Stroke is the third most common cause of death and the main cause of acquired adult disability in high-income countries. The most common deficit after stroke is motor impairment of the contralateral arm, with more than 80% of stroke survivors experiencing this condition in the acute phase, and only half regaining some useful upper limb function after six months.
Within the European project RETRAINER (grant agreement No 644721), the consortium developed a platform for the rehabilitation of the upper limb after stroke, which combines a passive arm exoskeleton for weight relief supporting both shoulder and elbow movements, Functional Electrical Stimulation (FES) of the two-most impaired muscles of the affected side, interactive objects, and voluntary effort. The system also provides a graphical user interface which helps the therapist set the training session and save the training data and parameters, and provides the subject a visual feedback about his/her active involvement in the exercise. The training consists of the execution of a series of exercises involving the affected arm during daily life activities. Typical exercises are anterior reaching on a plane or in the space, moving an object on a plane or in the space, moving the hand to the mouth, with or without an object in the hand, and lateral elevation of the shoulder.
The aim of this clinical study it to evaluate the efficacy of this novel training platform on patients between two weeks and nine months after their first stroke, who preserved at least a visible muscle contraction for the arm and shoulder muscles. Participants are randomized in an experimental and a control group. The control group is trained with an advanced rehabilitative program, including physical training, occupational therapy, FES, and virtual reality, while the experimental group is trained with the RETRAINER system for about 30 minutes, in addition to the same program of the control group. The daily training time is the same for the two groups. The intervention consists of three sessions a week for nine weeks. Patients are assessed at baseline, soon after the end of the intervention, and in a 4-week follow-up visits. It is planned to recruit 68 subjects for this study.
Since the RETRAINER platform was built on the up-to-date theory of motor re-learning, which supports task-oriented repetitive training, a close temporal association between motor intention and stimulated motor response, and an intensive and frequent training paradigm, the study's hypothesis is that the experimental group shows a greater treatment effect than the control group.
This is multi-center randomized controlled trial designed according to the CONSORT Statement recommendations. A total of 68 patients will be recruited in the two centers. This sample size was a-priori calculated as capable to detect a clinically important between-group difference of 5.7 points in the primary endpoint Action Research Arm Test, considering a standard deviation of 12.5, a type I error of 5%, and a power of 80%.
More technical details on the RETRAINER platform for the rehabilitation of the arm are here reported.
The experimental setup consists of a lightweight passive arm exoskeleton for weight compensation, a current-controlled stimulator with 2 channels of stimulation and 2 channels of EMG recordings developed by Hasomed GmbH, and interactive objects, which are daily life objects equipped with RFID (Radio Frequency Identification) tags used to identify the target positions so as to drive the execution of the rehabilitation exercises. A suitable reader is embedded in the exoskeleton with the antenna on the wrist joint. The control system is shared between an Embedded Control System (ECS), running on a BeagleBoneBlackTM, for real-time operation, and a Windows-based table (Microsoft Surface 3 running Windows 8), which provides a graphical user interface (GUI) for the therapist and the patient.
The exoskeleton is characterized by four degrees of freedom (DOFs): three of them, e.g. shoulder elevation, shoulder rotation in the transversal plane and elbow flex-extension, are equipped with angle sensors (Vert-X 13 E, ConTelec AG) to measure the position and electromagnetic brakes to avoid the fatiguing and unnecessary use of FES to hold a target position once reached. The additional DOF is provided by an inclination module, which enables the patient to move the trunk 20° forward without constriction. In addition to the 4 DOFs, the humeral rotation, the prono-supination as well as the length of the forearm and the upper arm can be adjusted at the beginning of the training session at subject-specific positions. The gravity compensation modules for upper arm and forearm consist of a carbon fiber-tube with springs inside whose pre-tension can be adjusted at the beginning of the training session in order to change the level of compensation. Thanks to the adjustability of the lengths and the level of compensation, the exoskeleton can fit and support patients within 5th and 95th female/male percentile. The exoskeleton can be mounted on the user's wheelchair or on a normal chair by means of a universal clamping mechanism which assures easy and stable mounting. The exoskeleton weights about 4kg plus 2kg for the clamping mechanism.
