Exp Brain Res (2009) 194:219–231 DOI 10.1007/s00221-008-1680-6
RESEARCH ARTICLE
Minimally assistive robot training for proprioception enhancement Maura Casadio Æ Pietro Morasso Æ Vittorio Sanguineti Æ Psiche Giannoni
Received: 23 August 2008 / Accepted: 4 December 2008 / Published online: 13 January 2009 Springer-Verlag 2008
Abstract In stroke survivors, motor impairment is frequently associated with degraded proprioceptive and/or somatosensory functions. Here we address the question of how to use robots to improve proprioception in these patients. We used an ‘assist-as-needed’ protocol, in which robot assistance was kept to a minimum and was continuously adjusted during exercise. To specifically train proprioceptive functions, we alternated blocks of trials with and without vision. A total of nine chronic stroke survivors participated in the study, which consisted of a total of ten 1-h exercise sessions. We used a linear mixed-effects statistical model to account for the effects of exercise, vision and the degree of assistance on the overall performance, and to capture both the systematic effects and the individual variations. Although there was not always a complete recovery of autonomous movements, all subjects exhibited an increased amount of voluntary control. Moreover, training with closed eyes appeared to be beneficial for patients with abnormal proprioception. Our results indicate that training by alternating vision and no-vision blocks may improve the ability to use proprioception as well as the ability to integrate it with vision. We suggest that the approach may be useful in the more general case of
M. Casadio (&) P. Morasso V. Sanguineti Department of Informatics, Systems and Telematics (DIST), University of Genoa, Via Opera Pia 13, 16145 Genoa, Italy e-mail:
[email protected] M. Casadio P. Morasso Italian Institute of Technology (IIT), Genoa, Italy P. Giannoni ART Education and Rehabilitation Center, Genoa, Italy
motor skill acquisition, in which enhancing proprioception may improve the ability to physically interact with the external world. Keywords Robot training Haptic interaction Proprioception Stroke patients Neuro-rehabilitation
Introduction During the last two decades, robots have been widely used to investigate the mechanisms underlying the acquisition of novel motor skills. Furthermore, starting with early experiments based on the MIT-Manus system (Aisen et al. 1997), robots have proven effective in promoting the recovery of sensorimotor functions in persons with neuromotor impairments (Prange et al. 2006; Kwakkel et al. 2007). As regards motor learning, in a seminal paper (Shadmehr and Mussa-Ivaldi 1994) used a manipulandum-type robot to simulate a dynamic environment that systematically perturbed arm motion. With practice, subjects gradually recovered their original performance, by learning to predict the disturbance. Vision and proprioception are known to be equally important in skill acquisition (Battig 1954), but in these experiments adaptation still occurred in absence of visual feedback (Franklin et al. 2007), thus suggesting a crucial role for proprioception in developing an internal model of the disturbance. The nervous system uses flexible strategies while integrating visual and proprioceptive information (Sober and Sabes 2005). However, when both sources of information are available, vision tends to dominate (Wolpert et al. 1994; van Beers et al. 1996, 1998; Botvinick and Cohen 1998; Smeets et al. 2006).
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A consequence of visual dominance is that vision may mask proprioceptive impairments. In fact, in stroke survivors motor impairment is frequently associated with degraded proprioceptive and/or somatosensory functions (Tyson et al. 2007), with negative consequences for functional outcome (Carey et al. 1993). These subjects may have difficulties in estimating the position of their arm in absence of vision. In addition, they may be unable to integrate visual and proprioceptive information. Furthermore, they may not be able to detect the presence, magnitude and direction of external forces. As a consequence, impaired proprioception may affect not only motor performance, but also neuromotor recovery. On the other hand, repeated, active exercise is known to have a positive influence not only on motor deficits, but on defective proprioception as well (Dechaumont-Palacin et al. 2007). Robot therapy exercises for neuromotor rehabilitation usually consist of video games with a predominantly visual component, which tends to hide the proprioceptive component of interaction. However, robots might be potentially beneficial for promoting the recovery of impaired proprioception and/or visuo-proprioceptive integration in addition to that of motor functions. In most cases, robot therapy protocols for neuromotor rehabilitation use a combination of different exercises (Prange et al. 2006; Kwakkel et al. 2007)—passive, activeassisted and/or active-resistive—and it is difficult to draw solid conclusions on which technique is more effective. However, the most recent studies (Takahashi and Reinkensmeyer 2003; Patton and Mussa-Ivaldi 2004; Patton et al. 2006b) suggest that therapy protocols should explicitly take the adaptive nature of the nervous system into consideration. More specifically, Emken et al. (2007) suggested that sensorimotor adaptation is driven by the optimization of a cost function which accounts for both effort and position error. Accordingly, the motor system would behave as a ‘greedy’ optimizer, exploiting the assistive forces generated by the robot to reduce the degree of voluntary control (and therefore muscle activation), while keeping the position error small. A similar situation is likely to occur during active-assisted exercises (and, even more, during passive training), so that an assistive force with constant magnitude would progressively depress voluntary control rather than promoting its increase. To prevent this, the degree of assistance should be continuously adjusted as training proceeds, and ideally kept to the minimum amount that is necessary to achieve the goal. As a consequence, in robotassisted motor rehabilitation (as well as motor skill learning) it is crucial to design interaction schemes that are capable of delivering a minimum amount of assistance (Casadio et al. 2009). This principle of interaction is often referred as ‘assist-as-needed’ (Trombly 1995; Emken et al. 2007).
