Robot therapy for stroke survivors: proprioceptive training and regulation of assistance.

Abstract

Robot therapy seems promising with stroke survivors, but it is unclear which exercises are most effective, and whether other pathologies may benefit from this technique. In general, exercises should exploit the adaptive nature of the nervous system, even in chronic patients. Ideally, exercise should involve multiple sensory modalities and, to promote active subject participation, the level of assistance should be kept to a minimum. Moreover, exercises should be tailored to the different degrees of impairment, and should adapt to changing performance. To this end, we designed three tasks: (i) a hitting task, aimed at improving the ability to perform extension movements; (ii) a tracking task, aimed at improving visuo-motor control; and (iii) a bimanual task, aimed at fostering inter-limb coordination. All exercises are conducted on a planar manipulandum with two degrees of freedom, and involve alternating blocks of exercises performed with and without vision. The degree of assistance is kept to a minimum, and adjusted to the changing subject's performance. All three exercises were tested on chronic stroke survivors with different levels of impairment. During the course of each exercise, movements became faster, smoother, more precise, and required decreasing levels of assistive force. These results point to the potential benefit of that assist-as-needed training with a proprioceptive component in a variety of clinical conditions.

Full text

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Robot therapy for stroke survivors:
proprioceptive training and regulation of
assistance
Vittorio SANGUINETIa,1 , Maura CASADIOa,b, Elena VERGAROa, Valentina
SQUERIa,b, Psiche GIANNONIc and Pietro G. MORASSOa,b
aDepartment of Informatics, Systems and Telematics, University of Genoa, Genoa,
Italy
bItalian Institute of Technology, Genoa, Italy
cART Education and Rehabilitation Center, Genoa, Italy
Abstract. Robot therapy seems promising with stroke survivors, but it is unclear
which exercises are most effective, and whether other pathologies may benefit
from this technique. In general, exercises should exploit the adaptive nature of the
nervous system, even in chronic patients. Ideally, exercise should involve multiple
sensory modalities and, to promote active subject participation, the level of
assistance should be kept to a minimum. Moreover, exercises should be tailored to
the different degrees of impairment, and should adapt to changing performance.
To this end, we designed three tasks: (i) a hitting task, aimed at improving the
ability to perform extension movements; (ii) a tracking task, aimed at improving
visuo-motor control; and (iii) a bimanual task, aimed at fostering inter-limb
coordination. All exercises are conducted on a planar manipulandum with two
degrees of freedom, and involve alternating blocks of exercises performed with
and without vision. The degree of assistance is kept to a minimum, and adjusted to
the changing subject’s performance. All three exercises were tested on chronic
stroke survivors with different levels of impairment. During the course of each
exercise, movements became faster, smoother, more precise, and required
decreasing levels of assistive force. These results point to the potential benefit of
that assist-as-needed training with a proprioceptive component in a variety of
clinical conditions.
Keywords. Robot therapy, stroke, rehabilitation
Introduction
During the last few years, considerable effort has been devoted to using robots for
delivering therapy to persons with motor disabilities [1, 2]. Robotic devices have been
frequently used to enforce passive movements (see Figure 1, left). In fact, it has been
shown that repeated passive exercise may help improving recovery[3, 7]. However, a
number of studies [8, 10] point at techniques that take the adaptive nature of the
nervous system into consideration. Such techniques include active-assisted exercises,
in which the robot guides the arm along a desired path (see Figure 1, right). A variant is

1 Corresponding author: Department of Informatics, Systems and Telematics, University of Genoa, Via
Opera Pia 13, 16145 Genoa (ITALY). E-mail: vittorio.sanguineti@unige.it

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Advanced Technology in Rehabilitation
Empowering Cognitive, Physical, Social and Communicative Skills through Virtual Reality, Robots,
Wearable Systems and Brain-Computer Interfaces
Volume 145 Studies in Health Technology and Informatics
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represented by active-constrained techniques, in which the robot only allows
movement when the limb forces are appropriately directed toward the target. In
contrast, in active-resisted exercises the robot provides resistance to the desired
movement. Furthermore, in adaptive exercises the robot provides an unfamiliar
dynamic environment, which requires the subject to adapt. Active-resisted and adaptive
techniques imply the presence of a sufficient residual voluntary function, but are not
viable options for severely impaired subjects, who may lack autonomous control of
their movements. On the other hand, these subjects may benefit from therapeutic
protocols in which a sufficient level of assistance allows them to exploit their residual
abilities.
In this chapter, we review a number of studies on using robots with chronic stroke
survivors. In particular, we suggest that rehabilitation protocols should involve vision
and no-vision (proprioceptive) training, and that assistance should be kept to a
minimum.
Motor learning and the role of assistance
Most robot therapy protocols tested in clinical trials use a combination of active and
passive training [1, 2]; therefore, it is hard to draw solid conclusions on their relative
merits. Some indications on what exercises are more effective may come from a better
understanding of the neural basis of motor learning.
The mechanisms of action of physical assistance in promoting motor learning or
re-learning are poorly understood. Assistive forces help subjects complete the motor
task, which in turn may increase subject motivation, even in the early phase of the
learning/recovery process. Furthermore, assistive forces may elicit the right afferent
signals (proprioceptive, tactile), thus promoting the emergence of the appropriate
associations in sensory and motor cortical areas. In addition, assistive forces may affect
learning by inducing a sensation of greater stability of the external environment, or
some aspects of it a necessary condition for long-term, more stable adaptation to occur
[11].
In the simplest form of assisted exercise, the robot has complete control of the task.
This may be beneficial, but as learning (or re-learning) progresses, the differences
between physically guided and active movements become more important. Passive
(completely assisted) movements provide feedback that is different from that of active
movements. In fact, in motor learning studies, the benefits of physical guidance for
motor learning have mainly been ascribed to its early phase, when the motor pattern is
brought into the ‘right ballpark’, e.g. [12].
robotrobot
humanhuman
x(t)x(t)
F(t)F(t)
--

