many Artificial Intelligence (AI)-based fitness trainers are developed based on human pose estimation models or assisted by Internet of Things (IoT) devices. However, many of them require access to a graphing processing unit (GPU) for model training or IoT sensors to deploy, less accessible for individuals. In our work, we designed and prototyped real-time mobile push-up detectors using three distinctive approaches: (1) Push-up pose classification, (2) Angle-heuristic estimation and (3) Optical flow detection. We trained our deep-learning model with over 2000 images to achieve a high accuracy for real time deployment. Models are tested on our video dataset applied data augmentation techniques to simulate real-world environmental conditions to evaluate model performance based on accuracy metrics (precision, recall, F1 score) and processing frame rate (FPS). From the results, we concluded that the angle-heuristic estimation method has the best overall performance and we analysed the reasons for the relatively poorer performance of the push-up pose classification and optical flow detection methods. All methods developed are capable of working on mobile devices without the need of GPU or IoT sensors.

student projects on biodiversity education and sleep quality at the 13th annual symposium of the Mobiles for Education Alliance

today our team received news that we have been invited to share our students' work on biodiversity education and the investigation of sleep quality during the 13th annual symposium of the Mobiles for Education Alliance in the track on Climate Education. This track focuses on the intersection of STEM education and climate change to enhance students' understanding of climate science, sustainable practices, and the role of technology in addressing environmental challenges.

we thank God for His continued grace and provision.

this year, the symposium is being conducted as an in-person event between the 11th to the 14th of September, with the overarching theme of Inspiring the next generation of STEM leaders - everywhere! 

other teams having representations are from UNESCO, the World Bank, the UNHCR, among others.

The mEducation Alliance was formed by USAID in 2010. It is an international collaborative effort between bilateral and multilateral donors, NGOs, foundations, private sector partners, and academic researchers. The collective agenda of the alliance is to: highlight and catalyze promising information and communications technology for education (ICT4E) practices and initiatives, particularly in low-resource and developing country context; to reduce duplicative efforts; to promote collective knowledge-sharing, and to identify and support efforts to evaluate and scale promising ICT4E interventions.

:-)

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; our paper is scheduled for the 23rd.

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, of the registered participants, 21 % were from African nations, 20 % were from Central and South Asia, 16 % were from the UK and Europe, 10 % were from East Asia, 10 % were from the Middle East, 9 % were from Central and South America, 9 % were from North America, and the remainder from Southeast Asia.

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, with our papers being read on 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.