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Augmented Reality platform for mobility of Parkinson's patients

SMARTAR: Augmented Reality platform for increasing mobility and independence of Parkinson's patients


Parkinson's disease is a progressive neurological condition that affects over 6 million people worldwide. Half of Parkinson patients suffer from a condition called Freezing of Gait (FOG), where they feel as if their feet are "glued" to the ground. This sensation can occur when you start to walk or while walking and may last for several seconds to minutes. In the UK there are 145,000 people with Parkinson's and over 72,000 people who suffer from Freezing of Gait. FOG not only contributes to falls and related injuries, but also compromises quality of life as people often avoid engaging in functional daily activities both inside and outside the home.


This project focused on developing SMARTAR -- Augmented Reality platform for increasing the mobility and independence of Parkinson's disease patients. SMARTAR is an Augmented Reality Glasses that, through the use of sensors, will monitor a person's gait, detecting if a freezing incident occurs. It will then use proven techniques of giving the user a visual focus point of parallel lines on the ground to "step over". 


This method has been proven to be the best solution to overcoming Freezing. SMARTAR is a portable solution that works both inside and outside, allowing the user to keep their mobility and to be more independent and less reliant on family members or caretakers. Other solutions addressing freezing in Parkinson patients require the user to turn them on/off or to always be on. They can also be very noticeable, drawing unwanted attention to the person with Parkinson's.

Brunel Innovation Centre's Role

The main challenge addressed by Brunel University was to develop a suite of methodologies and algorithms for gait characterisation in Parkinson's patients. Brunel has successfully delivered:

- Deep learning models with variable computational requirements for maximum compatibility with multiple devices for gait characterisation

- Deep learning models with variable computational requirements for maximum compatibility with multiple devices for action characterisation (walking, standing, sitting etc)

- A novel methodology for automatic retraining to allow fast and easy calibration to new patients by using 5 minutes of data for a new patient. Gait analysis algorithms have been developed in the past using similar sensors, but those sensors were attached to arms, legs and waist, making them invasive and disruptive to everyday life.

Project Partners


The Imagination Factory

Brunel University London

Meet the Principal Investigator(s) for the project

Professor Tat-Hean Gan
Professor Tat-Hean Gan - Professional Qualifications CEng. IntPE (UK), Eur Ing BEng (Hons) Electrical and Electronics Engg (Uni of Nottingham) MSc in Advanced Mechanical Engineering (University of Warwick) MBA in International Business (University of Birmingham) PhD in Engineering (University of Warwick) Languages English, Malaysian, Mandarin, Cantonese Professional Bodies Fellow of the British Institute of NDT Fellow of the Institute of Engineering and Technology Tat-Hean Gan has 10 years of experience in Non-Destructive Testing (NDT), Structural Health Monitoring (SHM) and Condition Monitoring of rotating machineries in various industries namely nuclear, renewable energy (eg Wind, Wave ad Tidal), Oil and Gas, Petrochemical, Construction and Infrastructure, Aerospace and Automotive. He is the Director of BIC, leading activities varying from Research and development to commercialisation in the areas of novel technique development, sensor applications, signal and image processing, numerical modelling and electronics hardware. His experience is also in Collaborative funding (EC FP7 and UK TSB), project management and technology commercialisation.

Related Research Group(s)

woman engineer

Brunel Innovation Centre - A world-class research and technology centre that sits between the knowledge base and industry.

Partnering with confidence

UKRI Research England

Organisations interested in our research can partner with us with confidence backed by an external and independent benchmark: The Knowledge Exchange Framework. Read more.

Project last modified 12/10/2023