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Overview of MediaEval 2020:
Insights for Wellbeing -Multimodal
Personal Health Lifelog Data Analysis
Peijiang Zhao1, Minh-Son Dao1, Ngoc-Thanh Nguyen2,
Thanh-Binh Nguyen3, Duc-Tien Dang-Nguyen4, Cathal Gurrin5,
1National Institute of Information and Communications Technology, Japan
2University of Information Technology (VNUHCM-UIT), Vietnam
3 University of Science (VNUHCM-US), Vietnam
4University of Bergen, Norway
5Dublin City University, Ireland
NICT CONFIDENTIAL
2
Insights for Wellbeing -Multimodal
BACKGROUND
The association between people’s wellbeing and
the properties of the surrounding environment
is an essential area of investigation
In order to know about the surrounding
environment, A lot of IoT devices have been set
in many cities.
However, most of those research focused on the
general population but not personal scale.
On a personal scale, local information about air
pollution, weather, urban nature are very
important for personal health
It is not always possible to gather plentiful
amounts of personal scale environment data
BACKGROUND
Personal Environment Data
3
NICT CONFIDENTIAL
Decoder Transfer Learning for Predicting Personal Exposure to Air Pollution
• The impact of the environment on
the scale of individual people.
• Using wearable or moveable
environmental data collection
devices
• Can be used to customize personal
health plans
• Which road has better air and
good for health
• Cannot be obtained at all times
• Difficult to collect in large quantities
Example of personal air quality collection in Fukuoka,
Japan,
NICT CONFIDENTIAL
4
Insights for Wellbeing -Multimodal
BACKGROUND
In this workshop, we want to find the answer to this question:
“Does the personal air quality be predicted by using other data
that is easy to obtain?”
predict
public/open data
lifelog data
personal air quality
NICT CONFIDENTIAL
5
Insights for Wellbeing -Multimodal
TASK DESCRIPTION
“Does the personal air quality be predicted by using other data that is easy to obtain?”
Personal Air Quality Prediction
with public/open data
Personal Air Quality
Prediction with lifelog data.
TASK 1 TASK 2
Prediction Target
The value of personal air pollution
data (PM2.5, O3,and NO2)
Input Other Data
weather data (wind speed, wind
direction, temperature, humidity) and
air pollution data (PM2.5, O3, and NO2)
from public/open data sources
Prediction Target
The value of personal Air Quality
Input Other Data
Lifelog images data
Public/open data
Whether we can use public/open data to
predict personal air pollution data.
Whether we can use only lifelog plus
some data from open sources to predict
the personal air pollution data.
NICT CONFIDENTIAL
6
Insights for Wellbeing -Multimodal
DATA DESCRIPTION
Personal Air Quality Dataset along the Tokyo 2021 Olympics Marathon Course
(PAQD)
• Five data collection participants assigned to five
routes to collect the data via wearable sensors.
• Collected from March to April 2019.
• Routes 1–4 were along the marathon course for the
Tokyo 2020 Olympics. Route 5 was the running
course around the Imperial Palace. The length of
each route was 5 km.
• Each participant started data collection at 9 am
every weekday, and it took 1 hours to walk each
route.
• Include the temperature and humidity, O3, PM2.5,
and NO2, GPS , and lifelog image.
• For this dataset, the personal air quality is O3, PM2.5,
and NO2 on each route.
• 6 sensors collect the data by walkers
• 2 sensors collect the data by cars
NICT CONFIDENTIAL
7
Insights for Wellbeing -Multimodal
DATA DESCRIPTION
Global air pollution dataset in Tokyo (GAPD).
-26 monitoring stations
-11 Air pollutant
SO2,NO,NO2,Nox,CO,Ox,NMHC,
CH4,THC,PM10,PM2.5
-4 weather data
wind speed, wind direction,
temperature, humidity
NICT CONFIDENTIAL
8
Insights for Wellbeing -Multimodal
GROUND TRUTH AND EVALUATION
Evaluation Metrics
Symmetric Mean Absolute Percentage ErrorRoot Mean Squared Error Mean Absolute Error
Personal Air Quality Prediction
with public/open data
Personal Air Quality
Prediction with lifelog data.
