Computing and Information Science PhD project opportunities

Find out more about self-funded PhD projects in areas where we already have supervisors active and engaged in the research topic in our School of Computing and Information Science.

A decentralized, data driven health monitoring and diagnostics platform based on Artificial Intelligence (AI) and wearable/portable Internet of Medical Things (IoMT) sensors

Research Group

Computing, Informatics and Applications Research Group

Proposed supervisory team

Dr Mahdi Maktabdar

Dr Lakshmi Babu Saheer

Theme

Artificial Intelligence, Machine Learning, Data Science and Applications

Summary of the research project

With the advent of the Fourth Industrial Revolution, novel information and communication technologies such as Cloud Computing, Big Data, Internet of Things (IoT), 5G and Artificial intelligence (AI) have been incorporated into organisations and industries to facilitate and support more efficient and flexible processes, services and products. Industry 4.0’s inherent future-forward benefits are driving innovation across various industries and disrupting outdated and inefficient practices (Chanchaichujit et al., 2019; Kumari et al., 2018).

Healthcare is one of the notable industries that has been influenced by the Fourth Industrial Revolution and Healthcare 4.0 is a term that has emerged to resemble this revolution. Healthcare 4.0 is a collective term for data driven digital health technologies such as smart health, mobile health, wireless health, eHealth, online health, medical IT, telehealth/telemedicine, digital medicine, health informatics, pervasive health, and health information system. The revolution in the healthcare industry is already underway, yet, because of the conservative and slower pace of technological adoption by healthcare insiders, as compared to other industries, digitalization in this sector has not been so evident (Pace et al., 2018; Manogaran et al., 2017).

But make no mistake the revolution in healthcare industry is arriving at your nearby hospital in near future. With populations aging, chronic diseases rising and medical costs skyrocketing, the healthcare universe is in desperate need of the improvements that digitalization and industrialization will bring in terms of saving costs, improved diagnostics and more effective care. Moreover, the global shortage of doctors, nurses and technicians demands for improved efficiency and the need for technology to help bridge the demand-supply gap in services(Xu et al.,2019; Xu et al.,2018).

Healthcare industry is gradually moving toward utilization of the Internet of Medical Things (IoMT) devices such as smart wearables, capable to measure vital signs, Artificial Intelligence (AI) and cloud computing, which is leading to great leaps in diagnostic speed, accuracy and transform how we keep people safe and healthy especially as the demand for solutions to lower healthcare costs increase in the coming years. The IoMT, a major driver of this revolution takes patient monitoring to the next level by providing healthcare system with an uninterrupted stream of patient’s vital signs and other critical measures which enables early diagnostic and proactive healthcare invention. The IoMT can improve early diagnoses while allowing data collection for analytics, a win-win for the patient, and patients that benefit from the data down the road (Alsubaei et al., 2017; Joyia et al., 2017).

In this regard, we are proposing a research with the primary aim of creating a decentralized, data driven health monitoring and diagnostics ecosystem based on Artificial Intelligence (AI) and Internet of Medical Things (IoMT) sensors and wearables. The proposed research takes advantage of uninterrupted streams of patient’s vital signs provided by IoMT wearables such as blood pressure, pulse, temperature, respiration and oxygen saturation monitors and temporal data analytic methods in specific Recurrent Neural Networks (RNN) to improve early diagnoses of tens of chronic health conditions. The proposed research benefits a supervised data analytic engine, pretrained with millions of patient’s historical data available in the following dataset:

  1. The University of Queensland Vital Signs Dataset
  2. PhysioNet Dataset
  3. OpenICE

The proposed ecosystem comprising IoMT, Artificial Intelligence and cloud computing enables healthcare system to perform effectively as our population continues to age. The proposed ecosystem also has tremendous potential to help deal with the rising costs of care. The system provides opportunity to help remote caregivers ensure the safety of their loved ones with wearable devices that learn the regular routines of the person who wears the device and can issue a warning if something seems amiss as well as alert if seniors have breached their boundaries which is often of concern for memory-care patients.

