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Department of Computer Science and Technology

Masters

 

Course pages 2022–23 (working draft)

Mobile Health

Principal lecturer: Prof Cecilia Mascolo
Taken by: MPhil ACS, Part III
Code: L349
Hours: 16 (14h lectures and 2h practicals)
Format: In-person lectures

Aims

The course aims to explore how wearable and mobile systems sensors can be used to gather data relevant to understand health, how the data can be analysed with advanced signal processing and machine learning and the performance of these systems in terms of diagnostics and disease progression detection.

Syllabus

Lectures 1 - 2   Course Overview. Introduction to Mobile Health. Evaluation metrics and methodology. Basics of Signal Processing.

Lectures 3 - 4   Inertial Measurement Units, Human Activity Recognition (HAR) and Gait Analysis and Machine Learning for IMU data.

Lectures 5 - 6   Radios, Bluetooth, GPS and Cellular. Epidemiology and contact tracing, Social interaction sensing and applications. Location tracking in health monitoring and public health.

Lectures 7 - 8   Audio Signal Processing. Voice and Speech Analysis: concepts and data analysis. Body Sounds analysis.

Lectures 9 -10  Photoplethysmogram and Light sensing for health (heart and sleep)

Lecture 11        Contactless and wireless behaviour and physiological monitoring

Lecture 12        Mobile Devices and Behaviour intervention

Lectures 13-14 Topical Guest Lectures

Objectives

The course aims to explore how wearable and mobile systems sensors can be used to gather data relevant to understand health, how the data can be analysed with advanced signal processing and machine learning and the performance of these systems in terms of diagnostics and disease progression detection.

Roughly, each lecture contains a theory part about the working of “sensor signals” or “data analysis methods” and an application part which contextualises the concepts.

At the end of the course students should: Understand how mobile/wearable sensors capture data and their working. Understand different approaches to acquiring and analysing sensor data from different types of sensors. Understand the concept of signal processing applied to time series data and their practical application in health. Be able to extract sensor data and analyse it with basic signal processing and machine learning techniques. Be aware of the different health applications of the various sensor techniques. The course will also touch on privacy and ethics implications of the approaches developed in an orthogonal fashion.

Assessment - Part II students

Two assignments will be based on two datasets which will be provided to the students. The first assignment (worth 30% of the final mark) will be set of preprocessing and basic data analysis steps in a “colab” style report.
The second assignment (worth 70% of the final mark) will be a fuller analysis where the students are asked to compare and contrast ML algorithms/soutions and discuss findings and interpretation in terms of health context. This will be in the form of a colab and a reflection report of 1000 words.

Recommended Reading

Please see Course Materials for recommended reading for each session.

Assessment  - Part III and MPhil students

Two assignments will be based on datasets which will be provided to the students.

The first assignment (worth 30% of the final mark) will be set of preprocessing and basic data analysis steps in a “colab” style report.

The second assignment (worth 70% of the final mark) will be based on multiple datasets. The students will be asked to compare and contract ML algorithms/solutions (trained and tested on the different data) and discuss the findings and interpretation in terms of health context. This will be in the form of a colab and a reflection report of 1500 words.

Further Information

This module is shared with Part II of the Computer Science Tripos. Assessment will be adjusted for the two groups of students to be at an appropriate level for whichever course the student is enrolled on.

This module is shared with Part II of the Computer Science Tripos. Assessment will be adjusted for the two groups of students to be at an appropriate level for whichever course the student is enrolled on. Further information about assessment and practicals will follow at the first lecture.