Wearable technology provides valuable health information that can benefit medical assessment and diagnosis, and improve patient recovery. Wearable devices can consist of various analogue and digital sensors such as accelerometers, inertial measurement units (IMU), vibration, fabric, piezo-resistive and fibre optic sensors that can capture specific movement information when strategically placed on the human body.

This module will provide students with an understanding of the various types of sensors that can capture targeted movements for particular use cases. Students will use an assigned sensor throughout the module and complete practical tasks to capture specific movements for analysis. They will interrogate sensor data using computer algorithms. Students will also appraise sensor pre-processing techniques vital for sensor output improvement and will understand the strengths and weaknesses of wearable sensor technologies.

1. Identify and differentiate between various sensor technologies suitable for human movement capture .
2. Analyse latest developments in sensor technology for appropriate use in a digital healthcare environment.
3. Identify and appraise sensor filtering techniques for improve ment in sensor stability and accuracy.
4. A nalyse and develop techniques used for sensor data acquisition, data processing and visualisation.
5. Design and implement computer algorithms t hat interoperate with sensors and create meaningful output suited to particular use cases.
6. Assess the regulations governing the development of medical devices.
7. Critically examine case studies to identify relevant problems in a healthcare environment and develop suitable sensor -centric solutions .

This module aims to give students, healthcare professionals and bioresearchers, the ability to evaluate and gain a deeper understanding of the ethical and bioethical impact of current and approaching new digital technology in patient data, healthcare systems and data analysis.  It will also enable the learners to comprehend the issues regarding the protection and rights of research participants

1. Evaluate current ethical and bioethical theories in conjunction with emerging digital technology and healthcare approaches
2. Appraise the handling of patient and research subject data in coexistence with digital technology
3. Discriminate relevant patient data using appropriate healthcare/research/digital ethical guidelines.
4. Ascertain responsibility, healthcare legal liability of patient data knowledge and use
5. Establish autonomous ethical guidelines based on Ethical and Legal principles
6. Distinguish digital healthcare ethical principles in relation to patient rights and data protection
7. Justify ethical and bioethical principles in relation to research methods using patient digital data.

Healthcare Data Analytics plays a crucial role in progressing the knowledge and wisdom that can extracted from a variety of large data sources and an understanding of Data Analytic techniques is critical for individuals working in the Digital Healthcare industry. Good clinical decision making, and patient safety is driven by the scientific evidence base which informs best practice, integration and interpretation of patient information, informed execution of the clinical management plan and by information exchange between clinical teams. This module will provide a comprehensive overview of Data Analytics and Statistical Analysis that may applied in the Healthcare industry to provide insights and advances in the decision making process.

1. Critically explain the notion of Data Analytics in the Healthcare Domain
2. Identify the data sources appropriate to investigation and intelligence gathering for healthcare
3. Examine a range of approaches, tools and methodologies for analysing healthcare data.
4. Develop analytics methodologies and data visualisation for extrapolating information from datasets.
5. Identify current trends and approaches in dealing with challenges and limitations related to Healthcare Data.
6.
Devise and justify research, information gathering, critical analysis, design and implementation techniques appropriately and effectively

The aim of this module is to become familiar with the process and practice of design for IT systems for delivering healthcare information services. In this module, the architectural requirements of exemplar systems, including the interface, business services, cloud, quality attributes, privacy and security and multilingual requirements are put under the microscope. The focus is on data-driven technologies that underpin contemporary healthcare provision, decision services and mobile services. On completion of the module, the student will be familiar with the design of modern healthcare systems and have acquired the skillsets necessary to differentiate between good and bad systems design.

1. Critically analyse the composition of IT infrastructure in the context of healthcare requirements.
2. Critically examine the requirements for a data-driven organisation and identify prerequisites.
3. Critically appraise interfacing technology for data integration and determine its place within a layered architecture.
4. Analyse Healthcare IT systems and information flow in the context of patient journey.
5. Evaluate, analyse and examine key elements required to integrate and support the healthcare application in a hospital environment.
6. Determine the technologies and skills needed for cloud-based healthcare system applications.

This module will give students practice in academic research with the freedom to express their personal interests in the field of digital healthcare. Students will gain an appreciation of iterative development, critique and an ability to devise a plan for practical development. This module brings together various taught techniques from previous modules with the goal of enabling the student to produce a poster presentation.

1. E valuate pertinent emerging trends from information sources and present findings.
2. Develop and document detailed aims and objectives from preliminary research.
3. Articulate and justify ethically appropriate research questions and hypotheses.
4. Critically analyse and evaluate select ed pertinent literature succinctly.
5. Analyse advancements in technology with regard to digital healthcare.
6. Develop and present a preliminary design solution using appropriate methodologies for the field of study.

Measurement and analysis systems play an essential role within a healthcare environment to quantify and assess patient health status. Understanding the terminologies and methodologies used to assess accuracy, reliability, repeatability, sensitivity, error and tolerances of these systems are essential for their evaluation. Applying these methodologies under specific conditions and understanding subtitles in their application forms the basis of this module.

Students will gain an understanding of the various forms of assessment strategies used for appraisal of reliability and validity of patient measurement and assessment systems, and their application within a clinical environment.

1. Identify , differentiate and critique various forms of reliability and validity measurement.
2. Construct appropriate measurement techniques and evaluate their properties.
3. Appraise assessment strategies and examine instrument validity for development of a gold standard of measurement.
4. Examine measurement and analysis processes currently used for patient assessment within a healthcare environment.
5. Discuss and critique statistical methodologies to analyse variances in sensor reliability measurements.
6. Evaluate sensor processing methodologies to improve data filtering, transformation, and analysis.
7. Critically examine case studies to assess the appropriateness of various testing strategies and correctly apply these.

This module involves working on a research dissertation over an extended period. Working under the guidance of an academic supervisor, the module allows the student to develop theoretical and applied skills in using contemporary computing science techniques both from a theoretical perspective and as an applied discipline. The dissertation represents the capstone work on a topic related to their chosen programme of study.

1. Develop appropriate data collection instruments for mixed methods research and to evaluateeach for their appropriateness to the research question;
2. Engage in a sustained piece of individual, academic research on a chosen topic within the field(s) specifically relevant to the course;
3. Read widely and critically reflect on a number of pieces of written research in an appropriate and thorough manner;
4. Evaluate varying methodological approaches and to adopt the necessary approaches suitableto the topic being researched;
5. Produce a dissertation that evaluates and synthesises quantitative (statistical) approaches such as empirical tests on a hypothesis;
6. Produce a dissertation that evaluates and synthesises qualitative research methods such as the literature review and dissertation proposal and displays evidence of independent research skills.