There is possibility of several research approaches here which could spin-off as individual doctoral research in itself. The significance of an automatic digital health monitoring platform has been elevated in the recent times with the covid-19 pandemic. The isolated risk groups (like senior citizens or people with respiratory troubles) are advised to have minimal human contact. Such virtual healthcare platforms would be greatly beneficial in such pandemic situations, when there is a scarcity of carers all around. The proposed ecosystem may monitor the vital signs and at the same time could also have a machine learning backend to analyse these inputs to alert carers or appropriate medical authorities. These alert systems could be designed, customized and optimized by carers based on the type of monitoring to be focused at each stage or for each individual.

Another new dimension to add in this framework would be to include the mental wellbeing monitoring. This might require a bit more involved participation from the subjects rather than just vital signs. But, the same system could be extended and customized for general health and mental health monitoring in a broader sense across the whole population.

References:

Chanchaichujit, J., Tan, A., Meng, F., & Eaimkhong, S., 2019. Internet of Things (IoT) and Big Data Analytics in Healthcare. In Healthcare 4.0, pp.17-36. Palgrave Pivot, Singapore.

Kumari, A., Tanwar, S., Tyagi, S., & Kumar, N., 2018. Fog computing for Healthcare 4.0 environment: Opportunities and challenges. Computers & Electrical Engineering, 72, pp.1-13.

Manogaran, G., Thota, C., Lopez, D., & Sundarasekar, R., 2017. Big data security intelligence for healthcare industry 4.0. In Cybersecurity for Industry 4.0, pp.103-126. Springer, Cham.

Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A., 2018. An edge-based architecture to support efficient applications for healthcare industry 4.0. IEEE Transactions on Industrial Informatics, 15(1), pp.481-489.

Xu, L. D., & Duan, L., 2019. Big data for cyber physical systems in industry 4.0: a survey. Enterprise Information Systems, 13(2), pp.148-169.

Xu, L. D., Xu, E. L., & Li, L., 2018. Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), pp.2941-2962.

Joyia, G. J., Liaqat, R. M., Farooq, A., & Rehman, S., 2017. Internet of Medical Things (IOMT): applications, benefits and future challenges in healthcare domain. J Commun, 12(4), pp,240-247.

Alsubaei, F., Abuhussein, A., & Shiva, S., 2017. Security and privacy in the internet of medical things: taxonomy and risk assessment. In 2017 IEEE 42nd Conference on Local Computer Networks Workshops (LCN Workshops), pp.112-120. IEEE.

Where you'll study

Cambridge

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Adaptive security threat detection for small footprint computing devices within industrial control systems, intelligent homes, medical devices & smart infrastructure

Research Group

Cyber Security and Networking Research Group

Proposed supervisory team

Adrian Winckles

Dr Erika Sanchez-Velazquez

Theme

Cyber security

Summary of the research project

This research will explore a new way of managing some of the cyber security issues related to the use of low power computing and sensor devices, principally those for the Internet of Things (IoT) and Industrial Control Systems (ICS).

Currently large enterprises, service providers and other organisations rely heavily on a legacy model of cyber security threat detection/analysis based on an 'in-band' management solution which doubles the network bandwidth required to undertake data capture and effectively lowers network efficiency. This places an enormous strain on enterprises worldwide who will need to increase their managed detection and response services from 1% to 15% as the use of Iot/ICS devices reaches an anticipated 26 billion by 2020. Unless new approaches are found to manage IoT/ICS cybersecurity this situation will get worse.

The proposed research will seek to identify how a distributed thin model of real-time Adaptive Data Capture on small footprint devices within an Iot/ICS infrastructure can improve the effectiveness of an organisations threat detection capability so allowing the improved mitigation of risk. The use of intelligent learning systems will feature to allow the adaptive nature of data capture to be become autonomous, so realising benefits such as improved efficiency of a Security Operations Centre, improved response time between infection and detection, and improved pre-forensics capability.

This research has a high potential impact given the field is of paramount importance to the fight against cyber crime by government 'blue lights' organisations such as police forces and intelligence agencies.