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Here we investigate, in a pilot study, the potential benefit of a form of active-assisted training that emphasizes the role of proprioception and keeps assistance to a minimum, in the recovery of arm movements after stroke. Chronic stroke survivors performed reaching movements with their affected arm, under the influence of robot-generated assistive forces. Subjects were initially unable to complete the task without assistance. The therapist initially set the magnitude of the assistive force provided by the robot so that assistance allowed patients to initiate the movements, but in no way imposed the trajectory, the reaching time, and the speed profile. Whenever patient performance improved, in the subsequent blocks of trials force magnitude was reduced; however, each session included trials with all the previously experienced levels of assistance. In summary, we used an adaptive, ‘assist-as-needed’ protocol. Across sessions, trials were performed with open or closed eyes, in alternation. In closed eyes trials, subjects were forced to rely on proprioception alone to successfully achieve the movement goal. A problem of the protocols based on variable degrees of assistance for severely impaired subjects is that the amount of voluntary control (i.e., the performance in absence of assistance), as well as its change due to exercise, is not immediately observable when looking at performance in assisted trials (Colombo et al. 2005). Moreover, if assistance is tailored on individual subjects and is decreased as subjects improve, treatment protocols tend to vary widely across subjects, which makes comparisons difficult. A similar problem occurs if therapy protocols include both open and closed eyes training. The effect may be highly variable from subject to subject, depending on the nature of their impairment. Subjects with impaired proprioception may perform better in presence of vision. Subjects with problems in integration of proprioceptive and visual information may perform better in absence of vision. In these different situations, open and closed eyes training is likely to have different effects. To allow the exploration of this form of between-subjects variability, we used a mixed-effects statistical model, which separately accounts for the effects of exercise, vision and degree of assistance on the overall performance. At the same time, the model allows to analyze inter-subject variability.
Materials and methods Subjects Nine stroke survivors (2 males, 7 females, age 52 ± 14) participated in this study. Subjects were recruited among those followed as outpatients of the ART Rehabilitation
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and Educational Center, Genova. All patients were treated regularly on a weekly basis for at least 6 months before entering the study. The inclusion criteria were: chronic conditions (at least 1 year after stroke), stable clinical conditions for at least 1 month before entering robot therapy. The exclusion criteria were inability to understand instructions about the exercise protocol and other neuro-cognitive problems. Preference was given to patients with a high degree of motor impairment. Disease duration was 34 ± 19 months (range 12–76), with a majority of ischemic etiology (7/9). Patient impairment was evaluated by means of the Fugl-Meyer score, limited to the arm section (FMA) (Gladstone et al. 2002; Platz et al. 2005). Four subjects had a severe impairment (FMA \ 10/66); three patients had an impairment of intermediate level (10 \ FMA \ 20); two patients had a mild impairment (FMA [ 20). The average FMA score was 15 ± 13 (range 5–41). The average Ashworth score of muscle spasticity (Bohannon and Smith 1987) was 1.9 ± 0.9 (range 1–3). Table 1 reports the demographic and clinical data for all the subjects. Due to the small size of the population of subjects, this study cannot be considered a clinical trial but, rather, a feasibility study for the proposed robot-therapy approach (assist-as-needed and proprioceptive training) and a demonstration of the application of the related analytical tools. The research conforms to the ethical standards laid down in the 1964 Declaration of Helsinki, which protect research subjects, and to ethical bylaws of the International Association of Bobath Instructors (IBITA). Each subject signed a consent form that conforms to these guidelines.
Experimental apparatus The robot—Braccio di Ferro (BdF)—is a planar manipulandum with 2 degrees of freedom (Fig. 1), which has been described elsewhere (Casadio et al. 2006). The subjects sit in a chair, with their chest and wrist restrained by means of suitable holders, and grasp the handle of the manipulandum. A light, soft support is connected to the forearm that allows low-friction sliding of the hand on the horizontal surface of a table, with no influence of gravity. The position of the seat can be adjusted in such a way that the farthest targets (see the next section) can only be reached with an almost extended arm. A 1900 LCD screen is positioned right in front of the patients at a distance of about 1 m in order to display the positions of hand and target (see below) by means of circles of different colors, with a diameter of 2 cm. The visual scale factor is 1:1. Training protocol The training protocol specifically focuses on facilitating active execution of outward movements. The task consists of hitting a set of targets, arranged in the horizontal plane (Fig. 1) according to three layers: inner (A, 3 targets), middle (B, 3 targets), and outer (C, 7 targets); reaching the outer targets requires an almost full arm extension. The distance between adjacent layers is 10 cm; the distance between targets on the same layer is, respectively, 6.26 cm (layer A), 8.77 cm (layer B), 5.65 cm (layer C). A target is reached when its distance from the hand is less than 2 cm. The training movements are performed either with open eyes (vision condition) or closed eyes (no-vision
Table 1 Subjects’ demographic and clinical data Subjects
Sex
Age (years)
Paretic hand
Etiology
Disease duration (months)
Site of lesion
FMA (0–66)
Ashworth (0–4)
S1
M
72
L
PACI
28
FTP
6
3
S2
F
59
R
TACI
39
FTP
5
3
S3
F
69
R
PACI
25
FL-PR
12
1?
