REST: Reconfigurable lower limb Exoskeleton for effective Stroke Treatment in residential settings
Lead Research Organisation:
University of Leeds
Department Name: Electronic and Electrical Engineering
Abstract
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.
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.
Planned Impact
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.
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.
Organisations
- University of Leeds (Lead Research Organisation)
- LEEDS TEACHING HOSPITALS NHS TRUST (Collaboration)
- Tongji University Hospital (Collaboration)
- Steeper Group (Collaboration)
- Device for Dignity MedTech Co-operative (Collaboration)
- KING'S COLLEGE LONDON (Collaboration)
- DIH Technologies (Project Partner)
- AiTreat Pte Ltd (Project Partner)
Publications
Ai Q
(2020)
High-Order Model-Free Adaptive Iterative Learning Control of Pneumatic Artificial Muscle With Enhanced Convergence
in IEEE Transactions on Industrial Electronics
Alzaid A
(2022)
Automatic detection and classification of peri-prosthetic femur fracture.
in International journal of computer assisted radiology and surgery
Bao T
(2022)
CNN Confidence Estimation for Rejection-Based Hand Gesture Classification in Myoelectric Control
in IEEE Transactions on Human-Machine Systems
Bao T
(2021)
Inter-Subject Domain Adaptation for CNN-Based Wrist Kinematics Estimation Using sEMG.
in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Bao T
(2023)
LSTM-AE for Domain Shift Quantification in Cross-Day Upper-Limb Motion Estimation Using Surface Electromyography
in IEEE Transactions on Neural Systems and Rehabilitation Engineering
Bao T
(2021)
A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography
in IEEE Transactions on Instrumentation and Measurement
Bao T
(2021)
A deep Kalman filter network for hand kinematics estimation using sEMG
in Pattern Recognition Letters
Bao T
(2022)
Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning-A Survey in Myoelectric Control.
in IEEE journal of biomedical and health informatics
Gao T
(2022)
CI-Net: a joint depth estimation and semantic segmentation network using contextual information
in Applied Intelligence
Ghaffar A
(2023)
Actuation system modelling and design optimization for an assistive exoskeleton for disabled and elderly with series and parallel elasticity.
in Technology and health care : official journal of the European Society for Engineering and Medicine
Ghaffar A
(2023)
Design optimization and redundant actuation selection for an efficient assistive robotic exoskeleton
in Journal of the Chinese Institute of Engineers
He C
(2021)
Iterative feedback tuning for optimal repetitive constraint-following control of uncertain mechanical systems using Udwadia-Kalaba theory
in Optimal Control Applications and Methods
Jiang L
(2022)
Fractional robust finite time control of four-wheel-steering mobile robots subject to serious time-varying perturbations
in Mechanism and Machine Theory
Jin L
(2022)
Flexible unimodal strain sensors for human motion detection and differentiation
in npj Flexible Electronics
Li Z
(2022)
Bearing-Only Formation Control With Prespecified Convergence Time.
in IEEE transactions on cybernetics
Li Z
(2020)
Distributed Optimal Coordination for Heterogeneous Linear Multiagent Systems With Event-Triggered Mechanisms
in IEEE Transactions on Automatic Control
Liu Q
(2022)
Design and Hierarchical Force-Position Control of Redundant Pneumatic Muscles-Cable-Driven Ankle Rehabilitation Robot
in IEEE Robotics and Automation Letters
Liu Q
(2021)
Path Planning and Impedance Control of a Soft Modular Exoskeleton for Coordinated Upper Limb Rehabilitation
in Frontiers in Neurorobotics
Liu Q
(2020)
Design and control of soft rehabilitation robots actuated by pneumatic muscles: State of the art
in Future Generation Computer Systems
Liu Z
(2021)
A highly sensitive stretchable strain sensor based on multi-functionalized fabric for respiration monitoring and identification
in Chemical Engineering Journal
Qian K
(2023)
Robust Iterative Learning Control for Pneumatic Muscle With Uncertainties and State Constraints
in IEEE Transactions on Industrial Electronics
Qian K
(2023)
Data-Driven Adaptive Iterative Learning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training
in IEEE Robotics and Automation Letters
Shi Y
(2024)
A Physics-Informed Low-Shot Adversarial Learning for sEMG-Based Estimation of Muscle Force and Joint Kinematics
in IEEE Journal of Biomedical and Health Informatics
Wang C
(2022)
Quantitative Elbow Spasticity Measurement Based on Muscle Activation Estimation Using Maximal Voluntary Contraction
in IEEE Transactions on Instrumentation and Measurement
Wang T
(2023)
Stiffness evaluation of a novel ankle rehabilitation exoskeleton with a type-variable constraint
in Mechanism and Machine Theory
Wang X
(2024)
Quantitative Upper Limb Impairment Assessment for Stroke Rehabilitation: A Review
in IEEE Sensors Journal
Zhang J
(2022)
Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer
in IEEE Transactions on Instrumentation and Measurement
Zhang J
(2022)
Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG.