Where you'll study

Cambridge

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

AI for sustainability in food supply chain and pricing

Research Group

Computing, Informatics and Applications Research Group

Proposed supervisory team

Dr Lakshmi Babu Saheer

Dr Cristina Luca

Theme

Artificial Intelligence, Machine Learning, Data Science and Applications

Summary of the research project

Artificial Intelligence has wide spread impact in every aspect of human life. This impact could potentially range from current day challenges of Climate change (Climate change AI, 2019) to building a sustainable future with alternate sources of energy and sustainability in food supply. Challinor et.al., (2014) and Ray, et.al., (2012) emphasize the importance of research in the areas of food production to control food supply and pricing, to build a sustainable future. Recent pandemic situation and threat of scarcity in food supply has highlighted need for identifying local food sources to maintain a steady and affordable flow of food in local communities.

There have been various studies to look into data analysis for identifying food production shocks and food price in global economy (Jones & Phillips, 2016). Recently, block chain has been extensively used to model food supply chain (Casino, et.al., 2014). This greatly helps in traceability and proof of regulatory compliance. It is of great importance to identify the local supply chain to keep the prices and availability in check along with building a sustainable future.

This research will be focussed on identifying the key factors in the food supply chain. The research could be initiated by understanding features influencing food pricing and shocks by looking at historic data (FaoStat, 2019). Based on these build sustainable models of food supply and pricing such that locally grown products get to the local markets. Understanding the key expenditures in the food supply chain supports in not only ensuring food supply, but also affordable food pricing and greater profit to local farmers. In turn this should lead to reduction in the energy and costs used in transport/storage and contribute to climate change.

The project would involve extensive data collection for local produce and demands in a selected region (probably Cambridgeshire) to support this endeavour. The project will also look into detailed data analysis and machine learning models to build optimum solutions including block chain supply model to get the most efficient system in place. Some of this research may be carried out in collaboration with the global sustainability institute at Anglia Ruskin. In effect, this research is expected to build a tool to co-ordinate and build an efficient local food supply chain for a sustainable future.

References:

Aled Jones and Alexander Phillip, 2016. Historic Food Production Shocks: Quantifying the Extremes, Sustainability open access journal, 8, pp.427.

Climate Change AI, 2019. Climate Change AI, [Accessed 25 November 2019]

Challinor, A.; Watson, J.; Lobell, D.B.; Howden, S.M.; Smith, D.R.; Chhetri, N., 2014. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang., 4, pp.287–291.

Ray, D.K., Ramankutty, N., Mueller, N.D., West, P.C., Foley, J.A., 2012. Recent patterns of crop yield growth and stagnation. Nat. Commun., 3, pp.1293.

Fran Casino, Venetis Kanakaris, Thomas K. Dasaklis, Socrates Moschuris, Nikolaos P. Rachaniotis, 2019. Modeling food supply chain traceability based on blockchain technology, IFAC-Papersonline, 32(13), pp.2728-2733.

Food and Agriculture Organisation of the United Nations Statistical Database, (accessed on March 2020).

Where you'll study

Cambridge

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

AI to the rescue of climate change, modelling air quality for cleaner urban planning

Research Group

Computing, Informatics and Applications Research Group

Proposed supervisory team

Dr Lakshmi Babu Saheer

Theme

Artificial Intelligence, Machine Learning, Data Science and Applications

Summary of the research project

Climate change is the main challenge that humanity is facing today, threatening the existence of life on earth. Awareness campaigns and drastic steps towards bringing the situation under control have been initiated in every nook and corner of the world. Both developed and underdeveloped countries are working hard to tackle this problem. Artificial Intelligence has big potential to help mankind wherever (big) data is available to help build models and make predictions or provide prescriptive solutions. Such models for emissions, resources, energy consumption, etc have already been statistically worked out by specialist groups around the world to tackle climate change (UCL Energy Models, 2019).

(Climate change AI, 2019) is a classic example of such an initiative. There is big data available in the fields of energy consumption, transport, building & cities, carbon footprint, farming, climate change & prediction etc (Rolnick, et.al., 2019). It is hard to mine all this data in a reasonable time to get useful resources with mathematical models. The approach for this PhD would be to first identify the most useful and informative data for the identified topics of climate change and start to build machine learning models and methodologies to extract useful and relevant information in the form of predictions or prescriptions. Even though a lot of data is available for every field, it is very difficult to gather majority of these information in a structured useful format.

