Current Projects

Understanding and Improving through Mobile Technology Users’ Experience of Bladder Training

The number of people suffering from Overactive Bladder worldwide will reach 20.1% by 2018. The traditional treatment (including lifestyle changes and exercises) is an effective approach that can cure or ameliorate up to 70% of cases. Even though it is a treatable condition, it remains undiagnosed for several reasons: embarrassment, lack of knowledge of existing treatment and the misperception of being an age-related condition.

oab prjMobile health applications are increasingly being used to help in the rehabilitation of several conditions such as stroke or diabetes and in in promoting lifestyle changes. Our aim is to understand how a mobile phone app could be an appropriate tool to help people with Overactive Bladder symptoms (1) increase adherence to treatment, (2) assist during their rehabilitation treatment and (3) help them better understand their condition and symptoms.

Principle Investigator: Assoc. Prof. Lynne Baillie

Researcher: Miss Ana-Maria Salai

Dates Jan 2016 – Jan 2019

Investigation of Stroke Rehabilitation Technologies with the User Community

It is estimated that there are 15 million new strokes each year worldwide. In the UK stroke is the leading cause of disability among adults (1). For stroke survivors access to timely and skilled rehabilitation can improve recovery. However, with an NHS system under financial pressure and increasing demand being placed on rehabilitation services many Scottish stroke survivors fail to achieve optimal recovery (2). Rehabilitation technologies ranging from mobile apps to advanced robotics, can support efficient and effective delivery of rehabilitation. The integration of these technologies into mainstream practice, however, has been slow and variable (3). Reasons for this include lack of familiarity, availability, cost, setup time and lack of evidence (3). Resolving this disconnect between technology development and implementation into practice will require innovation from developers, users and policy makers. Current models of practice need to be challenged if technologies are to be fully exploited for patient benefit.

Our aim is reduce the healthy years lost to stroke through greater integration of technologies that promote patient centred functional recovery. To do this we propose a programme of activities designed to generate new thinking in this area by clarifying user priorities, developing a framework to evaluate and guide technology development in a way that places the user at the centre and to form a network of stakeholders capable of influencing practice nationally and internationally.

PI’s Lynne Baillie (Heriot-Watt University) and Andrew Kerr (Strathclyde University)


scottish universities insight institute


Dates Jan 2016 – Dec 2016

Falls Games – Phase 2

There is a fundamental gap regarding the suitability of existing technology to fully support fallers to adhere to their exercise prescription. We have built a system which aims to fill that gap and fully support fallers whatever their category and whatever their living circumstances to undertake their rehabilitation exercises supported and encouraged by user centred technology.

 falls games user workshops nghDuring this project we wish to expand and test our custom built exergame technology in order that it:

1. Exergames work for the widest possible range of users.

2. Assess how it works in different types of ‘operational environments’(Golden Living, WV Bureau, Cabell Huntington Hospitals, St Marys Medical Centre, Golden Living and the Institute for Geri Olympics).

3. Investigate ways in which it can be designed to facilitate community play.

4. Investigate how progress can be designed to motivate adherence.

Funder: Digital Health and Care Institute
Partners: Geri Olympics USA, Golden Living, West Virginia Bureau of Senior Services, Marshall University, School of Physiotherapy
PI: Lynne Baillie
Co-I: Stephen Uzor
Project Running from: May 2016- Apr 2017


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Novel LED-Based Indoor Tracking of People with Dementia

The population of Scotland is rapidly aging and, within the group of over 65s, there are over 83,000 people with dementia, according to Alzheimer Scotland. It is estimated that currently 44 million people have dementia worldwide, with the total set to increase to 76 million by 2030, according to Alzheimer’s Society in the UK. They also estimate that dementia costs the UK economy £23bn a year which is more than cancer, stroke and heart disease combined.

One of the prominent problems for people with dementia is that they can get lost or become disorientated. For this reason, GPS trackers are used to locate patients. However, accurate location and indoor tracking is not feasible with GPS-based systems since satellite signals cannot work indoors nor do they offer sub-meter accuracy.  The main challenge, therefore, is to achieve monitoring from a remote location of the status of people with dementia in terms of motion behavioural status in real-time. Knowing the accurate position of the person with dementia is therefore not only important for safety, but also for classification of ‘episodes’ of dementia for better management of the condition. Current techniques for indoor tracking use RF (radio frequency) sensors and these are not accurate enough, as they usually provide accuracy in the order of several meters. Hybrid systems which have additional sensors such as accelerometers and heading sensors offer somewhat improved accuracy at the expense of additional complexity since all these multiple sensors are then required to work in a coordinated fashion.

We propose a solution that is: lightweight, easy to install, relatively cheap to deploy and run. Our solution utilises LED (Light Emitting Diode) lighting, which is currently being installed in many homes due to its rapid drop in price, excellent colour rendering, LED longevity and LED energy efficiency. The solution works by utilising the fact that LEDs blink rapidly (this is imperceptible to a human eye) and that this blinking can be modulated to transmit data in the range of tens of megabits per second.  We therefore propose using the LEDs to convey position information to a sensor located on the person.  Due to the propagation of light properties, it is possible to achieve better precision in localization than with RF e.g. centimetres rather than metres.

Project Leader: Sinan Sinanovic

Project Researchers: Lynne Baillie, Wasiu Popoola, Roberto Ramirez Iniguez, Funmilayo Ogunkoya

Project Collaborators: North Glasgow Homes Housing Association

Project Funder: Digital Health Institute


Falls Smart Insole Project

Prof Lynne Baillie and Dr Philip Smit are developing an innovative insole device which uses sensors to establish if older people are at risk of falls.

Nearly half of people over 65 have a fall, and around 400,000 people over the age of 75 will have to go to hospital as a result of a fall every year, with huge costs to healthcare services estimated at 2 billion a year in the UK. Many elderly people who have suffered a fall are scared of further injury and stop taking exercise that might help them remain healthy and active.

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Gait analysis - the study of human motion - is currently the primary method of assessing the risk of falls by an elderly person. Balance and gait disturbances act as a good indicator of the risk of falls. However, gait analysis can usually only be conducted in research environments, which include 3D motion capture, ultrasound techniques, force and pressure analysis, and metabolic and physical activity monitoring.

The researchers are developing a prototype insole which can be worn on the foot and which can measure the force and movement of a person walking, capturing data within a normal living environment. The data from the sensors will be saved to memory embedded within the insole.

Poor balance and gait are treatable through exercise programmes, so researchers believe the insoles will help people who have already had a fall to readjust their walking patterns. The insoles may also be used by physiotherapists, GPs and other healthcare providers to measure risk of falls and proactively prevent falls in elderly people.

This research has also featured on the Times:


Project Leader: Lynne Baillie
Project Researchers: Dr Philip Smit & Prof Dawn Skelton
Project Funder: Digital Health Institutedhci main col logo rgb

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