We recruited 19 stroke patients (8 females, mean age: 56 ± 11 [from 34 to 71] years) in the chronic phase after stroke (78 ± 55 [from 8 to 244] months) who presented with severe and persistent hemiparesis (13 right-sided, 6 left-sided; 11 ischemic, 8 hemorrhagic) and who provided written informed consent (for demographic information see Table 1). Patient inclusion criteria were: age ≥18 years, time since stroke: ≥6 months, UE-FMA: ≤30 out of 66 points. Participants were excluded from the study if they had uncontrolled epilepsy, drug abuse, psychiatric diseases, a bilateral motor deficit, a severe and uncontrolled clinical disease, cognitive impairment, pregnancy, metal implants or a cardiac pacemaker.
The UE-FMA captures the motor function and contains the subscores A (upper extremity), B (wrist), C (hand) and D (coordination/speed), resulting in a total of max. 66 points. This clinical evaluation was performed by two examiners at the same time to minimize assessment variability. Clinical and kinematic assessments were done subsequently. The average UE-FMA score of the whole patient group was 16.1 ±5.2 points; the individual patient scores had a range from 7 to 29 points; thus, the study included only severely impaired patients. This study was approved by the ethical review committee of the local medical faculty.
Table 1: Demographic information for all participants.
Patient No
|
Age
|
Type of Stroke
|
Gender
|
Side of stroke
|
Month post-stroke
|
UE-FMA
|
1
|
56
|
hemorrhagic
|
male
|
right
|
56
|
29
|
2
|
52
|
ischemic
|
male
|
right
|
156
|
22
|
3
|
68
|
hemorrhagic
|
male
|
right
|
34
|
16
|
4
|
55
|
hemorrhagic
|
male
|
right
|
88
|
10
|
5
|
67
|
hemorrhagic
|
male
|
right
|
75
|
7
|
6
|
69
|
ischemic
|
female
|
right
|
130
|
16
|
7
|
69
|
ischemic
|
male
|
right
|
81
|
14
|
8
|
34
|
hemorrhagic
|
male
|
right
|
45
|
13
|
9
|
63
|
ischemic
|
female
|
right
|
58
|
16
|
10
|
59
|
ischemic
|
female
|
left
|
20
|
19
|
11
|
63
|
ischemic
|
female
|
left
|
133
|
13
|
12
|
51
|
ischemic
|
female
|
right
|
21
|
22
|
13
|
56
|
ischemic
|
female
|
right
|
87
|
22
|
14
|
49
|
hemorrhagic
|
male
|
left
|
69
|
21
|
15
|
71
|
hemorrhagic
|
male
|
right
|
244
|
14
|
16
|
41
|
hemorrhagic
|
male
|
right
|
62
|
9
|
17
|
48
|
ischemic
|
male
|
left
|
8
|
13
|
18
|
36
|
ischemic
|
female
|
left
|
32
|
16
|
19
|
49
|
ischemic
|
female
|
left
|
81
|
14
|
Exoskeleton and Visualization:
The basic methodology of our exoskeleton-based training and assessment setup has already been described in detail in previous studies and is cited here accordingly [8, 9, 33]: We used a commercially available (Armeo Spring, Hocoma, Volketswil, Switzerland) rehabilitation exoskeleton with separate sensors for shoulder (arm rotation, arm elevation), elbow (flexion/extension) and wrist joints (flexion/extension, pronation/supination) to provide gravity-balancing support for the paretic arm and simultaneous registration of movement kinematics and grip force.
This device enabled us to make individual adjustments of gravity compensation, thereby supporting patients with severe impairment in performing task-oriented practice within a motivating virtual environment. To align posture and to minimize the exoskeleton-patient interaction, the same position (neutral zero) with a distance of 90 degrees between forearm and upper arm, with the shoulder being adducted to the trunk and with the thumb pointing upwards, was applied as the starting position for all assessments. In accordance with the manufacturer’s instructions, the length of the different components of the exoskeleton with regard to the wrist, forearm and upper arm was adjusted to suit the individual anatomical proportions of each patient. Gravity compensation was set according to the manufacturer’s instruction, thereby, allowing for a complete gravity compensation of the upper limb in the neutral zero position. In this context, a better understanding of the weight compensation provided by this device may help to fully utilize it in clinical and research settings [10]. A file mapping communication protocol was used to read the real-time movement data, as originally represented in the angles of all arm joints, and the grip force measured by the device from a shared memory block.