The topic proposed for this PhD would be to look at the parallel data on road traffic, emissions, air quality, vegetation and other related information like weather around UK or even specifically cities like London, or Cambridge. This can directly help us plan our cities and traffic routes or even come up with laws to keep our carbon footprint under control. This research needs traffic and air quality or emissions monitoring datasets and possibly auxiliary information on vegetation and other related areas that would directly impact air quality.

The traffic monitoring data in UK is available with different authorities like:

  1. Cambridgeshire county council
  2. Highways England data
  3. Traffic for London

Emissions monitoring data in UK is available with authorities like:

  1. London Air
  2. Government Monitoring
  3. Defra, UK Air
  4. Traffic for London

The challenge would be to map these different sources to come with parallel data to extract useful information through machine learning approaches like SVMs or Neural Networks (Nathan, et.al., 2016), (Massimiliano, et.al., 2013), (Robert, et.al., 2016), (Ioannis & William, 2015). Traffic for London seems to have usable parallel data for traffic and emissions and is known to publicly share this information to support research. This could be a starting point for this research, extending to other cities or more resources for further information. Initial research on this topic has revealed several challenges with this data including missing and inconsistencies data. The first step of this project might to design solutions for an effective data collection based on the initial studies of big data to build custom solutions and policies around transportation.

Using AI for climate change control through efforts like the traffic-emissions control and improving air quality through urban vegetation planning could be a major step forward to build upon strategies for tackling climate change based on big data available in various fields to build a sustainable future.

References:

UCL Energy Models, 2019. Energy models at the UCL Energy Institute, www.ucl.ac.uk/energy-models [Accessed 25 November 2019] Climate Change AI, 2019. Climate Change AI, www.climatechange.ai [Accessed 25 November 2019]

Rolnick, David, et.al., 2019. Tackling Climate change with Machine Learning, https://arxiv.org/pdf/1906.05433.pdf [Accessed on 25 November 2019]

Nathan Mundhenk, Goran Konjevod, Wesam A Sakla, and Kofi Boakye, 2016. A large contextual dataset for classification, detection and counting of cars with deep learning, European Conference on Computer Vision, pages 785–800. Springer.

Massimiliano Gastaldi, Riccardo Rossi, Gregorio Gecchele, and Luca Della Lucia, 2013. Annual average daily traffic estimation from seasonal traffic counts, Procedia-Social and Behavioral Sciences, 87:279–291.

Robert Krile, Fred Todt, and Jeremy Schroeder, 2016. Assessing roadway traffic count duration and frequency impacts on annual average daily traffic estimation, Technical Report FHWA-PL-16-012, Federal Highway Administration, Washington, D.C., United States.

Ioannis Tsapakis and William H Schneider, 2015. Use of support vector machines to assign short-term counts to seasonal adjustment factor groups, Transportation Research Record: Journal of the Transportation Research Board, (2527):8–17.

Where you'll study

Cambridge

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Automating Creation Randomised Digital Evidence Crime scene Device Images for Digital Forensics and Incident Response Education

Research Group

Cyber Security and Networking Research Group

Proposed supervisory team

Adrian Winckles

Dr Erika Sanchez-Velazquez

Theme

Cyber Security and Digital Forensics Training Resources

Summary of the research project

One of the critical training needs for First Responders and Digital Forensics Investigators is to be able to have digital evidence that is customized to their training needs and uniquely different to previous resources which can be forensically investigated, and evidence retrieved and analysed.

This requires the use of automation tools to take a wide range of base digital platforms Windows, Mac OSX and Linux and create digital evidence artefacts with different case scenario evidence. It is essential to have a wide base of material and many digital devices now are not just desktops or laptops but consumer devices or industrial IoT devices.

We need to be able to take existing platforms such as SecGen which generate resources for Cyber Security training and Capture the Flag tournaments and see what the delta’s are to be able to use the base platform to create appropriate forensic resources.

Where you'll study

Cambridge

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Deep-learning for semantic-based information extraction from natural language

Research Group

Computing, Informatics and Applications Research Group

Proposed supervisory team

Dr Arooj Fatima

Dr Cristina Luca

Theme

Semantic web, Big data deep-learning

Summary of the research project

The idea of automatic information extraction from text documents comes from the time of first steps in natural language processing (NLP). Understanding the complex nature of natural language utterances is a key component of Artificial Intelligence. The amount of text data on the web is overwhelming and techniques to extract information automatically from this data will help manage this.

