Periodic Reporting for period 2 - PRO GAIT (Physiological and Rehabilitation Outcomes: Gains from Automated Interventions in stroke Therapy (PRO GAIT))
Période du rapport: 2020-03-01 au 2023-02-28
THE PROBLEM
Robotic walking devices are helpful after stroke to promote gait restoration. At present these devices provide assistance levels not well matched to the person's ability or intention to move.
Responsive robots are required to advance stroke robotic rehabilitation. The PROGAIT study collects brain and muscle activity during robotic walking to better understand human machine interaction and to improve synchronisation between robot and person during gait. Allowing successful, repetitive task practice initiated and driven by the user to their maximum capability aligns with motor learning principles and neuroplastic drivers.
SOCIETAL IMPACT
PROGAIT makes an important societal contribution. Stroke is the leading cause of adult acquired disability; 3months after stroke 20% remain wheelchair dependent and 70% walk with reduced capacity resulting in stroke burden of 1.156 DALYs (Disability-Adjusted Life Years)/100.000 European disability-related care costs of €15.9 billion and productivity losses estimated at €4 billion.
Stroke incidence is estimated to increase by 59% by 2030, a projection that could overwhelm services. Urgent strategies are required to meet escalating rehabilitation demands. Robotic assisted gait training (RAGT) can deliver intensity of practice with less human resources but requires developments, proposed in PROGAIT, to move from a passive therapy. While current deployment of RAGT improves the likelihood of walking independently after stroke, new knowledge in intent controlled RAGT could further restore efficient gait patterns, enabling more people to return home and contribute to society.
OVERALL OBJECTIVES
1. establish the current state of the art in integration of neural biosignals with robotic gait devices towards a responsive robotic gait training after stroke
2. identify the acceptability and feasibility of robotic gait training in acute stroke and to explore if capturing electrical muscle activity levels during robotic gait training is useful and identifies optimal settings
3. collect electrical activity at brain level and muscle level during robotic walking after stroke and in healthy individuals to create a large data set
4. use the data set to identify if predictive modelling of gait is possible
5. explore the feasibility of using electrical biosignals to direct RAGT
Robotic walking devices are helpful after stroke to promote gait restoration. At present these devices provide assistance levels not well matched to the person's ability or intention to move.
Responsive robots are required to advance stroke robotic rehabilitation. The PROGAIT study collects brain and muscle activity during robotic walking to better understand human machine interaction and to improve synchronisation between robot and person during gait. Allowing successful, repetitive task practice initiated and driven by the user to their maximum capability aligns with motor learning principles and neuroplastic drivers.
SOCIETAL IMPACT
PROGAIT makes an important societal contribution. Stroke is the leading cause of adult acquired disability; 3months after stroke 20% remain wheelchair dependent and 70% walk with reduced capacity resulting in stroke burden of 1.156 DALYs (Disability-Adjusted Life Years)/100.000 European disability-related care costs of €15.9 billion and productivity losses estimated at €4 billion.
Stroke incidence is estimated to increase by 59% by 2030, a projection that could overwhelm services. Urgent strategies are required to meet escalating rehabilitation demands. Robotic assisted gait training (RAGT) can deliver intensity of practice with less human resources but requires developments, proposed in PROGAIT, to move from a passive therapy. While current deployment of RAGT improves the likelihood of walking independently after stroke, new knowledge in intent controlled RAGT could further restore efficient gait patterns, enabling more people to return home and contribute to society.