Using the real-time sensor data of the exoskeleton to display a three-dimensional multi-joint visualization of the user’s arm in virtual reality (VR), we extended these features in-house to provide both visual and auditory instructions and feedback for the patient. Since our exoskeleton-based rehabilitation interventions were already using this VR set-up, we applied the same technology also for the assessment protocol to avoid a methodological disruption of the integrated training and assessment sessions. The aim of this VR approach was, furthermore, to standardize the evaluation independent of the interaction of an examiner to reduce assessment variability. The system’s features allow for further optimization (e.g., multimodal feedback, personalized content, gamification) in the future. The real-time sensor data enabled us to display a natural virtual representation of the patient’s arm on a computer screen. This provides the patient with additional visual feedback on how the movement was performed. The virtual arm engine was programmed in a Microsoft XNA framework. The arm model utilized by the engine was constructed as a meshed bone-skin combination with 56 bones which were modelled as interconnected bodies in the simulation (3Ds Max 2010TM, Autodesk). This model included 14 finger bones, 11 hand route bones and one bone for each shoulder, forearm and upper arm for each side of the body [9]. The real-time sensor data modulated the 3D model displayed on a 2D screen. Specifically, the joint angles and grip forces of the device measured with the exoskeleton were used to modify the pose of the bones (i.e., position of the bone objects in CAD space) of the meshed model in accordance with the movements of the user, thereby providing online closed-loop feedback. The joint angles of the exoskeleton were directly represented in virtual reality, while the grip forces were amplified to feedback natural hand function.
Movement assessment design:
The positioning of the patient in the exoskeleton (~5 min), including the complete movement assessment (~6 min) and the clinical evaluation with the UE-FMA score (~ 30 min) were performed on the same day. Participants were instructed by the therapists before the examination. To facilitate an efficient evaluation of the motor abilities of severely affected stroke patients, a kinematic registration of the arm was conducted in one self-contained session, i.e., the task was different and separated from the tasks in the training sessions.
A software design instructed the patients by arrows, text that indicated the respective instructions (flex/extend the wrist) and tone messages to repeat single-joint movements while providing feedback related to the performed movements and the range of motion (Figure 1). The simple instructions and single-joint movements ensured that the self-contained movements could be performed by patients of all cognitive levels. Since these tasks were designed to measure the maximum range of motion of single-joints in joint space, reference points did not require tracking and so no overshoots occurred, which otherwise may be observed during 3D motion tracking when 3D rendering is displayed on a 2D screen [34]. In this study, we designed simple, self-contained tasks that minimize patient-exoskeleton interactions and do not rely on learning [24], thus preventing potential confounds that are related to human-device interactions but not to motor recovery [32].
In order to develop a fast and practical assessment for severely impaired stroke patients that could be applied in the context of daily rehabilitation sessions, the overall number of evaluated parameters was restricted. The single-joint movements of this task were, moreover, carefully selected to be independent of the exoskeleton environment, i.e., they could also be translated to the environment outside the exoskeleton if the patients’ training progress eventually enabled them to perform them without gravity-compensation. The following joints were measured subsequently and selectively: grip pressure (difference between closing and opening the hand), wrist movement (flexion and extension), elbow movement (flexion and extension), shoulder flexion/extension and shoulder rotation; shoulder ab- and adduction was limited due to the physical constraints of the exoskeleton and was not evaluated. Also, pronation/supination was not assessed in this study. During each joint movement, the other joints were blocked to measure improvements without compensatory movements. Each task was performed 5 times, allowing the movement to be performed for 5s in each direction followed by a 5s rest period.
All joint movement data for the wrist, elbow, upper arm, and shoulder were recorded during the exercises in °. The grip pressure was estimated in kilopascal (kPa).
Statistics and data evaluation
Statistical analysis was performed on a Matlab 2010b Engine and SPSS (IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp.). The extent of the kinematic parameters was calculated as a mean over the trials.
A multiple regression was performed to predict the UE-FMA score from grip pressure, wrist movement, elbow movement, shoulder flexion/extension, and shoulder rotation. The linearity was assessed by partial regression plots and a plot of studentized residuals against the predicted values. The independence of residuals was assessed by a Durbin-Watson statistic. The assumption of normality was assessed by a Q-Q Plot. The significance level was set at p=0.05 for all tests.
A Pearson's product-moment correlation was estimated to assess the relationship within and between the subscores A-D of the UE-FMA and the kinematic parameters. The analyses showed a linear relationship with the variables being normally distributed as assessed by the Shapiro-Wilk test (p > .05); there were no outliers.