REST: Reconfigurable lower limb Exoskeleton for effective Stroke Treatment in residential settings
- Start date: 1 April 2019
- End date: 31 March 2022
- Funder: EPSRC
- Value: £1,065,414
- Partners and collaborators: Tongji University Hospital (Collaborator), Steeper Group (Collaboration), Leeds Teaching Hospitals NHS Trust, United Kingdom (Collaborator), Device for Dignity MedTech Co-operative (Collaborator), King's College London, United Kingdom (Collaborator), DIH Technologies (Project Partner), AiTreat Pte Ltd (Project Partner).
- Primary investigator: Professor Shane Xie
- Co-investigators: Dr Zhi-Qiang Zhang
- External co-investigators: Professor J Dai (Kings College London), Professor Rory James O'Connor, Professor Abbas Ali Dehghani-Sanij
According to the UK Guidelines for stroke rehabilitation, the national standard for stroke rehabilitation is at least 45 minutes per day of each relevant therapy for a minimum of 5 days per week to people who have the ability to participate. However, this standard has never been met due to the decreasing availability of rehabilitation services and increasing pressures on the NHS.
In the UK, over 600,000 people with stroke live further than 20km from a stroke support group, the majority of whom live with severe mobility issues. It would be very challenging and costly, or even impossible for them to travel and receive rehabilitation treatments regularly in hospitals or rehabilitation centres. The NHS Five Year Forward View therefore made recommendations in 2017 to bring rehabilitation to people in their own homes and care homes.
People with stroke commonly experience post-stroke movement disorders, particularly weakness, disordered movement patterns, including post-stroke dystonia and spasticity. The majority of stroke patients are disabled and dependent on their family members or others for some or all of their daily living activities. Leveraging our previous success in robotic exoskeletons, our ambition is to deliver innovative rehabilitation through exoskeletons that are modular and reconfigurable to meet individual needs, and have the required intelligence to monitor recovery, personalise treatments and deliver effective rehabilitation in patients' own homes.
We will pursue this goal by:
1) introducing new soft muscles and novel reconfigurable robotic mechanisms for the lower limb exoskeletons, enabling them for home rehabilitation use and easy to manufacture, maintain and repair;
2) developing standardised exercise programmes, with innovative disability assessment methods and intelligent personalised treatment strategies. The intelligent lower limb exoskeleton controller will learn the patients' recovery status and continually update the rehabilitation strategy to meet the patients' changing needs and deliver the best possible outcome. Personalised treatment methods will be investigated to enable adaptive rehabilitation training for patients in their own homes;
3) evaluating the functionality, acceptability, robustness, reliability and sustainability of the robotic exoskeletons, initially in laboratory settings, and then in the Leeds Teaching Hospital rehabilitation service and residential settings; and
4) assembling the required pre-clinical documentation to initiate future clinical trials.
Our long-term goal is to develop a nationwide robot-assisted home-based rehabilitation programme, which builds upon the technology and the experimental evidence originated from this proposal. Our project partners Devices for Dignity (D4D), Steeper Group, DIH/Hocoma, AiTreat and the National Demonstration Centre for Rehabilitation at Leeds Teaching Hospital NHS Trust will provide adequate links and resources for this project. This project will establish a transferable technology for stroke survivors' rehabilitation at home, with a potential impact on millions of people in the UK and worldwide.
Stroke remains a significant societal challenge in the UK and around the world. The potential benefit from home-based rehabilitation interventions is substantial, especially when set against the annual UK cost of stroke of £9 billion. It is also predicted that there will be a 59% rise in the number of people suffering a stroke over the next 20 years, imposing unprecedented pressure on the NHS.
This project aims to develop distinctive science and technology for enhancing the outcome and effectiveness of robot-assisted treatment in residential settings, it aligns perfectly with the NHS Five Year Forward View in 2017 to bring rehabilitation to people in their own homes and care homes. In addition to the academic impact, other beneficiaries of this project will include: Economic benefit and emerging industries: Robotics and Autonomous Systems (RAS) is one of the 'Eight Great Technologies' identified by the UK Government in 2012.
The government also recognises the need to build on the local and national investment to support this technology and to raise the profile internationally of the UK's world-class position in robotics. It has also confirmed that it will implement the creation of a Robotics and Autonomous Systems Leadership Council to enable industry, academia and government to collaborate on the planning and execution of the RAS strategy in 2015.