OVERALL OBJECTIVES
1. establish the current state of the art in integration of neural biosignals with robotic gait devices towards a responsive robotic gait training after stroke
2. identify the acceptability and feasibility of robotic gait training in acute stroke and to explore if capturing electrical muscle activity levels during robotic gait training is useful and identifies optimal settings
3. collect electrical activity at brain level and muscle level during robotic walking after stroke and in healthy individuals to create a large data set
4. use the data set to identify if predictive modelling of gait is possible
5. explore the feasibility of using electrical biosignals to direct RAGT
A systematic review of state of the art integration of neural biosignals during robotic assisted gait training (RAGT) after stroke has been completed and a checklist developed to guide more uniform data collection. Open access publication: https://pure.ulster.ac.uk/ws/portalfiles/portal/79628365/DCoyle_OA_Frontiers.pdf
Early laboratory work examined EEG data (N=10 healthy individuals) during stepping for 3D motion trajectory prediction. Accuracy predicting movement in the forward plane was established; testing of the GTec 32 channel GNautilus wireless device observed low artefact.
Clinical work in acute stroke exoskeleton training (N=10 individuals). Differences in EMG signals in 4 lower-limb muscle groups during different RAGT modes were evident; adaptive assistance showed better muscle activity in comparison to a passive mode.
Qualitative data (N=8 ) explored experiences of RAGT in acute/subacute stroke where RAGT was considered acceptable; participants noted they were anxious starting RAGT but there was nothing to fear and in many cases allowed them to walk when they could not otherwise. Negative features included the device weight and the time to measure and first set up.
Data (N=3 healthy; N=3 stroke) co-registered 32 channel EEG with EMG during RAGT walking in different modes and during overground walking without an exoskeleton. Preliminary analysis identifies that classifying stepping by EEG is better during robotic walking that overground walking.
ERSP and Cortico-Muscular Coherence (CMC) neurophysiological biomarkers showed modulation in healthy individuals during RAGT represented in high θ-desynchronization, undetected in stroke during RAGT. Reduced swing time observed during exoskeleton walking in stroke was not correlated with an increment of θ-desynchronization and/or β-CMC.
Co-registered EMG and EEG collected during RAGT (transparent, adaptive and full assistive modes) and non-assisted gait in healthy (N=10) and neurological patients (N=3)identified that irrespective of the exoskeleton mode, stronger modulation of central mid-line mu (8–13 Hz) and low-beta (14–20 Hz) rhythms compare with free overground walking, with no significant differences by assistance levels during RAGT observed. Implementation of gait decoders, based on deep neural networks trained on the EEG data during the different walking conditions, demonstrated all decoders achieved an average accuracy of 84% in predicting swing and stance phases. In addition, the decoder trained on transparent mode exoskeleton data predicted gait phases during adaptive and full modes with 78% accuracy, while the free overground walking decoder failed to decode gait during exoskeleton walking (accuracy 59%). This work validated the new 64 channel GNautilus wireless device.
A closed loop BCI robotic gait device was explored with partners EKSObionics, centred on a data portal allowing external commands to the operating system, in addition to a new hardware and software integration platform using Matlab Simulink for real time EEG processing (GTec neurotechnology), translation of neural data into exoskeleton actuate step commands through sensorimotor modulation alone. Trained in a healthy individual, a BCI classification system was deployed in real time RAGT.
Dissemination activities have included: three publications, 2 preprint publications, and one paper under peer review at project completion; conference presentations, outreach activities, show case events and an online research symposium.
Early laboratory work examined EEG data (N=10 healthy individuals) during stepping for 3D motion trajectory prediction. Accuracy predicting movement in the forward plane was established; testing of the GTec 32 channel GNautilus wireless device observed low artefact.
Clinical work in acute stroke exoskeleton training (N=10 individuals). Differences in EMG signals in 4 lower-limb muscle groups during different RAGT modes were evident; adaptive assistance showed better muscle activity in comparison to a passive mode.
Qualitative data (N=8 ) explored experiences of RAGT in acute/subacute stroke where RAGT was considered acceptable; participants noted they were anxious starting RAGT but there was nothing to fear and in many cases allowed them to walk when they could not otherwise. Negative features included the device weight and the time to measure and first set up.
Data (N=3 healthy; N=3 stroke) co-registered 32 channel EEG with EMG during RAGT walking in different modes and during overground walking without an exoskeleton. Preliminary analysis identifies that classifying stepping by EEG is better during robotic walking that overground walking.