S4
M
57
L
PACI
40
FTP
17
3
S5
F
34
R
PACI
24
FTP
13
1?
S6
F
30
L
TACI
12
FTO
6
2
S7 S8
F F
46 53
R R
ICH SAH
26 39
F-PR FTN
6 41
2 1
S9
F
55
L
SAH
76
IPO
36
1
Mean
52.78
34.33
15.78
1.88
SD
14.19
18.08
13.56
0.92
Disease duration refers to the time of start of the robot therapy protocol. Etiology is expressed according to the classification of (Bamford et al. 1991). Arm portion of Fugl-Meyer score (FMA), and Ashworth scale of muscle spasticity, both at the beginning of robot therapy Site of lesion: FTP, fronto-temporo-parietal; FT-PR, fronto-temporal ? pre-rolandic; FTO, fronto-temporo-occipital; FTN, fronto-temporonuclear; IPO, intra-parenchimal occipital; TACI, total anterior circulation infarct; PACI, partial anterior circulation infarct; ICH, intracerebral hemorrhagic; SAH, subarachnoid hemorrhagic
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Fig. 1 A view from above of a subject holding the manipulandum. The subject’s shoulders are strapped to a chair; the forearm is attached to a sliding support; the wrist is stabilized by means of a skateboard wrist brace and the hand grasp by means of a Velcro holder. The targets are arranged on three layers: A, B, C. The C layer is placed in front of a virtual wall. The basic sequence of target activation is A ? C ? B ? A and it is repeated 3 9 7 9 3 = 63 times in a random order. Note that the target distance in the figure is twice the real distance for graphical reasons
condition). Target sequences are generated according to the following scheme: A ? C ? B ? A in order to emphasize the training of wide, outward movements with respect to the return to the initial flexed posture. In the vision condition, targets are presented to the subjects simultaneously in two ways, visual and haptic: (1) visually, by means of a circle on the computer screen; (2) haptically, by means of an assistive force field, i.e., a force vector directed toward the current target, xT whatever the current position of the hand. This field was designed in order to fulfill two requirements: (1) to have a ‘gentle’ interaction with the patient, (2) to be effective even in absence of vision, in order to focus the patient’s attention on proprioceptive informations. For these reasons, the assistive force is not activated abruptly at target presentation but with a ramp-and-hold profile R(t): rise time of 1 s and saturation to a force magnitude, FA, which is set by the therapist as the minimum value that evokes a functional response, i.e., a (possibly incomplete) movement in the intended direction. The force is switched off as soon as the subject hits the target. The next target is presented after a pause of 1 s. It should be noted that the chosen assistive field does not emulate the behavior of an ordinary linear spring but rather a constant-force spring. In both cases it is possible to define a potential function with a point attractor. In a linear spring, the potential function grows in a quadratic way with respect to the distance from the target whereas, in the constant-force spring, it only grows linearly. In this application, a constant-force spring
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is a better assistive mechanism because it provides a uniform assistance throughout the whole movement, irrespective of the distance to the target. In contrast, a linear spring would provide an assistive force that vanishes as one approaches the target and is very large when far from the target. In addition to the assistive force component, the haptic control of the robot includes a mild viscous force field (viscosity coefficient: 12 Ns/m), intended to damp occasional hand oscillations without significantly affecting the voluntary reaching patterns, and a virtual ‘wall’ (stiffness: 1,000 N/m) which prevents subjects to go beyond the C layer of targets and provides an additional feedback about the successful achievement of the outward target. The force field generated by the robot is summarized as follows: ðxT xH Þ FðtÞ ¼ FA RðtÞ Bx_ H jxT xH j KW ðxW xH Þ step ðxW xH Þ ð1Þ where xT is the vector that identifies the target position in the plane; xH and x_ H are, respectively, the hand position and speed vectors; xW indicates the projection of hand position on the wall; B is the viscous coefficient; KW is the stiffness coefficient of the wall; step identifies a step function, in order to allow the ‘‘wall’’ term to be one-sided. Considering that subjects were simply instructed to reach the targets as soon as possible, it should be noted that the above scheme of assistance does not explicitly specify the timing of the reaching movement and/or the trajectory that subjects have to follow in order to reach the target, except for the occasional ‘sliding’ movements along the virtual wall. In other words, the robot does not ‘‘guide’’ the subject’s hand along a fixed trajectory, and does not enforce a fixed reaching time. This was done on purpose, together with the choice of keeping the level of robotic assistance as low as possible, in order to make sure that the observed responses were mainly driven by active motor control, not robot action. The protocol started with a test phase, in which individual subjects familiarized with the apparatus and the range of assistive forces. This phase was supervised by a physical therapist, who observed the subjects’ response to the different force levels and selected the minimum level Ftest capable to induce at least a hint of active response in the direction of the target. One block of trials included repetitions of the A ? C ? B ? A sequence with different targets in random order, for a total of 3 9 3 9 7 = 63 movements; 21 of them were large amplitude, outward movements and 42 movements had smaller amplitude and were directed inward. The protocol, for each session, is then defined by the following pseudo-code:
