A Multimodal Deep Learning Based Approach for the Early Detection of Neurodegenerative Diseases
Lead Research Organisation:
University of Bristol
Department Name: Aerospace Engineering
Abstract
Project description:
Across industrialised nations, the population is aging. By 2050 individuals over the age of 65 will comprise up to 30% of the population of countries such as Italy and Germany. Increased age is associated with an increase in chronic diseases, including neurodegenerative diseases (ND). In the USA there will be an estimated 68% increase in the incidence of Parkinson's disease between 2010-2030 (630,000 increasing to 1.06 million) [1], and 40% for Alzheimer's (increasing from 5 million to 7.1 million) [2]. In-home healthcare platforms (e.g. SPHERE) could provide a method of monitoring the health of users, particularly for such diseases which typically have effects on human motion (e.g. gait, eye movement [5]). Such monitoring devices (e.g. RGB-D cameras) could potentially detect early signs of such ND, or help in monitoring their progression - perhaps aiding healthcare services in giving more timely and targeted medical assistance. The project aims to address three main questions:
"Can generative adversarial networks (GANs) be used to effectively generate realistic, varied data that can subsequently be employed to categorise individuals as healthy or unhealthy with regards to neurodegeneration?"
"What types of sensors (e.g. cameras, gyroscopes, inertial measurement units (IMUs), pressure mats) and data (e.g. gait and body motion, eye movements) are most informative and complementary for the consistent early detection of ND, and how can this data be most effectively gathered with respect to experiments?"
"What experiments should be designed to simulate early signs of ND, and how should different machine learning methods be designed and validated when trained with the data from this experiments?"
Project aim:
Develop a multimodal deep learning architecture capable of analysing the healthiness of a human being using data acquired from their gait, eye movement, and cognitive impairment.
o Collect multi-modal data to establish what features are the best to use in this architecture to provide an early diagnosis of a specific ND
Across industrialised nations, the population is aging. By 2050 individuals over the age of 65 will comprise up to 30% of the population of countries such as Italy and Germany. Increased age is associated with an increase in chronic diseases, including neurodegenerative diseases (ND). In the USA there will be an estimated 68% increase in the incidence of Parkinson's disease between 2010-2030 (630,000 increasing to 1.06 million) [1], and 40% for Alzheimer's (increasing from 5 million to 7.1 million) [2]. In-home healthcare platforms (e.g. SPHERE) could provide a method of monitoring the health of users, particularly for such diseases which typically have effects on human motion (e.g. gait, eye movement [5]). Such monitoring devices (e.g. RGB-D cameras) could potentially detect early signs of such ND, or help in monitoring their progression - perhaps aiding healthcare services in giving more timely and targeted medical assistance. The project aims to address three main questions:
"Can generative adversarial networks (GANs) be used to effectively generate realistic, varied data that can subsequently be employed to categorise individuals as healthy or unhealthy with regards to neurodegeneration?"
"What types of sensors (e.g. cameras, gyroscopes, inertial measurement units (IMUs), pressure mats) and data (e.g. gait and body motion, eye movements) are most informative and complementary for the consistent early detection of ND, and how can this data be most effectively gathered with respect to experiments?"
"What experiments should be designed to simulate early signs of ND, and how should different machine learning methods be designed and validated when trained with the data from this experiments?"
Project aim:
Develop a multimodal deep learning architecture capable of analysing the healthiness of a human being using data acquired from their gait, eye movement, and cognitive impairment.
o Collect multi-modal data to establish what features are the best to use in this architecture to provide an early diagnosis of a specific ND
Planned Impact
Rapid growth in the already burgeoning Robotics and Autonomous Systems (RAS) market has been estimated from many sources. This growth is driven by socio-economic needs and enabled by advances in algorithms and technologies converging on robotics. The market potential for applications of robotics and autonomous systems is, therefore, of huge value to the UK. There are four major areas where FARSCOPE will strive to fulfil and deliver on the impact agenda.
1. Training: A coherent strategy for impact must observe the value of the 'innovation pipeline'; from training of world-class researchers to novel products in the 'shop window'. The FARSCOPE training programme described in the Case for Support will produce researchers who will be able to advance knowledge, expertise and skills in the many associated aspects of academic pursuit in the field. Crucially, they will be guided by its industrial partners and BRL's Industrial Advisory Group, so that they are grounded in the real-world context of the many robotics and autonomous systems application domains. This means pursuing research excellence while embracing the challenges set within the context of a range of real-world factors.