TASK 1 TASK 2
The value of personal air pollution
data (PM2.5, O3,and NO2)
NICT CONFIDENTIAL
9
Insights for Wellbeing -Multimodal
REGISTERED TEAM
We have 11 registered teams
UEHB-ML nainasaid@uetpeshawar.edu.pk, (Pakistan)
NREI ngadtt@gmail.com, (Vietnam)
SCI-UTB P20190005@student.utb.edu.bn, (Brunei)
AISIA dat181197@gmail.com, (Vietnam)
BPGC f20180443@goa.bits-pilani.ac.in, (India)
QHL-UIT quannt.13@grad.uit.edu.vn, (Vietnam)
HCMUS tmtriet@fit.hcmus.edu.vn, (Vietnam)
CLCRO dumitru.cercel@upb.ro, (Romania)
MLRG jaisakthi.murugaiyan@vit.ac.in, (India)
MMSys 277945743@qq.com, (China)
pnu_ccis amel.ksibi@gmail.com (KSA)
At last, 4 submissions
NREI (University of Resources and
Environment, Vietnam)
AISIA (Vietnam National University in
HCM city, University of Science,
Vietnam)
QHL-UIT (Vietnam National University
in HCM city, University of
Information Technology, Vietnam)
PNU_CCIS (College of Computer and
Information systems, PNU , KSA)
NICT CONFIDENTIAL
10
Insights for Wellbeing -Multimodal
RESULTS
TASK 1
Personal Air Quality Prediction with
public/open data
Team
PM25
MAE
PM25
RMSE
PM25
SMAPE
NO2
MAE
NO2
RMSE
NO2
SMAPE
O3
MAE
O3
RMSE
O3
SMAPE
Score
AISIA 6.42 8.34 0.60 14.53 17.10 0.46 13.53 16.50 0.68 6
QHL UIT 3.70 5.45 0.41 15.29 18.04 0.54 16.58 20.75 0.69 3
NREI 13.20 16.75 0.80 15.09 18.28 0.47 14.09 17.02 0.67 0
pnu_ccis 30.24 36.74 1.59 21.60 25.74 0.86 14.33 18.06 0.81 0
BEST TEAM AISIA
NICT CONFIDENTIAL
11
Insights for Wellbeing -Multimodal
RESULTS
TASK 2
Personal Air Quality Prediction
with
lifelog data.
PM2.5 O3 NO2
AISIA-run1
MAE 3.491 7.186 15.692
RSME 3.756 8.684 17.173
SMAPE 0.149 0.567 0.567
AISIA-run2
MAE 4.574 7.698 15.817
RSME 5.425 8.555 17.994
SMAPE 0.202 0.595 0.575
QHL-run2
MAE 5.513 9.392 22.420
RSME 5.747 10.338 24.068
SMAPE 0.235 0.719 0.689
QHL-run1
MAE 5.759 16.059 28.629
RSME 5.986 17.263 31.155
SMAPE 0.275 1.987 1.995
BEST TEAM AISIA
NICT CONFIDENTIAL
12
Insights for Wellbeing -Multimodal
DISCUSSION
For Task 1
Personal Air Quality Prediction with public/open data
We also prepared a baseline mode Decode Transfer Learning
network
P. J Zhao and K Zettsu. 2019. Decoder Transfer Learning for Predicting Personal Exposure
to Air Pollution. In 2019 IEEE International Conference on Big Data. 5620–5629
Team
PM25
MAE
PM25
RMSE
PM25
SMAPE
NO2
MAE
NO2
RMSE
NO2
SMAPE
O3
MAE
O3
RMSE
O3
SMAPE
AISAI 6.42 8.34 0.60 14.53 17.10 0.46 13.53 16.50 0.68
QHL UIT 3.70 5.45 0.41 15.29 18.04 0.54 16.58 20.75 0.69
NREI 13.20 16.75 0.80 15.09 18.28 0.47 14.09 17.02 0.67
pnu_ccis 30.24 36.74 1.59 21.60 25.74 0.86 14.33 18.06 0.81
Baseline DTL 4.52 6.43 0.50 13.46 16.89 0.52 13.02 16.34 0.71
NICT CONFIDENTIAL
13
Thank you
Decoder Transfer Learning for Predicting Personal Exposure to Air Pollution
DISCUSSION
14
NICT CONFIDENTIAL
The distribution of dataset
Example of the distribution of the GAQD in Tokyo (PM2.5,NO2,O3) and the target
dataset PAQD
Insights for Wellbeing -Multimodal

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Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health Lifelog Data Analysis

  • 1. Overview of MediaEval 2020: Insights for Wellbeing -Multimodal Personal Health Lifelog Data Analysis Peijiang Zhao1, Minh-Son Dao1, Ngoc-Thanh Nguyen2, Thanh-Binh Nguyen3, Duc-Tien Dang-Nguyen4, Cathal Gurrin5, 1National Institute of Information and Communications Technology, Japan 2University of Information Technology (VNUHCM-UIT), Vietnam 3 University of Science (VNUHCM-US), Vietnam 4University of Bergen, Norway 5Dublin City University, Ireland
