DATA2600 Assistive and welfare technologies Course description

Assistive technologies - AT - are equipment, software or device used to increase, maintain or improve functional abilities for people with disabilities. Such equipment can be helpful to improve ability to take part in social activities, studies and working life, and can provide greater independence and control over one’s own everyday life.

Welfare technology is a common term for all technical installations and solutions that can improve the individual’s ability to get by in their own home, and contribute to ensuring quality of life and general well-being. Welfare technology can provide better services for elderly people living at home, patients in nursing homes, in the field of intoxicants and mental health, and people with disabilities. Welfare technology can also provide more efficient use of resources in the health and care services.

In this course you will learn how to plan, design, evaluate and test solutions within assistive and welfare technologies.

None.

Ingen ut over opptakskrav.

On successful completion of this module students should be able to:

Knowledge:

• Demonstrate an understanding of the most important assistive technologies and welfare technologies being used today, and have a thorough understanding of how they work.
• Command a thorough understanding of the sensory, physical and cognitive functional disabilities and its consequences for social activity.
• Be able to distinguish the human and technological prerequisites needed for technical systems to function for users with disabilities.
• Critically assess and discuss the assistive and welfare technologies in a health and societal context.

Skills:

• Be able to address the user’s different needs, and to propose and implement technological facilitation.
• Demonstrate an ability to evaluate and consider user interface in relation to the needs of a person.

General competence:

• Reflect on how technology can assist people with and without disabilities
• Understand, communicate and implement solutions for different user needs
• Reflect on ethical dilemmas related to human interaction, technology and social participation

Lectures and tutorials. The student works individually and in groups (two to four students).

None.

Portfolio assessment with the following portfolio requirements:

• A group project (2-4 students, written report of approx. 20 pages)
• An individual project (written project of approx. 20 pages)

For the portfolio assessment, a comprehensive assessment is given with one final grade. Exam results can be appealed. In the case of a postponed examination, a different form of examination may also be proposed and used or a new assignment with a new deadline will be given. If an oral examination is used, its final grade cannot be appealed.

We are witnessing the era of Big Data where data is generated, collected, and processed at an unprecedented scale and data-driven decisions influence many aspects of modern life.

Data mining is the process of discovering patterns in large data sets involving methods in statistics and database systems. A large number of applications such IoT sensors generate large amounts of data streams. The necessity of data stream mining and learning from the data is increasingly becoming more prevalent and urgent.

Extracting knowledge from data sets requires not only computational power but also programming abstractions as well as analytical skills. In this course, the students will be exposed to the different approaches for data mining and stream processing such as association rules learning, anomaly detection, data clustering, visualizations, and extracting statistical features on the fly from large data streams. The students will be exposed to concrete data mining and neural network architectures including deep learning models for handling large data streams such as convolutional neural networks, recurrent neural networks, autoencoders, transformers and attentions. In this course, the student will also be exposed to different data mining systems, working end-to-end pipelines including performance evaluation, detecting overfitting, underfitting, and data defects. With a focus on data mining applications, we will study some powerful numerical linear algebra methods.

The student should have the following outcomes upon completing the course:

Knowledge

Upon successful completion of the course, the student:

• has a deep understanding of how data mining can be used to extract knowledge from data sets.
• has advanced knowledge of the different data mining algorithms

Skills

Upon successful completion of the course, the student:

• can design and implement data mining algorithms
• can deploy different data mining systems and configure them
• can utilize a specialized library for data mining

General competence

Upon successful completion of the course, the student:

• can use data mining systems to mine data
• can analyse data mining solutions with regard to robustness and in relation to his/her intended tasks
• can explain how data mining can be used in different applications areas such as business analytics

This course is divided into two parts. The first part with focus on covering the principles of data mining and stream processing. Different seminars will be given on the different methodological aspects of data mining and stream processing as well as the programming paradigms and software tools that enable them.

The second part will focus on the students completing a programming project. The project can be chosen from a portfolio of available problems. The student will work in a group on the project and submit a final code-base with a report.

During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.

Practical training

Lab sessions.