In addition to the support provided by the exoskeleton, EMG-triggered FES is delivered to two muscles, selected by the therapist based on the subject-specific needs. For each stimulated muscle, the residual volitional EMG signal is detected and used to trigger the onset of a predetermined stimulation sequence applied to the muscle itself. In case the muscle does not reach the pre-defined threshold, the stimulation sequence is automatically started after a time-out. EMG signals are acquired at 4kHz, the stimulation frequency is set at 25Hz, the pulse width is fixed at 300µs, while the stimulation intensity is set at the beginning of the training session on each muscle individually at a value tolerated by the subject and able to induce a functional movement. Separate EMG and stimulation (Pals® electrodes, Axelgaard Manufacturing Ltd) are placed over each muscle belly. When the stimulation starts, EMG signals are continuously measured in order to provide a visual feedback about the patient's volitional involvement at the end of the execution of each task. An adaptive linear prediction filter is used to estimate the volitional EMG during hybrid muscle contractions. If the mean value of the volitional EMG estimate during the stimulation phase is over a pre-defined threshold, a happy emoji is shown to the patient through the GUI; conversely, if it is below the pre-defined threshold a sad emoji is shown in order to promote the active involvement of the subject. A fast and automatic calibration procedure is required before the beginning of each session. This procedure aims at setting the current amplitude and the EMG threshold values. During the procedure the subject is asked to be relaxed. Specifically, three thresholds are set on each muscle: two of them are used to trigger the stimulation, one in case the muscle is activated as first and one in case the muscle is activated as second one; the third threshold is used to define the subject's active involvement in the task. The thresholds are defined as twice the mean volitional EMG during a phase of no stimulation (first threshold), during a phase of stimulation of the other muscle (second threshold), and during a phase of simultaneous stimulation of the two muscles (third threshold).
The control interface of the system, implemented in .Net 4.6, provides a GUI including multiple software tools to organize rehabilitation exercises and monitor rehabilitation progress. The heart of the control interface is a State Machine, which drives both the parameterization and the execution of the exercises. Each exercise is divided into single tasks: the State Machine drives the exercise execution throughout the tasks, while the execution of each single task is controlled by the ECS. The ECS controls all the modules requiring real time constraints, such as the stimulator, the FES controller and the exoskeleton sensors. To keep the control interface and the ESC synchronized, a strict master slave concept using a custom made communication protocol was implemented, meaning that the ECS must not act independently, but only reacts to commands sent by the high level control. Transitions between states of the state machine and thus tasks of the exercise are triggered by angle sensors data, RFID data or a timer (depending on the task). Transitions have to fulfill certain conditions, so called guards. These guards are predefined for each task and have to be parameterized as described in the Section D. The GUI guides the user through the training by providing visual instructions and feedback.
The workflow of a typical training session consists of four main phases: the setting, donning and parameterization of the system, and the training following a pre-defined sequence of exercises. The control interface supports the therapist and the patient throughout all the phases via the GUI.
The setting starts with the therapist creating a new user, or selecting an existent one, and selecting the exercises. Afterwards, the donning phase starts with the placement of the EMG and stimulation electrodes. Once the electrodes placement is checked, the therapist should adjust the exoskeleton lengths to fit with the patient and let the patient don the exoskeleton. The following step is the calibration of the FES controller by means of the automatic procedure previously described. The therapist sets the gravity compensation both at the arm and forearm level and saves the final exoskeleton settings. On the following training days, the setting and donning procedure is partly simplified since the therapist can load the settings of the previous day and eventually adjust them.
The parameterization step is designed to set the guards of the State Machine. In this process the GUI guides the patient and the therapist through each task of the selected exercises without stimulation. The patient-specific parameters for each task, such as the target positions, the desired time for the execution of each task, and the time of the relax phases, are determined. At the end of the parameterization phase, all the parameters are stored and the training session can start.