in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Zhang J
(2023)
Toward Robust and Efficient Musculoskeletal Modeling Using Distributed Physics-Informed Deep Learning
in IEEE Transactions on Instrumentation and Measurement
Zhang Y
(2022)
Multi-Objective Optimization-Based High-Pass Spatial Filtering for SSVEP-Based Brain-Computer Interfaces
in IEEE Transactions on Instrumentation and Measurement
Zhang Y
(2021)
Data Analytics in Steady-State Visual Evoked Potential-Based Brain-Computer Interface: A Review
in IEEE Sensors Journal
Zhang Y
(2023)
Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-based BCIs.
in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Zhang Y
(2023)
SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation.
in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Zhang Y
(2021)
Multi-Objective Optimisation for SSVEP Detection
Zhang Y
(2023)
Bayesian-Based Classification Confidence Estimation for Enhancing SSVEP Detection
in IEEE Transactions on Instrumentation and Measurement
Zhao X
(2022)
Deep CNN model based on serial-parallel structure optimization for four-class motor imagery EEG classification
in Biomedical Signal Processing and Control
Zhao Y
(2020)
An EMG-Driven Musculoskeletal Model for Estimating Continuous Wrist Motion.
in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Zhao Y
(2023)
Adaptive Cooperative Control Strategy for a Wrist Exoskeleton Using Model-Based Joint Impedance Estimation
in IEEE/ASME Transactions on Mechatronics
Zhao Y
(2023)
An EMG-driven musculoskeletal model for estimation of wrist kinematics using mirrored bilateral movement
in Biomedical Signal Processing and Control
Zhao Y
(2023)
Computationally Efficient Personalized EMG-Driven Musculoskeletal Model of Wrist Joint
in IEEE Transactions on Instrumentation and Measurement
Description | Robot-assisted treatment strategy. We have developed a model-free adaptive iterative learning method to control the pneumatic muscles of an ankle rehabilitation exoskeleton. The pneumatic muscles work as actuators to enable the exoskeleton to provide rotational rehabilitation exercise. The pneumatic muscles can guarantee a safe patient-robot interaction due to its intrinsic compliance. However, the high nonlinearity of the pneumatic muscles makes the exoskeleton difficult to control. To solve this problem, we develop a pesudo-partial based updating law to estimate the dynamics of the pneumatic muscles in real-time according to the input-output data. Then an intelligent iterative learning algorithm is developed to estimate the patient-exoskeleton interaction force based on the estimated model. This approach is capable of regulating the assistance of exoskeleton so that the patient only get help when they cannot accomplish the rehabilitation tasks. To verify the approach we developed, a Labview based ankle rehabilitation testbed has been built. Our most recent results show that using this approach, the exoskeleton driven by pneumatic muscles can deliver more precise ankle rehabilitation exercise than the traditional method. |
Exploitation Route | This project will have a wide impact across robotics, industry, and health professions, due to its interdisciplinary nature. The outcome of this project is not limited to stroke rehabilitation. It will potentially benefit a lot of people with disabilities in the UK. In order to maximise the impact of this research, we design the following three stages to explore the best ways for clinical trials and commercialisation: a pre-clinical stage, a clinical trial stage, and a commercialisation stage. For the first stage, we will encourage end-users (stroke patients) to play an active role in our research and also engage in the programme of public engagement events. For the second stage, we will pursue external funding to support clinical trials from charitable foundations, and from research councils. For the third stage, with the help of our industrial partner, we will coordination with university expertise in the field of commercialization and support in research and innovation service. This will facilitate and expedite the transfer of the research outcome from theory to practice. |
Sectors | Digital/Communication/Information Technologies (including Software) Electronics Healthcare |
Description | A multi-modal sensing system with IMU, force, and EMG sensors has been developed and integrated into the knee exoskeleton prototype to measure knee kinematics, joint force, and muscle activations. A novel digital model has been proposed to estimate the stiffness of the joint using the spring-mass-damper model or the linear regression model. Different biomechanical modelling approaches are explored for data analysis, such as the musculoskeletal gait model to measure joint contact force, human exoskeleton interactive force model to measure interaction forces, and the finite element model to measure joint pressure and strain. AI technology, such as sensor fusion technology, will be developed to improve the accuracy of the joint contact force and joint stiffness estimation by combining data from multiple sensors and counting for the variability and complexity of human movement. Tests have been completed on five patients to evaluate the accuracy of the sensing system and the amount of assistance that the exoskeleton can provide. Comments to the knee exoskeletons have been collected as part of the PPI engagement activities. We have also recruited two PhD students to continue working on collecting and processing those data from this multi-modal system. |
