Projects / Local Chapter Challenge

[French Chapter] Conversational AI Chatbot for the Elderly and Disabled Using NLP

Challenge Completed!

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This Omdena Local Chapter Challenge runs for 4 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world.

You will work on solving a local problem, initiated by the Omdena France Chapter and Omdena Paris, France Chapter

The problem

In this project, our goal is to create a virtual caregiver system that extracts the expression of mental and physical health states through dialogue-based human-computer interaction to support tailored treatment for elderly people and the disabled.

Natural language processing libraries like Natural Language Toolkit or Microsoft DialoGPT etc. make it possible to model a conversational AI chatbot capable of assisting with daily living activities, providing advice on managing complex care, providing emotional support, participating in decision-making, and communicating with healthcare providers.

The project goals

  • Data cleaning: The process of data understanding, removal of outliers, and missing data, and investigating the data flow for proper alignment with the set goals
  • Data Intent: The goal of intent can be to extract data from the user or process-acquired data. The data may be categorical data in variables like social, mental, and physical, which will be converted to formats suitable for NLP algorithms.
  • Data Normalization: Prioritized compatibility of data to units or arrangements in order to avoid problems during modeling. Due to the different types of data (e.g., continuous, discrete, and categorical) present in the dataset, it is essential to normalize the data to eliminate the influence of the dimension and avoid difficulties during the model development phase.
  • Contextualization: The goal of the personalization intent is to extract personal demographic data from the user to provide a personalized experience throughout the application. therefore, it uses a question-answer format to extract information about the user (such as name, age, height, weight, etc) and populate slots in the Personalisation template
  • Goal Setting: The Goal Setting intent extracts a user’s physical activity goals for the upcoming week. Following behavior change theory (mic ), the idea is to enable users to be conscious (e.g. voicing it) about the specific goals being set as a form of commitment to positive behavior change.
  • The Reporting Intent: is aimed at enabling self-reporting of activities for the purpose of self-management. Depending on the type of goal being set the conversational agent must initiate a contextually relevant dialogue with the intent of extracting activities the user has undertaken during the day. For this purpose, the data obtained from the Goal Setting template is retrieved to form the context of the conversation. For instance, if the user had indicated that she was playing golf on Thursday, then the agent would be able to ask how she got on with that activity on a Thursday.

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work

First Omdena Local Chapter Challenge?

Beginner-friendly, but also welcomes experts



Duration: 4 to 8 weeks

Your Benefits

Address a significant real-world problem with your skills

Build your project portfolio

Access paid projects (as an Omdena Top Talent)

Get hired at top organizations


Good English

Suitable for AI/ Data Science beginners but also more senior collaborators

Learning mindset

This challenge is hosted with our friends at

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