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Session: set F = Ftest Train: perform 1 block with assistive field intensity F, Vision condition if patient is fatigued or time is over then stop otherwise perform 1 block with assistive field intensity F, No-vision condition if patient is fatigued or time is over then stop else if performance over threshold then reduce F by 10–20% go to Train. Fatigue was ascertained verbally, by asking the patient at the end of each session. The ‘Time over’ threshold was set to 1 h (±5 min). Performance was measured as the mean speed within the session. The performance threshold for force decrease was empirically set to 10%. A consequence of the protocol is that, as training proceeded, the number of blocks increased and the minimum assistance level decreased. Moreover, in each session subjects experienced all the assistance levels used in the previous sessions, plus some additional ones if they were not fatigued and time was not over. In other words, the overall pattern of variation of the assistive force during the training procedure is nonmonotonic: at the beginning of each session it goes back to the initial force, selected in the test session; then it is reduced in steps down to a minimum value that sometimes is lower than the value reached in the previous session and sometimes is not. The rationale is double: (1) to help consolidate the memory of the learned patterns; (2) to adapt the assistance to the actual performance. If subjects reached a level of assistance with a force below 4 N, the no-vision blocks were eliminated because that level of force is quite close to the acknowledged perceptual threshold and thus is insufficient for allowing the subjects to perceive target direction without vision. The robot training protocol consisted of ten sessions (1 session/week), plus the initial test session. Each session did not last more that 1 h. Data analysis Hand trajectories and the forces generated by the robot were recorded at a sampling rate of 100 Hz. Hand position was measured from the 17-bit encoders of the motors, with a precision better than 0.1 mm in the whole workspace. Hand speed was estimated by using a 4th order Savitzky– Golay smoothing filter (with an equivalent cut-off frequency of 6 Hz). The analysis focused on the outward movements (A ? C). In particular, we defined four performance indicators:
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•
•
•
•
Mean speed: average hand speed, computed from the time of target presentation to the time at which the subject reaches the target. This indicator is expected to increase as training proceeds. Number of sub-movements, identified by the number of peaks in the speed profile. The output of the smoothing filter may contain a few spurious velocity peaks. We eliminated them by means of two criteria: (1) a threshold on the speed (0.01 m/s), (2) a threshold on the time interval between one peak and the next one (0.3 s). Normal reaching movements are characterized by a single-peaked, bell-shaped speed profile (Morasso 1981) and thus this indicator should approach 1, as training proceeds. T-ratio, defined as the ratio between the duration of the first sub-movement and the total time required for reaching the target. The corresponding sub-movement duration is identified by two consecutive points of minimum in the speed profile, one before and the other after the point of peak. As training proceeds, this indicator should go up to 1 (or 100%). Endpoint error, defined as the distance from the target of the hand position at the end of the firs submovement. As training proceeds, this indicator should decrease to 0.
The first indicator is a global performance parameter. The second and third indicators express, in different ways, the degree of smoothness of the movements. This choice is motivated by the fact that studies on the recovery from neural injury suggested that smoothness is a result of a learned, coordinative process rather than a natural consequence of the structure of the neuromuscular system. The fourth indicator is related to accuracy. Statistical analysis As stated in ‘‘Introduction’’, we are interested in assessing the overall effect of the treatment (number of sessions, degree of assistance, presence–absence of vision) on movement performance. However, this same effect may differ in individual subjects and, within the same subject, for different target directions. To account for this, we used a multilevel mixed-effects model (Laird and Ware 1982), with three fixed factors (session, force, vision) plus an interaction term (session 9 vision) and two (nested) random factors (subject and target). This allows to properly account for the correlations among repeated measures from the same subject and within the same target (Murdoch et al. 1998), and at the same time to analyze inter-subject variability, i.e., how performance and the effect of treatment varies in different subjects and, for the same subject, in different movement directions.