2. Economic and Social Exploitation: The elevated position of advanced robotics, in the commercial 'value chain', makes it imperative that we create graduates from our Centre who are acutely aware of this potential. BRL is centrally engaged in its regional SME and business ecology, as evidenced by its recent industry workshop and 'open lab' events, which attracted some 60 and 280 industrial delegates respectively. BRL is also a key contributor to regional economic innovation. BRL has engaged two business managers and allocated some dedicated space to specifically support work on RAS related industrial engagement and innovation and, importantly, technology incubation. BRL will be creating an EU-funded Robotics Innovation Facilities to help coordinating a EUR 20m a programme to specifically promote and encourage direct links between academia and industry with a focus on SMEs. All of these high-impact BRL activities will be fed directly into the FARSCOPE programme.
A critical mass of key industrial and end-user partnerships across a diverse array of sectors have given their support to the FARSCOPE centre. All have indicated their interest in engaging through the FARSCOPE mechanisms identified in the Case for Support. These demonstrate the impact of the FARSCOPE centre in engaging existing, and forming new, strategic partnerships in the RAS field.
3. Fostering links with other Research Institutions and Academic Dissemination: It is essential that FARSCOPE CDT students learn to share best practice with other RAS research centres, both in the UK and beyond. In addition to attendance and presentation at academic conferences nationally and overseas, FARSCOPE will use the following mechanisms to engage with the academic community. BRL has very many strong links with the UK, EU and global RAS research community. We will use these as a basis for cementing existing links and fostering new ones.
4. Engaging the Public: FARSCOPE will train and then encourage its student cohorts to engage with the general public, to educate about the potential of these new technologies, to participate in debates on ethics, safety and legality of autonomous systems, and to enthuse future generations to work in this exciting area. UWE and the University of Bristol, BRL's two supporting institutions, host the National Coordinating Centre for Public Engagement. In addition, UWE's Science Communication Unit is internationally renowned for its diverse and innovative activities, which engage the public with science. FARSCOPE students will receive guidance and training in public engagement in order to act as worthy RAS research 'ambassadors'.
1. Training: A coherent strategy for impact must observe the value of the 'innovation pipeline'; from training of world-class researchers to novel products in the 'shop window'. The FARSCOPE training programme described in the Case for Support will produce researchers who will be able to advance knowledge, expertise and skills in the many associated aspects of academic pursuit in the field. Crucially, they will be guided by its industrial partners and BRL's Industrial Advisory Group, so that they are grounded in the real-world context of the many robotics and autonomous systems application domains. This means pursuing research excellence while embracing the challenges set within the context of a range of real-world factors.
2. Economic and Social Exploitation: The elevated position of advanced robotics, in the commercial 'value chain', makes it imperative that we create graduates from our Centre who are acutely aware of this potential. BRL is centrally engaged in its regional SME and business ecology, as evidenced by its recent industry workshop and 'open lab' events, which attracted some 60 and 280 industrial delegates respectively. BRL is also a key contributor to regional economic innovation. BRL has engaged two business managers and allocated some dedicated space to specifically support work on RAS related industrial engagement and innovation and, importantly, technology incubation. BRL will be creating an EU-funded Robotics Innovation Facilities to help coordinating a EUR 20m a programme to specifically promote and encourage direct links between academia and industry with a focus on SMEs. All of these high-impact BRL activities will be fed directly into the FARSCOPE programme.
A critical mass of key industrial and end-user partnerships across a diverse array of sectors have given their support to the FARSCOPE centre. All have indicated their interest in engaging through the FARSCOPE mechanisms identified in the Case for Support. These demonstrate the impact of the FARSCOPE centre in engaging existing, and forming new, strategic partnerships in the RAS field.
3. Fostering links with other Research Institutions and Academic Dissemination: It is essential that FARSCOPE CDT students learn to share best practice with other RAS research centres, both in the UK and beyond. In addition to attendance and presentation at academic conferences nationally and overseas, FARSCOPE will use the following mechanisms to engage with the academic community. BRL has very many strong links with the UK, EU and global RAS research community. We will use these as a basis for cementing existing links and fostering new ones.
4. Engaging the Public: FARSCOPE will train and then encourage its student cohorts to engage with the general public, to educate about the potential of these new technologies, to participate in debates on ethics, safety and legality of autonomous systems, and to enthuse future generations to work in this exciting area. UWE and the University of Bristol, BRL's two supporting institutions, host the National Coordinating Centre for Public Engagement. In addition, UWE's Science Communication Unit is internationally renowned for its diverse and innovative activities, which engage the public with science. FARSCOPE students will receive guidance and training in public engagement in order to act as worthy RAS research 'ambassadors'.