  • 2. NICT CONFIDENTIAL 2 Insights for Wellbeing -Multimodal BACKGROUND The association between people’s wellbeing and the properties of the surrounding environment is an essential area of investigation In order to know about the surrounding environment, A lot of IoT devices have been set in many cities. However, most of those research focused on the general population but not personal scale. On a personal scale, local information about air pollution, weather, urban nature are very important for personal health It is not always possible to gather plentiful amounts of personal scale environment data
  • 3. BACKGROUND Personal Environment Data 3 NICT CONFIDENTIAL Decoder Transfer Learning for Predicting Personal Exposure to Air Pollution • The impact of the environment on the scale of individual people. • Using wearable or moveable environmental data collection devices • Can be used to customize personal health plans • Which road has better air and good for health • Cannot be obtained at all times • Difficult to collect in large quantities Example of personal air quality collection in Fukuoka, Japan,
  • 4. NICT CONFIDENTIAL 4 Insights for Wellbeing -Multimodal BACKGROUND In this workshop, we want to find the answer to this question: “Does the personal air quality be predicted by using other data that is easy to obtain?” predict public/open data lifelog data personal air quality
  • 5. NICT CONFIDENTIAL 5 Insights for Wellbeing -Multimodal TASK DESCRIPTION “Does the personal air quality be predicted by using other data that is easy to obtain?” Personal Air Quality Prediction with public/open data Personal Air Quality Prediction with lifelog data. TASK 1 TASK 2 Prediction Target The value of personal air pollution data (PM2.5, O3,and NO2) Input Other Data weather data (wind speed, wind direction, temperature, humidity) and air pollution data (PM2.5, O3, and NO2) from public/open data sources Prediction Target The value of personal Air Quality Input Other Data Lifelog images data Public/open data Whether we can use public/open data to predict personal air pollution data. Whether we can use only lifelog plus some data from open sources to predict the personal air pollution data.
  • 6. NICT CONFIDENTIAL 6 Insights for Wellbeing -Multimodal DATA DESCRIPTION Personal Air Quality Dataset along the Tokyo 2021 Olympics Marathon Course (PAQD) • Five data collection participants assigned to five routes to collect the data via wearable sensors. • Collected from March to April 2019. • Routes 1–4 were along the marathon course for the Tokyo 2020 Olympics. Route 5 was the running course around the Imperial Palace. The length of each route was 5 km. • Each participant started data collection at 9 am every weekday, and it took 1 hours to walk each route. • Include the temperature and humidity, O3, PM2.5, and NO2, GPS , and lifelog image. • For this dataset, the personal air quality is O3, PM2.5, and NO2 on each route. • 6 sensors collect the data by walkers • 2 sensors collect the data by cars
  • 7. NICT CONFIDENTIAL 7 Insights for Wellbeing -Multimodal DATA DESCRIPTION Global air pollution dataset in Tokyo (GAPD). -26 monitoring stations -11 Air pollutant SO2,NO,NO2,Nox,CO,Ox,NMHC, CH4,THC,PM10,PM2.5 -4 weather data wind speed, wind direction, temperature, humidity
