Introduction to AI-Enabled Wearable Technologies

10 – 21 August 2026:  Lecture weeks (on-site at Aalto University campus)

• Daily lectures in the mornings between 09 and 12 (Monday-Friday)
• Group project work and workshops

Alongside the lectures, students have independent work, either in groups or individually. Students can coordinate with their teammates to complete these assignments outside of teaching hours.

The total workload of the course includes both contact lectures and the estimated amount of independent work. Some of the courses also have pre- or post-assignments included in the independent working hours.

-> Find the total course workload here

After completing the course, students will be able to:

Knowledge (Knowing) 

• Explain the fundamental concepts of wearable systems, including common sensing modalities (e.g., PPG, IMU, temperature), embedded hardware, and low-power wireless communication.
• Describe the basic principles of bio signal characteristics, noise sources, and signal processing methods used in wearable applications.
• Recognise the role of data-driven and AI-based methods in transforming raw wearable sensor data into meaningful health- and activity-related insights. 

Skills (Acting) 

• Acquire, visualize, and log multimodal sensor data using an open-source wearable platform and basic embedded software tools.
• Apply introductory signal processing techniques to wearable data in order to extract simple features in the time and frequency domains.
• Implement and evaluate basic machine learning or neural network models to analyze wearable data for tasks such as activity classification or physiological estimation.
• Work collaboratively in a small team to define a simple research or application question, design an analysis pipeline, and communicate results clearly through a project presentation. 

Attitudes and Professional Identity (Being) 

• Demonstrate a problem-oriented and exploratory mindset when working with real-world wearable data, acknowledging limitations, noise, and variability.
• Reflect on ethical, usability, and societal considerations related to wearable technologies and data-driven health applications.
• Develop confidence in engaging with interdisciplinary topics that combine hardware, data analysis, and AI, supporting further studies in wearable systems, data science, or applied machine learning. 

The teaching methods for the workshop will be interactive and engaging, combining a variety of instructional strategies. The course will utilise a blend of lectures, hands-on practical sessions, and collaborative group activities to foster an active learning environment. Lectures will introduce theoretical concepts and industry applications, while practical sessions will provide participants with the opportunity to work directly with tools and frameworks used in AI/ML and wearables. 

Group discussions and peer-to-peer learning will encourage participants to share insights and collaborate on problem-solving, enhancing their understanding and application of AI/ML in real-world scenarios in wearable technology. Additionally, the use of online resources and guided self-study will support independent learning and reinforce key concepts outside of the classroom. 

Course workload
Contact hours, intensive course participation 30h
Individual work 21h
Team project work 30h
Total 81h (3 ECTS)

The course is graded as pass/fail.

Please note that the undergraduate courses offered by Aalto University Summer School can not be included in Aalto degree studies.

• Participants are encouraged to have an interest in programming, preferably in Python and C, but no prior coding experience is required.
• A basic understanding of statistics and mathematics will be helpful, but is not essential. A willingness to learn and engage in hands-on activities, along with a curiosity about AI and wearable technologies, are the most essential prerequisites for this summer course.
• The course is designed to be accessible to beginners, providing a supportive environment for all participants to explore the exciting applications of AI/ML in real-world scenarios of wearable technologies.
• Participants must have completed a high school or vocational degree or equivalent by the time the course starts. However, they do not have to be a degree student at a university to participate in our courses.
• Additionally, all summer school course students must be 18 years or older, as it is the legal age in Finland.

Alongside your CV, please include a motivation letter or video in your application. In your motivation letter or video, describe your motivation for taking this course. The recommended length for the motivation letter is 150-250 words, and 1-2 minutes for the video.

Tuition fees for Summer School courses

One course: 1270€ (incl. VAT 25,5%) 

• The course fee includes academic course sessions and assignments, an official digital Aalto University certificate and support from the Summer School team. The fee does not cover accommodation, daily meals, or transportation during the course
• Unfortunately, Aalto University Summer School does not offer scholarships for participants.

Social program fee

More information about the social program coming soon!

Learn about the Aalto University Summer School cancellation terms. You will receive the full terms before making the payment and confirming the course participation.

If you need a visa, we recommend applying by April 15th 2026.

• This is to ensure that we have enough time to process your application and that you have time to make the tuition fee payment, book a visa appointment and wait for the application to be processed.
• You can apply and be accepted to the courses after April 15th. In that case, we recommend that you pay at your earliest convenience so that we can deliver your invitation letter to you promptly.
• According to the Ministry of Foreign Affairs of Finland, the processing time for a visa application is 15 days. However, the processing time may be longer if a more detailed application examination is required.