There have been various machine learning models introduced in the past to facilitate NLP tools. Recently, deep learning methods have exhibited relatively high performance in achieving certain tasks for NLP. Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains.

The proposed research will explore and utilise the methods of deep-learning to extract semantic information from the natural language text. From the semantic information, the authors mean that the system is able to (i) detect sentences and their structure (ii) identify named entities (iii) find relations and (iv) present information in a way that support human understanding. On this occasion, the authors have chosen visual representation (graph) of data as an out-put since it is relatively easier to evaluate.

Where you'll study

Cambridge

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Ontological modelling for data analysis

Research Group

Computing, Informatics and Applications Research Group

Proposed supervisory team

Dr Cristina Luca

Dr Arooj Fatima

Theme

Semantic web, big data and the analysis of free text

Summary of the research project

'Big Data' is the currently fashionable term used to describe data that exceeds the ability of traditional approaches to store and analyse due to its volume, velocity and variety. Sources typically include postings on the internet, research documents and surveys. This research seeks to utilise improvements in processing capacity to enable the effective and timely analysis of very large sets of complex data. In government and large organisations statistical methods are used to construct models that show how decisions may affect outcomes. However, these take a long time to construct and may have other technical limitations on the amount and variety of data they can consider.

The increasing use of feedback mechanisms and other Web 2.0 user generated content has created a large, unstructured but potentially valuable source of information representing the opinions of users, consumers, patients, students, travellers, holiday makers, diners etc. A site such as TripAdvisor operates an explicit star rating system but there are many other sources of data that could be useful to the manufacturer, retailer or service provider that do not provide their own degree of satisfaction. One promising approach has been through the use of online text analysis resources combined with an ontological classification which has been used to analyse the sentiment expressed in twitter posts.

Sentiment analysis of text can highlight those concepts that are associated with positive or negative sentiment and this information can be used to develop an ‘Ontological’ model that helps to identify issues and model behaviour. An ontology is a way of representing words with similar meanings between different textual representations. For example tutor, teacher, lecturer are textually distinct but have similar meanings. This allows us to build a model that summarises the key features of a domain, such as higher education satisfaction, through analysing free, unstructured text that might be found posted on social media.

Where you'll study

Cambridge

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Remote sensing and advanced spectral analysis for coaching and rehabilitation

Research Group

Computing, Informatics and Applications Research Group in collaboration with the Exeter Biomechanics Research Team (ExBiRT), University of Exeter

Proposed supervisory team

Dr Domenico Vicinanza

Dr Genevieve Williams (University of Exeter)

Dr Jin Zhang

Theme

Wireless sensors, Remote Sensing and Internet of Things, data sonification, coaching and rehabilitation

Summary of the research project

This research project is part of a series of activities carried out with Cambridge Centre for Sport and Exercise Sciences, using smart sensors/wireless sensors and audio analysis in biomechanics and biomedical sciences.

Remote sensors and Wireless Sensor Networks (WSN) are increasingly being applied to retrieve data from environmental measurements to motion and position tracking. Remote sensing provides the potential to collect data at spatial and temporal scales that could be either not feasible or difficult to implement with existing instrumentation. While remote sensing and wireless sensing is currently accepted as an adequate mechanism to gather remote data and share it over networks, very little has been currently done in the actual deployment of networked sensor-based infrastructures for sport and rehabilitation applications.

When coupled with remote sensing and networks, data sonification (mapping/converting measurements to audio signals) can provide physicians, physiotherapists and sport patients with uniquely effective ways to analyse data and provide accurate and personalised feedback without having to travel to a particular hospital. Consultants can analyse sonograms generated by the sonification of sensors in real-time from anywhere in the world and give immediate and accurate feedback.

Remote access to data and measurements are especially relevant when dealing with rehabilitation. While working with injured patients, having the possibility of thoroughly assessing the progress of a certain therapy, measuring in a quantitative way the success of a surgery can have a huge impact on the patience prognosis. Data sonification can display extremely accurately the progress of recovery in terms of subtle changes in spectral lines of kinematic/kinetic sensor audification.

