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The success of many medical devices depends on selecting materials that can function safely and reliably within the human body. Whether designing joint replacements, dental implants or cardiovascular devices, biomedical engineers must understand how materials interact with biological tissues and how these interactions influence long-term clinical performance. This module explores the engineering principles behind the selection and application of biomaterials.

You will investigate the properties of metallic, ceramic and polymeric biomaterials, examining how engineers balance mechanical performance, durability, corrosion resistance and biocompatibility when designing medical implants. Through real clinical case studies—including hip and knee replacements—you will explore how engineering failures have shaped the development of modern biomaterials and gain an appreciation of the relationship between materials engineering and patient outcomes.

By the end of the module, you will be able to evaluate and select biomaterials for a range of biomedical applications, combining engineering knowledge with an understanding of clinical performance and healthcare needs.

The success of many medical devices depends on selecting materials that can function safely and reliably within the human body. Whether designing joint replacements, dental implants or cardiovascular devices, biomedical engineers must understand how materials interact with biological tissues and how these interactions influence long-term clinical performance. This module explores the engineering principles behind the selection and application of biomaterials.

You will investigate the properties of metallic, ceramic and polymeric biomaterials, examining how engineers balance mechanical performance, durability, corrosion resistance and biocompatibility when designing medical implants. Through real clinical case studies—including hip and knee replacements—you will explore how engineering failures have shaped the development of modern biomaterials and gain an appreciation of the relationship between materials engineering and patient outcomes.

By the end of the module, you will be able to evaluate and select biomaterials for a range of biomedical applications, combining engineering knowledge with an understanding of clinical performance and healthcare needs.

The module aims to provide an integrated understanding of the representation and analysis of dynamical systems (electrical and mechanical), their solution and practical implementation in diagnosis and health monitoring for biomedical engineering problems and applications.

The module integrates three components related to the analysis of (1) mechanical system, (2) electrical machines, and (3) power drives, and each component has specific aims:

1.To provide a detailed understanding of mechanical systems, vibration analysis using frequency response and energy approximations methods, which is further extended into continuous mechanical problems.

2.To introduce the students to fundamental concepts and principles of operation of types of electrical machines and provide basic experimental and modelling skills associated with electrical machines.

3.To provide a detailed understanding of all aspects of the selection, sizing and operation of modern electrical drive systems; this will be achieved by consideration of the individual sub-system including power semiconductors, electronic power converters and associated electric motors, mechanical power transmission, speed and velocity transducers, and controllers.

The module aims to provide an integrated understanding of the representation and analysis of dynamical systems (electrical and mechanical), their solution and practical implementation in diagnosis and health monitoring for biomedical engineering problems and applications.

The module integrates three components related to the analysis of (1) mechanical system, (2) electrical machines, and (3) power drives, and each component has specific aims:

1.To provide a detailed understanding of mechanical systems, vibration analysis using frequency response and energy approximations methods, which is further extended into continuous mechanical problems.

2.To introduce the students to fundamental concepts and principles of operation of types of electrical machines and provide basic experimental and modelling skills associated with electrical machines.

3.To provide a detailed understanding of all aspects of the selection, sizing and operation of modern electrical drive systems; this will be achieved by consideration of the individual sub-system including power semiconductors, electronic power converters and associated electric motors, mechanical power transmission, speed and velocity transducers, and controllers.

The module aims to provide an integrated understanding of the representation and analysis of dynamical systems (electrical and mechanical), their solution and practical implementation in diagnosis and health monitoring for biomedical engineering problems and applications.

The module integrates three components related to the analysis of (1) mechanical system, (2) electrical machines, and (3) power drives, and each component has specific aims:

1.To provide a detailed understanding of mechanical systems, vibration analysis using frequency response and energy approximations methods, which is further extended into continuous mechanical problems.

2.To introduce the students to fundamental concepts and principles of operation of types of electrical machines and provide basic experimental and modelling skills associated with electrical machines.

3.To provide a detailed understanding of all aspects of the selection, sizing and operation of modern electrical drive systems; this will be achieved by consideration of the individual sub-system including power semiconductors, electronic power converters and associated electric motors, mechanical power transmission, speed and velocity transducers, and controllers.

Modern medicine runs on data. Every diagnosis or monitoring procedure, from blood pressure measurement to heart signal recording and medical scanning, generates vast quantities of information, from time series to the large image datasets a modern scanner produces. Making clinical sense of it depends on computational tools that can enhance, analyse and monitor signals and images and extract what matters. The same need drives biomedical research, where ever larger studies collect ever larger datasets on both healthy function and disease.