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For each indicator, the model is defined as: indicatorijk ¼ B0ij þ B1ij Sijk þ B2ij Fijk þ B3ij Fijk þ B4ij ðSijk Vijk Þ þ eijk
ð2Þ
where Sijk (session) is the session number (from 0 to 9), Fijk (force) is the intensity of the assistive force (in N), and Vijk (vision) is the absence (0) or presence (1) of vision, respectively for the ith subject (i = 1,…,9), the jth target (j = 1,…,7) and the kth outward trial (k = 1,…,3). The residual eijk is the portion of the indicator that is explained by neither of the above factors. Model coefficients may be interpreted as follows. The intercept, B0ij, is the estimated baseline performance level, i.e., the performance at the initial session, with zero assistive force; this can be interpreted as the initial degree of voluntary control. Parameter B1ij is the between-session rate of improvement. Parameter B2ij is a ‘compliance’ component, measuring the dependence of performance on the assistance level. Parameter B3ij is the ‘vision’ component, which denotes the contribution to the performance provided by presence of vision. Finally, parameter B4ij is the ‘session 9 vision’ component, which accounts for the differences in the session effect that are due to vision. In other words, B4ij accounts for the different behaviors, in terms of between-session improvement, of the vision and no-vision trials. The presence of random factors implies that each of the above parameters can be seen as having a fixed component (the same for all subjects and targets), and a random component (different for each subject and, within each subject, for each target). For instance: B0ij ¼ b0 þ b0i þ b0i;j
ð3Þ
where b0 is the ‘fixed’ portion of the parameter, common to all subjects and targets; b0i is the portion of the parameter which relates to the ith subject, and b0i,j is the portion of the parameter related to the jth target within the ith subject. These components can be estimated separately. Testing the significance of the ‘fixed’ components (e.g., b0) correspond to hypothesis testing as in ANOVA. For instance, asking like whether the therapy produces a significant improvement would correspond to testing the significance of the session effect, i.e., b1. Moreover, if we look at the random parameters (e.g., b0i and b0i,j) we can analyze inter-subject (and possibly intertarget) variability. For instance, for the ith subject we may look at the relationship between the baseline performance (b0i) and the subsequent improvement (b1i) in the no-vision condition. Or, we may look at the difference of baseline performance between vision and no-vision trials (b3i) and the corresponding difference in improvement between the same trials (b4i). For each indicator, we fitted the model to
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the data by using a restricted maximum-likelihood procedure, which provided estimates of the coefficients that account for both the fixed and the random components, as well as the corresponding significance scores. For all statistical calculations, we used the R statistical package and specifically the ‘lme’ function library implementation of mixed-effect models (Bates and Pinheiro 1998). Results Overall evolution of the training sessions In the early sessions, the outward reaching movements were typically segmented into a sequence of sub-movements. The first sub-movement only covered part of the total distance, possibly with a directional error, thus requiring the subjects to make subsequent corrections. In contrast, inward movements were usually characterized by a single, higher peak in the speed profile. As a consequence, outward movements tend to have a greater duration than inward ones. Figure 2 shows, for all the subjects, the evolution of the speed profile from the initial to the final session in the two experimental conditions, vision and no vision, respectively. In general, all the subjects were characterized by a trend to quicker and smoother reaching movements in spite of the fact that the level of the assistance force was progressively reduced. The evolution of the assistance level is summarized in Table 2. Table 2 shows that the initial assistance level, which matched the subjects’ degree of impairment, ranged between 5 and 25 N; this range was reduced to 0–10 N in the final session. It should be noted that the less severe patients (S8, S9) could carry out the task without assistance. The corresponding number of blocks of trials was increased from an initial range of 3–8 to a final range of 8–12, while keeping the same total duration of the therapy sessions. The evolution of the four performance indicators, from the initial to the final session, in the two experimental conditions (vision vs. no vision), is reported in Tables 3 (mean speed), 4 (number of sub-movements), 5 (T-ratio), and 6 (end point error after the first sub-movement). Fixed effects Movement performance is affected by repeated training (the session factor), by the magnitude of assistive force (the force factor) and by the experimental condition (vision vs. no vision). In the same session we used different levels of assistive force, and for a specific subject the same assistive force was applied throughout the training protocol in a variable number of blocks.
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Fig. 2 Speed profile of the typical movement in the first and the last c session, for each subject and for the same movement (from the central position of the A layer to the central position of the C layer). The plots refer to the vision (open eyes, black) and non vision (closed eyes, gray) conditions, respectively
Force As regards the level of assistance, we found a significant effect of force—b2 coefficient—on peak speed (P \ 0.0001), number of peaks (P = 0.0026), endpoint error (P = 0.0003) and T-ratio (P \ 0.0001). This is no surprise, as it is the mere confirmation that assistance has a beneficial effect on performance. Session We found highly significant effects of session for the mean speed (P = 0.0076), the number of sub-movements (P \ 0.0001), the T-ratio (P = 0.0001), and the endpoint error (P = 0.0308). The b1 coefficient—session, systematic part—is positive for the two indicators (mean speed, T-ratio) for which the improvement corresponds to an increase of the indicator (0.335 ± 0.096 cm/s per session and 2.70 ± 0.659% per session, respectively), and negative for the two indicators (number of sub-movements and endpoint error) for which improvement is denoted by a decrement (-0.369 ± 0.098 peaks per session and -0.316 ± 0.088 cm per session, respectively). This means that the significant effects of session in fact correspond to improved performance. Vision As regards the effect of vision, in no indicator we found significant vision and session 9 vision effects. This means that the presence of vision did not have a systematic effect. Again, this is hardly surprising, as subjects are likely to differ widely in their sensory impairment as regards its relationship with the underlying motor impairment: some subjects strongly rely on vision because proprioception is poor; in other subjects proprioception is good enough to take the place of vision; in some case there may even be a conflict between vision and proprioception. Random effects The model of Eq. 2 can be used to identify the individual variations—subjects by subject and target by target—in the contributions of each factor (session, force, vision) to each indicator. Here we only discuss the variability across subjects.