Where you'll study

Cambridge

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Security threat intelligence

Research Group

Cyber Security and Networking Research Group

Proposed supervisory team

Adrian Winckles

Dr Erika Sanchez-Velazquez

Theme

Cyber security

Summary of the research project

According to both Gartner and NIST (National Institute of Standards and Technology), 92% of security vulnerabilities are now found in software. Within the industry, there are multiple layers of protection which offer a security in-depth approach around IT infrastructures filling the many holes offered by some layers.

There is a major industry need to identify emerging attacks against web applications and report them to the security community, in order to facilitate protection against such targeted attacks. We are leading the OWASP Web Honeypot project to produce a community of threat intelligence information.

The purpose of this part of the research is to capture intelligence on attacker activity against web applications and utilise this intelligence as ways to protect software against attacks.

This could potentially involve the use of honeypots as an established industry technique to provide a realistic target to entice a criminal, whilst encouraging them to divulge the tools and techniques they use during an attack. Like bees to a honeypot. These honeypots are safely designed to contain no information of monetary use to an attacker, and hence provide no risk to the businesses implementing them.

The honeypots in VM, Docker or small computing profiles like Raspberry Pi, employ ModSecurity based Web Application Firewall technology using OWASP’s Core Rule Set pushing intelligence data back to console to be converted to STIX/TAXII format for threat intelligence or pushed into ELK for visualisation.

The project will create honeypots that the community can distribute within their own networks. With enough honeypots globally distributed, we will be in a position to aggregate attack techniques to better understand and protect against the techniques used by attackers. With this information, we will be in a position to create educational information, such as rules and strategies, that application writers can use to ensure that any detected bugs and vulnerabilities are closed.

Where you'll study

Cambridge

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Smart sensors and spectral techniques in human movement science

Research Group

Computing, Informatics and Applications Research Group in collaboration with the Exeter Biomechanics Research Team (ExBiRT), University of Exeter

Proposed supervisory team

Dr Domenico Vicinanza

Dr Genevieve Williams (University of Exeter)

Dr Jin Zhang

Theme

Wireless sensors, data sonification, biomechanics and biomedical sciences Summary of the research project This research project is part of a series of activities jointly carried out by the Computing, Informatics and Applications Research Group and the Cambridge Centre for Sport and Exercise Sciences, using smart sensors/wireless sensors and audio analysis in biomechanics and biomedical sciences.

Our approach is based on the idea of analysing human movement signals and their relations by translating them into audible waveforms and using the advance sound analysis and spectral techniques to distinguish, characterise and analyse their shapes, amplitudes and structural properties. This process is called data sonification, and one of the main tools to investigate the structure of the sound is the sonogram (sometimes also called a spectrogram). A sonogram is a visual representation of how the spectrum of a certain sound signal changes with time, and we can use sonograms to examine the phase relations between a large collection of variables without having to reduce the data. Spectral analysis is a particularly relevant tool in many scientific disciplines, for example in high-energy physics, where the interest lies in energy spectra, pattern and anomaly detections, and phase transitions.

Using a sonogram to examine the movement of multiple markers on the body in the frequency domain, we can obtain an individual and situation-specific representation of co-ordination between the major limbs, detect pattern and anomalies, and identify and study phase transitions in biomedical sciences.

Where you'll study

Cambridge

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.

Sonification and smart sensors for healthy ageing

Research Group

Computing, Informatics and Applications Research Group in collaboration with the Exeter Biomechanics Research Team (ExBiRT), University of Exeter

Proposed supervisory team

Dr Domenico Vicinanza

Dr Genevieve Williams (University of Exeter)

Dr Jin Zhang

Theme

Wireless sensors, internet of things, data sonification, biomechanics and healthy ageing Summary of the research project This research project is part of a series of activities carried out with Cambridge Centre for Sport and Exercise Sciences, using smart sensors/wireless sensors and audio analysis in biomechanics and biomedical sciences.

Our approach is based on combining smart sensors, internet of things and data sonification. Sonification is the idea of analysing human movement signals and their relations by translating them into audible waveforms and using the advance sound analysis and spectral techniques to distinguish, characterise and analyse their shapes, amplitudes and structural properties.

This project will investigate the design and implementation of small (possibly wearable), wireless smart sensors with a special focus on healthy ageing.

Where you'll study

Cambridge

Funding

This project is self-funded. Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.

Next steps

If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.