  • 8. NICT CONFIDENTIAL 8 Insights for Wellbeing -Multimodal GROUND TRUTH AND EVALUATION Evaluation Metrics Symmetric Mean Absolute Percentage ErrorRoot Mean Squared Error Mean Absolute Error Personal Air Quality Prediction with public/open data Personal Air Quality Prediction with lifelog data. TASK 1 TASK 2 The value of personal air pollution data (PM2.5, O3,and NO2)
  • 9. NICT CONFIDENTIAL 9 Insights for Wellbeing -Multimodal REGISTERED TEAM We have 11 registered teams UEHB-ML nainasaid@uetpeshawar.edu.pk, (Pakistan) NREI ngadtt@gmail.com, (Vietnam) SCI-UTB P20190005@student.utb.edu.bn, (Brunei) AISIA dat181197@gmail.com, (Vietnam) BPGC f20180443@goa.bits-pilani.ac.in, (India) QHL-UIT quannt.13@grad.uit.edu.vn, (Vietnam) HCMUS tmtriet@fit.hcmus.edu.vn, (Vietnam) CLCRO dumitru.cercel@upb.ro, (Romania) MLRG jaisakthi.murugaiyan@vit.ac.in, (India) MMSys 277945743@qq.com, (China) pnu_ccis amel.ksibi@gmail.com (KSA) At last, 4 submissions NREI (University of Resources and Environment, Vietnam) AISIA (Vietnam National University in HCM city, University of Science, Vietnam) QHL-UIT (Vietnam National University in HCM city, University of Information Technology, Vietnam) PNU_CCIS (College of Computer and Information systems, PNU , KSA)
  • 10. NICT CONFIDENTIAL 10 Insights for Wellbeing -Multimodal RESULTS TASK 1 Personal Air Quality Prediction with public/open data Team PM25 MAE PM25 RMSE PM25 SMAPE NO2 MAE NO2 RMSE NO2 SMAPE O3 MAE O3 RMSE O3 SMAPE Score AISIA 6.42 8.34 0.60 14.53 17.10 0.46 13.53 16.50 0.68 6 QHL UIT 3.70 5.45 0.41 15.29 18.04 0.54 16.58 20.75 0.69 3 NREI 13.20 16.75 0.80 15.09 18.28 0.47 14.09 17.02 0.67 0 pnu_ccis 30.24 36.74 1.59 21.60 25.74 0.86 14.33 18.06 0.81 0 BEST TEAM AISIA
  • 11. NICT CONFIDENTIAL 11 Insights for Wellbeing -Multimodal RESULTS TASK 2 Personal Air Quality Prediction with lifelog data. PM2.5 O3 NO2 AISIA-run1 MAE 3.491 7.186 15.692 RSME 3.756 8.684 17.173 SMAPE 0.149 0.567 0.567 AISIA-run2 MAE 4.574 7.698 15.817 RSME 5.425 8.555 17.994 SMAPE 0.202 0.595 0.575 QHL-run2 MAE 5.513 9.392 22.420 RSME 5.747 10.338 24.068 SMAPE 0.235 0.719 0.689 QHL-run1 MAE 5.759 16.059 28.629 RSME 5.986 17.263 31.155 SMAPE 0.275 1.987 1.995 BEST TEAM AISIA
  • 12. NICT CONFIDENTIAL 12 Insights for Wellbeing -Multimodal DISCUSSION For Task 1 Personal Air Quality Prediction with public/open data We also prepared a baseline mode Decode Transfer Learning network P. J Zhao and K Zettsu. 2019. Decoder Transfer Learning for Predicting Personal Exposure to Air Pollution. In 2019 IEEE International Conference on Big Data. 5620–5629 Team PM25 MAE PM25 RMSE PM25 SMAPE NO2 MAE NO2 RMSE NO2 SMAPE O3 MAE O3 RMSE O3 SMAPE AISAI 6.42 8.34 0.60 14.53 17.10 0.46 13.53 16.50 0.68 QHL UIT 3.70 5.45 0.41 15.29 18.04 0.54 16.58 20.75 0.69 NREI 13.20 16.75 0.80 15.09 18.28 0.47 14.09 17.02 0.67 pnu_ccis 30.24 36.74 1.59 21.60 25.74 0.86 14.33 18.06 0.81 Baseline DTL 4.52 6.43 0.50 13.46 16.89 0.52 13.02 16.34 0.71
  • 13. NICT CONFIDENTIAL 13 Thank you Decoder Transfer Learning for Predicting Personal Exposure to Air Pollution
  • 14. DISCUSSION 14 NICT CONFIDENTIAL The distribution of dataset Example of the distribution of the GAQD in Tokyo (PM2.5,NO2,O3) and the target dataset PAQD Insights for Wellbeing -Multimodal

Editor's Notes

  • #2: ladies and gentlemen thank you for coming today My name is peijiang zhao, I’m a research of National Institute of Information and Communication Technology ,japan