The training consists of the execution of a series of exercises involving the arm during daily life activities. Typical exercises are anterior reaching on a plane or in the space, moving an object on a plane or in the space, moving the hand to the mouth, with or without an object in the hand, and lateral elevation of the shoulder. The execution of the exercises is controlled by the control interface which leads the patient throughout the single tasks by means of both visual and audio messages via the GUI.
- Conventional therapy Other
Intervention Desc: It consists of a combination of different treatment modalities among the following, based on the petient's specific needs: Upper limb passive motion Arm Cycle ergometer with or without FES Neuro Muscular Electrical Stimulation Upper limb exercises using augmented or virtual reality environment Occupational therapy exercises Constraint induced movement therapy Upper limb active movement (reaching, grasping, elevation, spatial orientation) Repetitive task training Mirror therapy Writing training Chemodenervation Therapy ARM 1: Kind: Experimental Label: RETRAINER-S1 & Conventional Therapy Description: 27 sessions, 3 sessions per week for a total of 9 weeks. Each session consists of 30-minute training with the RETRAINER-S1 system plus 60 minutes of conventional therapy. The training session is customized on the patients' need and can be adapted to their improvement during the intervention. ARM 2: Kind: Experimental Label: Conventional Therapy Description: 27 sessions, 3 sessions per week for a total of 9 weeks. Each session lasts about 90 minutes and consists of different training modalities typically used in the rehabilitation of the arm after stroke. The training session is customized on the patients' need and can be adapted to their improvement during the intervention.
- RETRAINER-S1 Device
Intervention Desc: It consists of the execution of different exercises with the affected arm supported by the RETRAINER-S1 device. The subject is actively involved in the exercises and the system provides two types of support: weight relief and FES. The following exercises can be performed: anterior reaching (in a plane or in the space) lateral elevation of the arm hand to mouth movements with or without an object in the hand moving objects on a plane or in the space. A subset of exercises is defined based on the patient's capability and the training time is equally shared between the selected exercises. The different training sessions can include different exercises and a different number of repetition for each exercise. ARM 1: Kind: Experimental Label: RETRAINER-S1 & Conventional Therapy Description: 27 sessions, 3 sessions per week for a total of 9 weeks. Each session consists of 30-minute training with the RETRAINER-S1 system plus 60 minutes of conventional therapy. The training session is customized on the patients' need and can be adapted to their improvement during the intervention.
|Type||Measure||Time Frame||Safety Issue|
|Primary||Action Research Arm Test||9 weeks|
|Secondary||Medical Research Council||baseline; 9 weeks; 13 weeks|
|Secondary||Motricity index||baseline; 9 weeks; 13 weeks|
|Secondary||Motor Activity Log||baseline; 9 weeks; 13 weeks|
|Secondary||Box & Blocks Test||baseline; 9 weeks; 13 weeks|
|Secondary||Stroke Specific Quality Of Life scale||baseline; 9 weeks; 13 weeks|
|Secondary||Modified Ashworth Scale||baseline; 9 weeks; 13 weeks|
|Secondary||Instrumental assessment||baseline; 9 weeks; 13 weeks|
|Secondary||System Usability Scale||9 weeks|
|Secondary||Technology Acceptance Model||9 weeks|
- Villa Beretta Rehabilitation Center Lead
- Translational Neural Engineering Laboratory, EPFL, Switzerland
- Hasomed GmbH, Magdeburg, Germany
- Ottobock Health Products GmbH, Wien, Austria
- Ab.Acus, Milan, Italy
- Asklepios Neurologische Klinik Falkenstein, Königstein, Germany
- Technische Universität Wien, Austria
- Control Systems Group, Technische Universität Berlin, Germany
- Dept. of Electronics, Informatics, Bioengineering, Politecnico di Milano, Italy