Description | A Medtech online course on medical robotics |
Geographic Reach | Europe |
Policy Influence Type | Influenced training of practitioners or researchers |
Impact | The online course received very positive feedback in terms of the impact of new technologies on healthcare, the changes on policy and practice are still to be seen. |
Description | Develop a portable and affordable knee device to monitor the rehabilitation therapy for knee disease |
Amount | £17,354 (GBP) |
Organisation | Wellcome Trust |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 01/2021 |
End | 01/2022 |
Description | FREEpHRI: Flexible, Robust and Efficient physical Human-robot Interaction with iterative learning and self-triggered role adaption |
Amount | £331,094 (GBP) |
Funding ID | EP/V057782/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 04/2022 |
End | 01/2023 |
Title | A smart knee exoskeleton for assessment and rehabilitation treatment |
Description | The research tool includes two new systems: 1) A smart sensing system for assessing the functions of knee joint; and 2) A smart exoskeleton that can provide assist-as-needed control for walking and rehabilitation training. |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | The impact of the tools are: 1) the assessment tool provides the first kind of tool for evaluating the knee joint using a sensing system; 2) the exoskeleton system provides a tool to develop AI-based control strategies for personalised training. |
Title | An ankle biomechanical model for injury assessment |
Description | The model is able to assess ankle injury in real time with sensor measurements as inputs, the model was established in Matlab taking into consideration of the detailed biomechanics of the ankle joint. |
Type Of Material | Physiological assessment or outcome measure |
Year Produced | 2020 |
Provided To Others? | No |
Impact | The research tool is able to measure ankle injuries with data inputs from the sensors, the impact is that the tool is able to assess ankle injuries objectively. |
Description | Coordinate steering groups |
Organisation | Device for Dignity MedTech Co-operative |
Country | United Kingdom |
Sector | Charity/Non Profit |
PI Contribution | We collaborate with the steering group organized by Devices for Dignity to assess the regulatory requirements of rehabilitation technology we developed. |
Collaborator Contribution | Recruit stroke survivors to pilot test groups and provide specialists to make sure the developed system is compliant with assessment and trial requirement. |
Impact | A steering group including both stroke survivors, physiotherapists and multi-disciplinary researchers have been created. |
Start Year | 2019 |
Description | King' College London - design of reconfigurable ankle exokeleton |
Organisation | King's College London |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | We have provided the dynamics of our self-designed soft actuator. All these parameters will be used in the design process of the ankle exoskeleton prototypes. |
Collaborator Contribution | The King' College London has provided us with the 3D model of the ankle exoskeleton they designed. The model has 3 rotational degrees of freedom and the rotation centre of the exoskeleton is align with the rotation centre of the ankle joint. The model will be used to manufacture a prototype. |
Impact | A 3D model of ankle exoskeleton robots |
Start Year | 2019 |
Description | Patient Focus Group |
Organisation | Leeds Teaching Hospitals NHS Trust |
Country | United Kingdom |
Sector | Public |
PI Contribution | We have been working with physiotherapists from Leeds teaching hospital to transfer current clinical rehabilitation exercises to Labview-based library for robotic exosketon |
Collaborator Contribution | Leeds teaching hospital has provided physiotherapists to support the development of a rehabilitation exercise library for the robotic exoskeleton. |
Impact | No outputs to date. |
Start Year | 2019 |
Description | Steeper: the manufacturing support |
Organisation | Steeper Group |
Country | United Kingdom |
Sector | Hospitals |
PI Contribution | We have been working with the engineers from Steeper as collaborators providing then with intelligence about the new actuation and control techonologies arising from this project. |
Collaborator Contribution | Steeper Ltd has provided engineering support and access to the equipment facilities for the manufacturing of robotic orthosis. |
Impact | A knee exoskeleton prototype has been developed. |
Start Year | 2019 |
Description | WHO Tongji hospital, Huazhong University of Science and Technology, China |
Organisation | Tongji University Hospital |
Country | China |
Sector | Hospitals |