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Table 2 Evolution of the minimum level of assistive force (N) and (in parentheses) the corresponding number of blocks, for each subject (S1– S9) and for each session (test session ? therapy sessions) Subjects
Sessions Test
1
2
3
4
5
6
7
8
9
10
S1
25
25 (3)
18 (7)
18 (8)
15 (10)
15 (10)
15 (10)
15 (10)
13 (10)
13 (11)
10 (12)
S2
22
20 (6)
18 (7)
16 (7)
16 (9)
14 (9)
12 (11)
12 (11)
12 (13)
12 (13)
8 (13)
S3 S4
15 13
13 (5) 10 (4)
12 (9) 10 (4)
12 (9) 9 (5)
10 (10) 8 (6)
9 (11) 8 (6)
9 (11) 8 (6)
6 (12) 7 (8)
6 (14) 6 (8)
6 (15) 5 (8)
6 (16) 4 (8)
S5
13
8 (4)
8 (4)
8 (4)
7 (5)
7 (6)
9 (4)
6 (7)
5 (7)
4 (8)
4 (8)
S6
9
6 (6)
6 (6)
6 (6)
5 (6)
5 (7)
6 (7)
5 (8)
4 (8)
2 (9)
2 (9)
S7
9
5 (8)
4 (8)
5 (10)
3 (10)
3 (10)
3 (11)
3 (10)
3 (11)
3 (11)
2 (12)
S8
5
3 (7)
2 (8)
1 (9)
0 (10)
0 (11)
0 (12)
0 (10)
0 (12)
0 (12)
0 (12)
S9
5
2 (8)
1 (8)
0 (10)
0 (10)
0 (10)
0 (12)
0 (13)
0 (12)
0 (12)
0 (11)
Table 3 Mean speed (cm/s): overall modification from the initial to the final session in the two experimental conditions (vision and no vision) Subjects
Vision Initial
S1
0.9 ± 0.1
Table 5 T-ratio: overall modification from the initial to the final session in the two experimental conditions (vision and no vision) Subjects
No vision
Vision Initial
No vision Final
Initial
Final
Final
Initial
Final S1
3.8 ± 1.2
72.8 ± 4.6
8.3 ± 4.1
88.3 ± 2.7
10.7 ± 0.8
1.4 ± 0.8
14.5 ± 0.9
S2
41.6 ± 31.2
48.2 ± 11.7
29.2 ± 2.6
57.5 ± 11.0
S3 S4
31.1 ± 7.1 21.9 ± 7.7
96.3 ± 4.6 82.9 ± 11.7
76.6 ± 4.0 29.5 ± 10.5
89.0 ± 7.8 68.5 ± 2.8
S2
8.1 ± 9.4
5.6 ± 0.8
3.0 ± 0.7
6.9 ± 0.8
S3
4.4 ± 1.4
12.4 ± 1.0
8.9 ± 1.9
11.4 ± 1.9
S4
2.4 ± 0.3
8.5 ± 1.2
3.8 ± 0.7
7.5 ± 0.3
S5
30.6 ± 9.3
62.8 ± 6.3
33.2 ± 9.5
67.6 ± 9.3
S5
4.5 ± 1.1
7.0 ± 1.0
4.9 ± 0.5
7.2 ± 0.9
S6
41.7 ± 14.2
95.7 ± 1.2
26.1 ± 8.3
90.8 ± 13.3
S6
3.4 ± 0.9
10.6 ± 0.6
2.4 ± 0.9
9.6 ± 1.7
S7
80.9 ± 0.4
97.8 ± 2.5
74.1 ± 6.1
96.7 ± 4.3
S7
8.7 ± 0.6
10.6 ± 0.5
7.8 ± 0.2
9.8 ± 1.0
S8
91.2 ± 5.5
98.7 ± 2.2
70.9 ± 4.4
93.6 ± 2.6
S9
96.7 ± 4.4
100 ± 0
82.9 ± 8.3
98.7 ± 1.2
S8
13.2 ± 1.3
16.5 ± 1.6
6.0 ± 0.5
7.8 ± 0.5
S9
11.7 ± 1.6
12.7 ± 1.7
7.4 ± 0.2
9.3 ± 0.3
Table 4 Number of sub-movements: overall modification from the initial to the final session in the two experimental conditions (vision and no vision)
Table 6 End-point error after the first sub-movement (cm): overall modification from the initial to the final session in the two experimental conditions (vision and no vision)
Subjects
Subjects
Vision Initial
No vision Final
Initial
Final
Vision Initial
No vision Final
Initial
Final
S1
10.1 ± 3.8
1.7 ± 0.2
10.3 ± 6.6
1.1 ± 0.1
S1
6.0 ± 0.7
1.3 ± 0.3
8.0 ± 2.2
0.4 ± 0.2
S2
1.9 ± 0.4
2.3 ± 0.6
5.4 ± 3.2
1.8 ± 0.2
S2
3.0 ± 1.4
1.9 ± 0.7
4.9 ± 1.3
1.5 ± 0.8
S3
4.5 ± 0.9
1.2 ± 0.1
2.8 ± 1.1
1.6 ± 0.4
S3
3.1 ± 0.3
0.1 ± 0.1
1.1 ± 0.1
0.6 ± 0.4
S4
14.6 ± 7.3
1.6 ± 0.5
13.2 ± 4.7
3.2 ± 0.7
S4
10.2 ± 2.3
1.3 ± 1.0
14.1 ± 2.8
3.3 ± 0.2
S5
6.1 ± 2.3
2.5 ± 0.3
8.0 ± 0.6
3.7 ± 0.6
S5
9.2 ± 2.0
2.9 ± 0.5
11.3 ± 0.3
2.7 ± 0.8
S6
7.2 ± 4.2
1.2 ± 0.1
10.8 ± 5.2
1.5 ± 0.7
S6
5.8 ± 0.8
0.3 ± 0.1
6.3 ± 0.7
0.6 ± 0.8
S7
1.9 ± 0.2
1.1 ± 0.0
2.8 ± 0.3
1.5 ± 0.2
S7
0.8 ± 0.2
0.2 ± 0.2
2.1 ± 1.2
0.3 ± 0.4
S8
1.6 ± 0.4
1.2 ± 0.2
2.9 ± 0.4
1.7 ± 0.2
S8
0.6 ± 0.4
0.0 ± 0.0
3.6 ± 0.5
0.4 ± 0.1
S9
1.4 ± 0.3
1.1 ± 0.1
5.9 ± 2.6
1.2 ± 0.2
S9
0.3 ± 0.4
0.0 ± 0.0
1.8 ± 0.3
0.1 ± 0.1
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Fig. 3 Relationship between the baseline performance (the b0 ? b0i parameter in the model) and the change over sessions (the b1 ? b1i parameter in the model), relative to the no vision (closed eyes) case. All plots display a strong negative correlation, indicating that improvement is greater in subjects with poorer initial performance. Negative values of the baseline parameters denote that these subjects are unable to move without assistance. For clarity, the ‘change’ parameter has been projected over the set of sessions (i.e., what is shown is b1 ? b1i multiplied by 9)
