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252-0312-00L 6 Credits BSC , DR , MSC , WBZ , NDS D-BSSE , D-MAVT , D-INFK , D-PHYS , D-ITET , D-GESS , D-MATH , D-HEST
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Mobile Health and Activity Monitoring

Lecturers & Examiners: Prof. Dr. Christian Holz
VVZ CR 4.1

Last Updated: 2026-02-05 16:38:54

Abstract

Health and activity monitoring has become a key purpose of mobile & wearable devices (e.g., phones, watches, rings). We will cover the phenomena they capture, user behavior, activity, and human physiology, alongside the sensors, signals, and methods they leverage.In the exercise, students will process raw recordings from a wearable wristband to extract activity insights and health signals.

Objective

The course will combine high-level concepts with low-level technical methods needed to sense, detect, and understand them. High-level: – sensing modalities for interactive systems – "activities" and "events" (exercises and other mechanical activities such as movements and resulting vibrations) – health monitoring (basic cardiovascular physiology) – affective computing (emotions, mood, personality) Lower-level: – sampling and filtering, time and frequency domains – cross-modal sensor systems, signal synchronization and correlation – event detection, classification, prediction using basic signal processing as well as learning-based methods – sensor types: optical, mechanical/acoustic, electromagnetic

Content

Health and activity monitoring has become a key purpose of mobile and wearable devices, including phones, (smart) watches, (smart) rings, (smart) belts, and other trackers (e.g., shoe clips, pendants). In this course, we will cover the fundamental aspects that these devices observe, i.e., user behavior, actions, and physiological dynamics of the human body, as well as the sensors, signals, and methods to capture, process, and analyze them. We will then cover methods for pattern extraction and classification on such data. The course will therefore touch on aspects of human activities, cardiovascular and pulmonary physiology, affective computing (recognizing, interpreting, and processing emotions), corresponding lower-level sensing systems (e.g., inertial sensing, optical sensing, photoplethysmography, electrodermal activity, electrocardiograms) and higher-level computer vision-based sensing (facial expressions, motions, gestures), as well as processing methods for these types of data. The course will be accompanied by a group exercise project, in which students will apply the concepts and methods taught in class. Students will receive a wearable wristband device that streams IMU data to a mobile phone (code will be provided for receiving, storing, visualizing on the phone). Throughout the course and exercises, we will collect data of various human activities from the band, annotate them, analyze, classify, and interpret them. For this, existing and novel processing methods will be developed (plenty of related work exists), based on the collected data as well as existing datasets. We will also combine the band with signals obtained from the mobile phone to holistically capture and analyze health and activity data. Full details: https://siplab.org/courses/mobile_health_activity_monitoring/2024

Resources

Lecture Notes

Copies of the slides will be made available. Related work and further reading will be provided.More information on the course site:https://siplab.org/courses/mobile_health_activity_monitoring/2024

Literature

Will be provided in the lecture

Learning Materials (Links)

General Information

Language
English
Levels
BSC , DR , MSC , WBZ , NDS
Frequency
Yearly recurring

Examination

Type
end-of-semester examination
Mode
written 90 minutes
Aids
One handwritten two-sided A4 sheet and a non-programmable calculator.
Digital
The exam takes place on devices provided by ETH Zurich.
The end-of-term exam will be a computer-based presence exam at ETH facilities. The exam platform will be Moodle.The exam will count 50% of the final grade. The practical exercise counts for the remaining 50% of the final grade. The exercise assignment is mandatory and will need to be submitted on time during the term.

Course Components

Type Title Time & Place Hours
lecture Mobile Health and Activity Monitoring
TA-Meeting: Monday 13-14, CAB G56
  • Mon 14:15-16:00 (CAB G 11)
2 h weekly
independent project Mobile Health and Activity Monitoring No time listed 3 h weekly

Offered In