robotrobot
humanhuman
x(t) x(t)
F(t)F(t)
--

Figure 1. Passive (left) and Active (right) training modalities

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The learning process may be facilitated if augmented feedback is provided on
selected aspects of performance and/or the outcome of the movement[13]. In fact,
assistance may be seen as a form of augmented feedback that emphasizes performance
on specific aspects of the task.
Overall, these considerations suggest that in order to be effective, assistance should
be highly task-specific. Recent studies indicate that only the task-relevant features of a
movement are explicitly controlled by the nervous system [14]. The remaining degrees
of freedom would be specified through an on-line optimisation process. This would
suggest that assistance should be limited to specific (task-relevant) features of the
movement.
According to this view, assistance would take the general form of a feedback
controller, with proportional (position-dependent) and derivative (speed-dependent)
components. An alternative view [15] is that assistance continuously generates the
forces that the impaired arm cannot provide by itself, so that the movement is as
normal as possible. In this view, assistance would take the form of a controller which
involves an explicit model of the (impaired) arm and its neural control.
The ultimate goal of robot therapy is to emulate as closely as possible a (good)
human physical therapist. This would suggest, at least in perspective, to look at the
patterns of patient-therapist interaction from the point of view of the modalities of
assistance outlined above. This would also allow to identify which combination of
feedback and feed-forward control is actually embodied by human therapists.
Robot-assisted exercise may be seen as a form of cooperative control, in which the
robot and the human subject aim at achieving a common goal. The aim is to gradually
transfer control from the robot to the human.
Regulation of assistance
Recently, a few studies have addressed the way assistive forces affect motor
performance and/or motor learning [16]. When moving under the effect of assistive
forces, provided by a robot agent, humans tend to quickly incorporate these forces in
their motor plan. More specifically, the motor system appears to behave as a ‘greedy’
optimiser, which exploits the assistive forces in order to reduce the amount of
voluntary control (and therefore muscle activation), while keeping the position error
small. This strategy would minimize effort while maintaining the required
performance. As a consequence, during active-assisted exercises (and, even more,
during passive training), a constant-magnitude assistive force would gradually depress
voluntary control; it has been suggested that this would have adverse effects on
recovery. To prevent this, assistance should be reduced to a minimum (assist-as-
needed), and continuously regulated as a function of the observed outcome[17].
Ideally, assistance should be adjusted with respect to the current amount of
voluntary control. As the latter is not readily available, most schemes of regulation are
driven by the observed performance; see Figure 2.

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Are there ‘optimal’ ways to provide assistance, and to continuously regulate it? If
this is the case, do they depend on the specific task, or do they obey to general
principles, valid for a wide range of motor learning problems? While optimal solutions
have been proposed for simple, specific tasks, like lifting a weight [18, 19], it would be
desirable to derive general principles and methods, that can (in principle) be applied to
any motor learning/re-learning task.
Recently, Wolbrecht and coll. [15] proposed an adaptive control scheme, in which
a controller negotiates an error-reducing and an effort-reducing component. This allows
to keep assistance to a minimum and to automatically adapt it to task performance,
while providing enough assistance to support task completion. This technique does not
explicitly aim at augmenting the degree of voluntary control. Such an increase is
assumed to result from the ability to successfully complete the task.
Proprioceptive training
In stroke survivors, motor impairment is frequently associated with degraded
proprioceptive and/or somatosensory functions [20]. Stroke subjects may have
difficulties with estimating the position of their arm in absence of vision. Moreover,
they may be unable to integrate visual and proprioceptive information. Furthermore,
when performing assistive training they may not be capable of detecting the presence,
magnitude and direction of assistive forces. Therefore, impaired proprioception may
affect the recovery of motor functions [21]. Like motor deficits, proprioceptive deficits
may decrease through repeated exercise [22]. The nervous system uses flexible
strategies in integrating visual and proprioceptive information [23]: when both visual
and kinesthetic information of a limb are available, vision is usually the dominant
source of information [24, 27]. As a consequence of visual dominance[28, 30],
proprioceptive impairment may be masked by vision if the latter is available. This
would suggest that in subjects with both proprioceptive and motor impairment,
assistive exercise might be more effective if at least part of the training were performed
without vision. In fact, recent studies demonstrate that visual feedback is not necessary
for learning novel dynamics [31].
In this context, the contribution of robotic devices to neuromotor rehabilitation
may turn out to be crucial. Moreover, different training conditions - either presence or
CONTROLLERCONTROLLER
Actual
PerfomancePerfomance
Target
PerfomancePerfomance
Degree of
assistance assistance
ROBOT+PATIENTROBOT+PATIENT
Actual
Target
Degree of

Figure 2. Mechanism of assistance regulation