Session To look at the session effect on each individual subject, for each indicator we compared the model coefficients (both the systematic and the random part), namely b0 ? b0i (baseline performance) and b1 ? b1i (session effect, i.e., the change over sessions), for each individual subject. This allowed to assess, across subjects, the relationship between initial performance and the magnitude of the session effect (i.e., the change in performance due to training). These results are displayed in Fig. 3. All indicators display a strong negative correlation between the baseline performance, b0 ? b0i and the change over sessions, b1 ? b1i: r = -0.89 for the mean speed; r = -0.75 for the number of peaks; r = -0.68 for the T-ratio; r = -0.95 for the endpoint error. This indicates that subjects with poorer initial performance tend to improve more. Although this result can be partly attributed to a ‘ceiling’ effect—in less severe subjects the initial values of the indicators are closer to the ‘ceiling’—it also suggests that, irrespective of the initial conditions, all subjects have a potential for improvement that is enhanced by the assist-as-needed protocol. Figure 3 specifically refers to the session effect in
the ‘no-vision’ case. Similar results were found in the ‘vision’ case. Vision The model also allows to assess the effect of vision on the individual subjects. A crucial question is how the different subjects compare in terms of their initial performance with open or closed eyes. Another question, similar to the one we asked before for the ‘session’ effect, is whether there is any systematic relationship between the difference in the vision and no-vision baseline behavior and the differential change in vision and no-vision trials. The former question can be addressed by comparing, for each subject, the baseline performance without vision (b0 ? b0i) and with vision (b0 ? b0i ? b3 ? b3i). For all four indicators, the comparison is shown in Fig. 4. The figure indicates that some subjects (namely, S1 and S3) have a better initial performance with closed eyes (data points below the diagonal line in the speed and T-ratio plots). In contrast, other subjects (S8, S9) have a better performance with open eyes (data points above the diagonal). The remaining subjects have similar performances
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Exp Brain Res (2009) 194:219–231
Fig. 4 Differential effect of open eyes (vision) and closed eyes (no vision) training. For each subject, filled circles indicate the baseline performance in vision and no vision trials. Circles below the diagonal denote subjects with better performance (or greater error, depending on the indicator) in absence of vision; circles above the diagonal indicate better performance (or greater error) in presence of vision. The lines indicate the direction and the magnitude of improvement. Subjects display a strong trend toward improving most in the modality in which they are initially more defective so that the final performance is closer to the diagonal. For clarity, the ‘change’ parameter (i.e., the length of the line) has been projected over the set of sessions
under both conditions. Moreover, the same figure suggests that, whatever the initial state of each subject as regards the relationship between vision and no-vision performance, the final performance in both conditions improves for all subjects and all indicators. Figure 5 complements the above picture by displaying how the initial performance compares with the change over sessions. More specifically, it depicts, for each subject, the relationship between the difference in the baseline performance with and without vision (i.e., parameter b3 ? b3i) and the difference in the performance change over sessions between trials with and without vision (i.e., parameter b4 ? b4i). Figure 5 indicates a strong negative correlation among these parameters. For speed, number of peaks, T-ratio and endpoint error, the correlation coefficients are -0.36, -0.96, -0.88, and -0.81, respectively. This means that subjects with an initial more severe impairment with closed eyes (negative b3 ? b3i) result in a greater improvement in closed eyes trials (negative b4 ? b4i), and vice versa.