It is predicted that RAS technologies can have a potential global economic impact of $1.9 to $6.4 trillion by 2025, via increasing the UK's health care productivity and reducing the total expenditure on long term care requirements of the UK's ageing population. It is predicted that RAS technologies could have a potential global economic impact of $1.9 to $6.4 trillion by 2025, while rehabilitation robot market size at $43.3 million in 2014 is expected grow dramatically to reach $1.8 billion by 2020.
The REST project will capture and expand the current rehabilitation robotics market by providing solutions for stroke treatment in residential settings. It will contribute to the UK's ambition to become the world leader in Robotics and Autonomous Systems, and enhance the competitiveness of related healthcare industries, especially home and community based healthcare.
Societal benefit: The most important beneficiaries of this research will be the huge number of people with stroke and their families. Currently, there are over 1.2M living with stroke in the UK with 152,000 people sustaining a new stroke each year. It is predicted that there will be a 59% rise in the number of people living with a stroke over the next 20 years.
Individual participants will benefit through their engagement in this project, which is an acknowledged benefit of participation in healthcare research, whether through the focus groups or the pilot studies. The impact on the wider community of people with stroke will be immense, with the ability to perform increased rehabilitation at home leading to improved outcomes and potentially earlier discharge from hospital, with benefits to the wider health and social care.
Stroke patients' families will have less pressure to provide day-to-day care, reducing their burden to provide adequate care, which will result in further social care benefits. Although the project is a direct response to the World Health Organization's appeal on "Stroke: a global response is needed for stroke", the outcome of the project is not limited to stroke. It will benefit hundreds of millions of patients with lower limb disability in the UK and worldwide, including individuals with brain and spinal cord injury, people with multiple sclerosis, survivors of polio infections and children with cerebral palsy.
We have designed a comprehensive program of dissemination across a wide range of platforms promoting cross-disciplinary knowledge exchange, such as patient engagement, public awareness, academic dissemination and IP translation. Please refer to Pathway to Impact for more details.
Publications and outputs
Abass, Z., Meng, W., Xie, S.Q. and Zhang, Z., 2019. A Robust, Practical Upper Limb Electromyography Interface Using Dry 3D Printed Electrodes. In 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 453-458). IEEE. Ai, Q., Ke, D., Zuo, J., Meng, W., Liu, Q., Zhang, Z. and Xie, S.Q., 2019.
High-order model-free adaptive iterative learning control of pneumatic artificial muscle with enhanced convergence. IEEE Transactions on Industrial Electronics. Li, Z., Tnunay, H., Zhao, S., Meng, W., Xie, S.Q. and Ding, Z., 2020.
Bearing-Only Formation Control With Prespecified Convergence Time. IEEE Transactions on Cybernetics. Li, Z., Wu, Z., Li, Z. and Ding, Z., 2019.
Distributed Optimal Coordination for Heterogeneous Linear Multiagent Systems With Event-Triggered Mechanisms. IEEE Transactions on Automatic Control, 65(4), pp.1763-1770. Liu, Q., Zuo, J., Zhu, C. and Xie, S.Q., 2020.
Design and control of soft rehabilitation robots actuated by pneumatic muscles: State of the art. Future Generation Computer Systems, 113, pp.620-634. Liu, Y., Liu, J., Ai, L., Wei, Q., Liu, Q. and Xie, S., 2019, July.
Objective Evaluation of Hand ROM and Motion Quality based on Motion Capture and Brunnstrom Scale. In 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 441-446). IEEE. Liu, Z., Ai, Q., Liu, Y., Zuo, J., Zhang, X., Meng, W. and Xie, S., 2019, July.
An Optimal Motion Planning Method of 7-DOF Robotic Arm for Upper Limb Movement Assistance. In 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 277-282). IEEE. Wu, L., Xie, S., Zhang, Z. and Meng, W., 2019, July.
Energy Efficiency of Gait Rehabilitation Robot: A Review. In 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) (pp. 465-470). IEEE. Zhang, Y., Xie, S.Q., Wang, H., Yu, Z. and Zhang, Z., 2020.
Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review. IEEE Sensors Journal. Zhao, Y., Zhang, Z., Li, Z., Yang, Z., Dehghani-Sanij, A.A. and Xie, S.Q., 2020.
An EMG-driven musculoskeletal model for estimating continuous wrist motion. IEEE Transactions on Neural Systems and Rehabilitation Engineering: a Publication of the IEEE Engineering in Medicine and Biology Society. Bao, T., Zaidi, S.A.R., Xie, S., Yang, P. and Zhang, Z.Q., 2020.
A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography. IEEE Transactions on Instrumentation and Measurement.