In this module you will study signal and image processing techniques and apply them to real biomedical data. You will see how these methods predict unobserved processes from non-invasive measurements, identify impairments, screen populations for conditions such as breast cancer, and compare physiological properties across groups. Alongside the analysis, you will learn the physiology behind biomedical signals and the engineering principles of the devices that record them, and you will consider the ethical, economic and multidisciplinary issues of engineering for healthcare.

By the end of the module, you will be able to select and apply appropriate processing methods to biomedical problems and interpret the results with an understanding of both their clinical meaning and their limits. These skills are in high demand across medical technology and biomedical research, and they provide a strong foundation for advanced study or a career at the intersection of engineering and medicine. Some prior knowledge of signal processing or control is strongly recommended, and MATLAB or Python programming is required.

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.

Modern medicine runs on data. Every diagnosis or monitoring procedure, from blood pressure measurement to heart signal recording and medical scanning, generates vast quantities of information, from time series to the large image datasets a modern scanner produces. Making clinical sense of it depends on computational tools that can enhance, analyse and monitor signals and images and extract what matters. The same need drives biomedical research, where ever larger studies collect ever larger datasets on both healthy function and disease.

In this module you will study signal and image processing techniques and apply them to real biomedical data. You will see how these methods predict unobserved processes from non-invasive measurements, identify impairments, screen populations for conditions such as breast cancer, and compare physiological properties across groups. Alongside the analysis, you will learn the physiology behind biomedical signals and the engineering principles of the devices that record them, and you will consider the ethical, economic and multidisciplinary issues of engineering for healthcare.

By the end of the module, you will be able to select and apply appropriate processing methods to biomedical problems and interpret the results with an understanding of both their clinical meaning and their limits. These skills are in high demand across medical technology and biomedical research, and they provide a strong foundation for advanced study or a career at the intersection of engineering and medicine. Some prior knowledge of signal processing or control is strongly recommended, and MATLAB or Python programming is required.

Modern medicine runs on data. Every diagnosis or monitoring procedure, from blood pressure measurement to heart signal recording and medical scanning, generates vast quantities of information, from time series to the large image datasets a modern scanner produces. Making clinical sense of it depends on computational tools that can enhance, analyse and monitor signals and images and extract what matters. The same need drives biomedical research, where ever larger studies collect ever larger datasets on both healthy function and disease.

In this module you will study signal and image processing techniques and apply them to real biomedical data. You will see how these methods predict unobserved processes from non-invasive measurements, identify impairments, screen populations for conditions such as breast cancer, and compare physiological properties across groups. Alongside the analysis, you will learn the physiology behind biomedical signals and the engineering principles of the devices that record them, and you will consider the ethical, economic and multidisciplinary issues of engineering for healthcare.

By the end of the module, you will be able to select and apply appropriate processing methods to biomedical problems and interpret the results with an understanding of both their clinical meaning and their limits. These skills are in high demand across medical technology and biomedical research, and they provide a strong foundation for advanced study or a career at the intersection of engineering and medicine. Some prior knowledge of signal processing or control is strongly recommended, and MATLAB or Python programming is required.

During the process of diagnosis and subsequent treatment, patients routinely undergo imaging, measurement and monitoring procedures using a wide range of techniques. Whether it is the automated monitoring of blood pressure of flow, the electrical signals generated during the contractions of the heart or medical images taken with a state of the art medical scanner, all these techniques produce vast amounts of data, for example in the form of time-series signals representing blood-pressure variation or the large image data-sets from a medical scanner. To help medical practitioners make sense of this flood of information, it is thus becoming increasingly important to provide reliable computational tools that can automatically enhance, analyse and monitor these signals and images, and extract (or facilitate the extractin of) clinically useful information. The same is true in medical and biological research, where similar biomedical monitoring techniques are used to study both healthy biological functions as well as mechanisms of disease and where ever larger studies collect ever larger data-sets of signals and images.

Signal and image processing techniques now allow us to predict unobserved biological processes from non-invasive measurements (for example in the control of blood flow), identify specific impairments (for example in executing movements of the limb), reliably screen large populations for common medical conditions (such as breast cancer) and allow us to automatically compare physiological properties between different populations (such as, for example, the change in the size of certain brain regions in epilepsy patients).

In this module you will study a range of signal and image processing techniques and will learn how they can be used to analyse a range of biomedical signals and images. Whilst learning general and specific analysis techniques, you will also gain insight into relevant biomedical background (such as the basic physiological properties that give rise to many biomedical signals and images) and many of the engineering principles that underlie the operation of key devices that are used to record biomedical signals or generate biomedical images. The module will also discuss engineering issues in the wider context of exploiting engineering for health-care, including relevant ethical and economic issues and multidisciplinary collaboration and communication.


Students should be aware that some knowledge of signal processing or control is strongly recommended. Knowledge of Matlab or Python programming required.