PI Contribution | The University of Leeds offers an ideal environment for prototyping of medical robots, with state-of-the-art technology and expertise in fabrication at the EPSRC National Facility for Innovative Robotic System (£5M). Long-established collaborations between the faculties of Engineering and Medicine and Health create a vibrant medical engineering community, capabilities for pre-clinical studies, support for large-scale clinical studies in the Leeds Clinical Trials Research Unit, extensive medical industry and healthcare community links, and expertise in medical device regulatory and commercialisation pathways at the EPSRC IKC in Medical Technologies. |
Collaborator Contribution | Tongji Hospital, affiliated to Tongji Medical College, Huazhong University of Science and Technology, China, is one of the best general hospital in China. The Department of Rehabilitation Medicine was one of the earliest departments of physical medicine and Rehabilitation in China. In 1990, it was recognized by the WHO as "the Collaborating Centre for Training and Research in Rehabilitation". The department is now equipped with the most advanced assessment and rehabilitation treatment equipment. The department also has a long history of collaboration with Asian governments (such as Hong Kong, Mongolia etc.) on stoke rehabilitation. |
Impact | The collaboration led to a network grant that was funded by the Academy of Medical Sciences Global Challenges Research Fund Networking Grant Scheme in 2019. |
Start Year | 2019 |
Title | An ankle exoskeleton |
Description | The exoskeleton is able to perform ankle rehabilitation exercises and measure the ankle joint with sensors. A control interface is developed so that the exoskeleton can interface with a user |
Type Of Technology | Physical Model/Kit |
Year Produced | 2021 |
Impact | The prototype will be further developed for clinical trials, the impact will be seen when the product is ready. |
Title | VR interface for ankle exoskeleton |
Description | A VR interface was developed to interface with the ankle exoskeleton so that users can perform ankle rehabilitation exercises while interacting with the VR games. The games are designed with different levels of difficulties for rehabilitation |
Type Of Technology | Software |
Year Produced | 2021 |
Impact | The prototype will be further developed for clinical trials, the impact will be seen when the product is ready. |
Description | 2nd consumer research advisory meeting |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Patients, carers and/or patient groups |
Results and Impact | 10 people including 2 physiotherapists and 3 stroke survivors have attended the meeting. The user requirement of the reconfigurable exoskeleton and other healthcare technology have been collected |
Year(s) Of Engagement Activity | 2019 |
Description | 2nd meet - REST research group |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Schools |
Results and Impact | 20 researchers from the University of Leeds, King's College London and Leeds Teaching Hospital meet together and present the recent technical progress of the project. The physiotherapists from Leed Teaching Hospital gave some feedback about the robot design. |
Year(s) Of Engagement Activity | 2019 |
Description | A talk or presentation - 3nd meet - REST research group |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Schools |
Results and Impact | 20 researchers from the University of Leeds, King's College London and Leeds Teaching Hospital meet together and present the recent technical progress of the project. The design of the robot and the development of assessment algorithms have been discussed. |
Year(s) Of Engagement Activity | 2021 |
Description | Consumer Research Advisory Group Meeting |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Patients, carers and/or patient groups |
Results and Impact | We co-organised a consumer research advisory group meeting including 3 physiotherapists and 4 stroke survivors at Leeds Centre for Integrated Living |
Year(s) Of Engagement Activity | 2019 |
Description | IEEE International Conference |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Industry/Business |
Results and Impact | 120 participants attended the conference including postgraduate researchers, academics and industrial businesses. |
Year(s) Of Engagement Activity | 2023 |
Description | Kick off meeting - REST research group |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Schools |
Results and Impact | 6 people from the University of Leeds, Leed teaching Hospital, and King's College London have been invited. The different research tasks have been allocated during the meeting. |
Year(s) Of Engagement Activity | 2019 |
Description | Patient focus meeting |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Patients, carers and/or patient groups |
Results and Impact | Around 5 patients and 2 health professionals have been invited to the focus group meeting. During the meeting, patients provide their requirements for the knee exoskeletons and give feedback about our exoskeleton prototype. |
Year(s) Of Engagement Activity | 2021 |
Description | Patient group workshop |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | The workshop includes 25 researchers (PhD, PDRA and Academics), 12 medical professionals and 5 patients, it includes presentations from researchers, demonstration of robots, interaction with patients and discussions. |
Year(s) Of Engagement Activity | 2023 |