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Discussion Subjects improve their motor performance Analysis of the results suggests that the proposed robot therapy exercise, based on an assist-as-needed paradigm, is capable of improving the ability to perform outward movements in chronic stroke survivors. Improved performance was characterized by a general regularization of the movements. In particular, we observed a remarkable reduction in the degree of segmentation into sub-movements and the emergence of normal reaching patterns—straight paths and bell-shaped velocity profiles (Morasso 1981). As training proceeded, kinematic indicators showed a trend toward movements that were faster, smoother, and with more symmetric speed profiles. It may be asked to what extent such improvements are also indicative of functional recovery. Studies on stroke recovery (Rohrer et al. 2002) have suggested that smoothness is a result of a learned, coordinative process rather than a natural
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Fig. 5 Relationship between the differential effect of training with open eyes (vision) and closed eyes (no vision) in the baseline performance (the b3 ? b3i parameter in the model) and in the change over sessions (the b4 ? b4i parameter in the model). For the number of peaks and endpoint error indicators, subjects with an initial more severe impairment in the no-vision condition (negative b4 ? b4i) result in a greater improvement in the same condition
consequence of the structure of the neuromuscular system. Additionally, there is some evidence that the segmented structure of arm movements in stroke patients can be attributed to a deficit of inter-joint coordination (Levin 1996). Therefore, smooth movements result from an improved coordination, which is a necessary condition for functional recovery. Minimally assistive training A peculiar feature of our proposed approach is that assistance is kept at a minimum, thus preventing movements, as much as possible, from being performed passively. In other words, movements are assisted, not enforced by the robot therapist. In contrast, in other more common approaches the level of assistance is increased if subjects are unable to reach the target, up to a value that allows to reach it in one way or another. The difference is that in our case robot assistance focuses on movement initiation whereas in other approaches it focuses on movement termination.
It is interesting to note that after training, the subjects generally reported mental, not physical tiredness. This occurs, in particular, when subjects have great difficulties in reaching the target, in spite of robot assistance. In this case, they tend to stop temporarily their effort to generate the appropriate command. To do this, they may carry out some kind of mental simulation, in which they ‘imagine’ how to reach it. In conclusion, the proposed minimally assistive strategy may incorporate an element of ‘mental practice’, facilitated by the fact that movement termination is not enforced by the robot. This speculation motivates the design of specific future experiments aimed at the incorporation of mental practice in human–robot interaction. Robot therapy is still effective in severely impaired subjects Our results suggest that even severely impaired patients benefit from robot therapy, to an extent that is at least comparable to those with mild to intermediate levels of impairment. This is partly in contrast with other studies,
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which suggest a greater benefit for mildly impaired subjects (Fasoli et al. 2003; Colombo et al. 2005; Patton et al. 2006a). This is a contribution to an open debate, about the extent to which physical therapy (by either human or robot therapists) may contribute to the functional recovery of severe, chronic patients. At least, our results provide some evidence for a positive prognostic outcome. The importance of proprioceptive training Our results suggest that at least a significant part of stroke survivors may benefit more from proprioception-enhancing therapy sessions (the ‘no-vision’ condition) than from traditional visually guided training. In particular, subjects who initially had problems with closed eyes tend to benefit most from this training modality (the inverse is true for subjects who initially displayed more problems with movements performed in presence of vision). Although this observation is not conclusive from a strictly clinical point of view, it strongly motivates future controlled clinical trials based on this working hypothesis. In conclusion, we suggest that robots may be useful in neuromotor rehabilitation not only because they combine in the same device a capability of delivering interactive and repeatable sensorimotor exercises and continuously monitoring the actual motor performance, but also because they allow to create new and ‘controlled’ haptic environments in which patients can learn to move by only using proprioceptive information. We also believe that robot-controlled, rich, haptic virtual environments may be powerful tools for a better understanding of the role of proprioception during acquisition of novel sensorimotor skills and, possibly, for designing effective robot trainers which are capable to improve subjects’ performance in skill acquisition. Acknowledgments This work was supported by two Research Projects of National Relevance (PRIN) grants awarded by the Italian Ministry of University and Research to P. Morasso and V. Sanguineti. We thank Ms. Liliana Zerbino, PT, for the help in the selection of the patients and the evaluation of the FMA score.
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