Omdena Chapter Page: Japan

Omdena Japan Chapter - Omdena Chapters

Welcome to the Japan Local Chapter!

Upcoming Projects

Applications close: 25th August 2022
Omdena Japan Chapter – Monitoring Wellbeing of Elderly People and Providing Support with IoT

 

The Background 

Japan, like other developed countries, has a large population of elderly people living on their own. They do not always have relatives nearby or access to community care. Because of mobility problems and also events like heatwaves and COVID, they are reluctant to leave their homes, causing isolation and putting them at risk of health emergencies. 

The Problem

Elderly people who live on their own are at risk of suffering medical emergencies. Being on their own, their relatives or the health emergency services may not be alerted on time, increasing the risk. Inclement weather (e.g. heatwaves) increase the risk even further.

 

Using IoT (e.g. home energy consumption data) will help identity signs of inactivity, which, combined with other data (e.g type of housing) will allow designing an alert system for elderly people who may be in need of urgent attention.

 

The Project Goals

– Find reliable source of IoT streaming data (home electricity use, water use)

– Determine a model that would work on this domain

– Build a model for individual health emergency risk that combines IoT with other data (e.g. type of housing, area indicators)

– Build an interactive app that outputs alert levels for addresses with inactivity

The Learning Outcomes

  • Learn how to:

    • – Source and work with IoT data
    • – Structure data and conduct exploratory data analysis
    • – Build a scoring model for emergency health risk
    • – Build interactive dashboards (Streamlit)

 

The Tasks & Timeline

Week 1 Week 2 Week 3 Week 4

– Domain Research

– IoT data and other data collection

– Tutorials

Exploratory Data Analysis (EDA)

– IoT data structuring 

– Risk model building

Risk model building

– Tutorial (Streamlit)

– Dashboard building

Integrating work

– Deployment dashboard app

Completed Projects

Finding Paths to Safety Following Natural Disasters with Satellite Imaging and AI – November 2021

Japan Chapter

Project Starts: November 25th

Duration: 4 weeks

All Data Science Skills Welcome

 

 

Finding Paths to Safety Following Natural Disasters with Satellite Imaging and AI

 

 

 

The Background 

Natural Disasters are problems in Japan, with risk of earthquakes, floods and tsunamis. Japan has well-developed disaster response systems, but densely populated cities and narrow roads make managing the response difficult. By giving individuals information about the safest ways from their homes and places of work, it will increase their awareness of the surrounding area and improve their preparedness.

The Problem

 Design a model collecting data about the local roads from satellite images, classify them and indicate the safest route to be taken from point A to point B. Design an interactive dashboard to display the safest route in a map.

By making individuals aware, it will improve their preparedness and it can be used within families to prepare disaster response plans, depending on their circumstances. To be used by individuals, families and groups, and foreign residents who may not understand local information. Further development will be covering more geographical areas and publicising on a local level.

 

The Project Goals

  • – collect satellite images and identify road characteristics 

    – build a model for scoring the roads in terms of their suitability for use in emergency

    – build a pathfinding model from A to B, combining it with road characteristics 

    – suggest safest path from A to B

    – publish interactive dashboards to display road characteristics and safest paths

    – arrange demonstration and publicise to local audiences

The Learning Outcomes

  • Learn how to:

    – Extract, process and classify satellite images

  • Work with OpenStreetMap
  • – Build a scoring model
  • – Apply pathfinding models
  • – Build interactive dashboards (Streamlit)

 

The Tasks & Timeline

Week 1 Week 2 Week 3 Week 4

 – Tutorials (OSM, Satellite Images)

– Satellite Image collection

– Image processing

– Exploratory Data Analysis(EDA)

– Road Scoring Model

– Tutorial (Streamlit)

– Dashboard building

– Road Scoring Model

– Pathfinding algorithms

– Dashboard building

– Integrating work

– Deploy dashboard app

Omdena Japan Chapter
Japan Chapter Lead
Galina Naydenova

Galina Naydenova

Data Scientist

Galina is a Data Scientist with years of experience in the technology and education sectors.

Her interests are Predictive Modelling, Learning Analytics, NLP and Citizen Science.

She is a contributor to academic publications in the area of Learning Analytics and is a Fellow of the Higher Education Academy in the UK.