The Intelligent Personalised Support theme focuses on studying and developing the means to collect and analyse comprehensive health-related data.

Examples of this health-related data includes symptoms, vital signs, emotions and activities. These data can, with the appropriate machine intelligence, provide the basis for intelligent personalised support to self-management, in various forms, such as information, advice and reminders.

This research theme will apply machine intelligence to personal sensed data to allow the inference of health and wellbeing related information in a holistic way (physical and mental health related parameters such as symptoms, vital signs, emotions and activities), detect patterns and pattern degradations over time and provide personal recommendations to promote behaviour change when self-managing health related conditions. This research theme involves adapting the information captured from wearable and environmental sensors, detecting features from the user voice and video images, inferring health and wellbeing related features using machine learning techniques, presenting the information to health professionals in a user friendly way, using machine learning techniques to learn the decisions from health professionals, developing personal health recommender systems and adapting the way in which advice is provided to each user to promote behaviour change.

Sheffield has a strong track record in developing technologies which use monitoring and feedback to assist people with long-term conditions in self-managing their health. For example, Mountain, Hawley and Mawson, together with partners in Ulster and Bath, have carried out a programme of research funded initially by EPSRC and currently NIHR to study the role of smart technologies in facilitating self-management. This has led to the development and testing of a number of innovative technologies. One of these has been the SMART-COPD project led by Hawley which has developed a smartphone app-based intervention to help people with severe lung disease to self-manage their pulmonary rehabilitation after discharge from the NHS, based on monitoring and feedback of data on physical activity levels.   Another, SMART-Stroke led by Mawson, has developed a smart shoe and app to help people with stroke to self-manage rehabilitation of their walking.

The outcome for people with long-term conditions will be an enhanced ability to self-manage their own condition, leading to improved quality of life, reduced need for urgent healthcare and, ultimately, reduced morbidity and mortality, also helping to reduce the burden on health services. Advances in this field will lead to translation into innovative new products, which we will take forward with our industry partners.

Projects in Intelligent Personalised Support

 

Members working in Intelligent Personalised Support

Name Dept (faculty) Research strengths
Prof Mario Munoz-Organero
Research Lead
ScHARR (MDH) Assistive Technology based on wearable sensors
Telehealth and Telecare based on wearable sensors
Digital Healthcare based on wearable sensors
supported by
Prof Mark Hawley
ScHARR (MDH) Assistive Technology
Telehealth and Telecare
Digital Healthcare

 

Name Dept (faculty) Research strengths
Prof Sue Mawson ScHARR (MDH) Sensors for monitoring stroke and osteoarthritis patients
Dr Heidi Christensen Computer Science (Engineering) Sensors for speech recognition
Prof Mahdi Mahfouf ACSE (Engineering) Intelligent systems based signal processing
Modelling and control in Biomedicine
Prof Neil Lawrence Computer Science (Engineering) Machine learning and inference
Prof Tim O’Farrell EEE (Engineering) Wireless communications
Dr Wei Liu EEE (Engineering) Signal processing and Human computer interfaces
Prof Peter Bath Information School (Social Sciences) Health informatics and applications of artificial intelligence and data mining
Prof Alex Frangi Mechanical Engineering (Engineering) Image analysis and image based biomedicine
Prof Marco Viceconti Mechanical Engineering (Engineering) Biomechanics of the musculoskeletal system
Prof Roger Lewis Mechanical Engineering (Engineering) Inclusive design, Skin tribology and interaction with objects and devices
Dr Mohammed Benaissa EEE (Engineering) Remote health monitoring for chronic conditions
Dr Mahnaz Arvaneh ACSE (Engineering) Brain- computer interface

Explore the other research areas:

Assistive Robotics & Social Robotics

Assistive Robotics & Social Robotics

Intelligent Personalised Support

Intelligent Personalised Support

Human Communication Technology

Human Communication Technology

Complex Behavioural Interventions

Complex Behavioural Interventions