ERSP and Cortico-Muscular Coherence (CMC) neurophysiological biomarkers showed modulation in healthy individuals during RAGT represented in high θ-desynchronization, undetected in stroke during RAGT. Reduced swing time observed during exoskeleton walking in stroke was not correlated with an increment of θ-desynchronization and/or β-CMC.
Co-registered EMG and EEG collected during RAGT (transparent, adaptive and full assistive modes) and non-assisted gait in healthy (N=10) and neurological patients (N=3)identified that irrespective of the exoskeleton mode, stronger modulation of central mid-line mu (8–13 Hz) and low-beta (14–20 Hz) rhythms compare with free overground walking, with no significant differences by assistance levels during RAGT observed. Implementation of gait decoders, based on deep neural networks trained on the EEG data during the different walking conditions, demonstrated all decoders achieved an average accuracy of 84% in predicting swing and stance phases. In addition, the decoder trained on transparent mode exoskeleton data predicted gait phases during adaptive and full modes with 78% accuracy, while the free overground walking decoder failed to decode gait during exoskeleton walking (accuracy 59%). This work validated the new 64 channel GNautilus wireless device.
A closed loop BCI robotic gait device was explored with partners EKSObionics, centred on a data portal allowing external commands to the operating system, in addition to a new hardware and software integration platform using Matlab Simulink for real time EEG processing (GTec neurotechnology), translation of neural data into exoskeleton actuate step commands through sensorimotor modulation alone. Trained in a healthy individual, a BCI classification system was deployed in real time RAGT.
Dissemination activities have included: three publications, 2 preprint publications, and one paper under peer review at project completion; conference presentations, outreach activities, show case events and an online research symposium.
The project developed and strengthened an international and interdisciplinary consortium who have made 5 grant applications at EU/national levels based on project data. Science Foundation Ireland funding has been secured to support further research, with 2 phD students and one post-doctoral researcher employed.
Career development of academic partners and secondees within or without their organisations and sectors has occured.
Broad dissemination has targeted audiences including public (e.g. EU researcher evenings) clinicians, and scientists (e.g. conferences and publications).
This project contributed to innovative developments in automated therapy for gait restoration post stroke, including: the first detailed health technology assessment of biosignal enhanced robotic gait training post stroke; first endusers’ perspectives of robotic gait in acute stroke; motion trajectory prediction of overground stepping by EEG; co-registration of EMG and EEG during robotic walking (sample N=3 healthy and N=3 with stroke) previously not captured overground; Human machine interaction insights during different RAGT modes (N=10 healthy individuals) showing central mid-line mu (8–13 Hz) and low-beta (14–20 Hz) rhythm modulation and exploitation of deep neural networks trained with EEG data during the different walking conditions to decode gait, with accurate swing and stance phase prediction and exoskeleton transparent (no assistance) decoders predictive of gait phases during adaptive and full assistance modes. The project completed proof of concept of closed loop RAGT in healthy individuals.
Career development of academic partners and secondees within or without their organisations and sectors has occured.
Broad dissemination has targeted audiences including public (e.g. EU researcher evenings) clinicians, and scientists (e.g. conferences and publications).
This project contributed to innovative developments in automated therapy for gait restoration post stroke, including: the first detailed health technology assessment of biosignal enhanced robotic gait training post stroke; first endusers’ perspectives of robotic gait in acute stroke; motion trajectory prediction of overground stepping by EEG; co-registration of EMG and EEG during robotic walking (sample N=3 healthy and N=3 with stroke) previously not captured overground; Human machine interaction insights during different RAGT modes (N=10 healthy individuals) showing central mid-line mu (8–13 Hz) and low-beta (14–20 Hz) rhythm modulation and exploitation of deep neural networks trained with EEG data during the different walking conditions to decode gait, with accurate swing and stance phase prediction and exoskeleton transparent (no assistance) decoders predictive of gait phases during adaptive and full assistance modes. The project completed proof of concept of closed loop RAGT in healthy individuals.