Error Augmented Robotic Rehabilitation of the Upper Limb
A Review
Aris C. Alexoulis-Chrysovergis
1
, Andrew Weightman
1
,
Emma Hodson-Tole
2
and Frerik J. A. Deconinck
2
1
School of Engineering, Manchester Metropolitan University, Chester Street, Manchester, U.K.
2
School of Healthcare Science, Manchester Metropolitan University, Chester Street, Manchester, U.K.
Keywords: Robotic, Rehabilitation, Upper, Limb, Haptic, Visual, Feedbacks, Augmented, Enhanced, Review.
Abstract: Objective: To collect and assess the available evidence for the efficacy of error augmentation in upper limb
robotic rehabilitation.
Methods: A systematic literature search up to May 2013 was conducted in one citation index, the Web of
Knowledge, and in two individual databases: PubMed and Scopus, for publications that utilized error
augmented feedback as practice modality in robotic rehabilitation of the upper limb.
Results: The systematic search returned 12 studies that utilized error augmented feedback in trials to
unimpaired and impaired individuals suffering from stroke, multiple sclerosis and primary dystonia. One
additional study utilizing viscous force fields was included as the authors paid special merit to the effects of
the field in directions where the error was amplified. In the studies that met the inclusion criteria two
different types of error augmented feedback was used that is, haptic and visual feedback which were used
either separately as rehabilitation modalities or in conjunction with each other. All studies but one report
positive outcome regardless of the type(s) of feedback utilized.
Conclusions: Error augmentation in upper limb robotic rehabilitation is a relatively new area of study,
counting almost nine years after the first relevant publication and rather understudied. Error augmentation in
upper limb robotic rehabilitation should be further researched in more practice-intensive studies and with
larger trial groups. The potential of error augmented upper limb rehabilitation should also be explored with
conditions other than the ones described in this review.
1 INTRODUCTION
Neurological impairments resulting from conditions
such as stroke and cerebral palsy are common. For
example, stroke affects 150 000 people in the UK
each year (2005/2006 S.S.C.A., 2001 ) and cerebral
palsy is the commonest cause of childhood disability
in Europe (Reinkensmeyer et al., 2004; Huang and
Krakauer, 2009; Weightman et al., 2011).
Neurological impairment, resulting from these
pathologies, often influences upper limb function
causing weakness, spasticity and loss of selective
muscle activation. These in turn, cause difficulties
with voluntary movements and affect the ability to
reach, grasp transport
and manipulate objects.
Movements in affected individuals are therefore
characterized by increased duration, reduced peak
velocity, increased variability and fewer straight
hand trajectories (Wu et al., 2000).
Improvement in upper limb function can lead to
better performance in activities of daily living,
increased social integration and can thus produce a
better quality of life (Maher et al., 2007; Imms,
2008). Exercise of an impaired limb is known to
improve function (Kluzik et al., 1990), with better
performance observed with increased time and
amount of practice devoted to learning a particular
task (French et al., 2007). Traditionally such
exercises are monitored by a trained clinical
therapist. However, researchers have (recently)
begun to investigate the application of robotics as a
potential modality to support such rehabilitation.
The paradigm of upper limb rehabilitation
robotics is a motivating computer environment,
which promotes therapeutic movements of the
impaired limb with a powered interface
implementing a control algorithm to promote
recovery (Prange et al., 2006; Scott and Dukelow,
167
C. Alexoulis-Chrysovergis A., Weightman A., Hodson-Tole E. and J. A. Deconinck F..
Error Augmented Robotic Rehabilitation of the Upper Limb - A Review.
DOI: 10.5220/0004654101670178
In Proceedings of the International Congress on Neurotechnology, Electronics and Informatics (SensoryFusion-2013), pages 167-178
ISBN: 978-989-8565-80-8
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
2011). Such a system can provide patients with
access to rehabilitation protocols, which do not
require direct, time demanding, supervision of a
clinical therapist. As such they can increase access
to therapy with limited additional burden on
healthcare provision. Furthermore, such systems
enable the logging of valuable data regarding user’s
activity and performance for the therapist to closely
monitor adaptation and provide feedback on
progress to the user. Rehabilitation robotic therapy
has demonstrated statistically significant benefits in
improving upper limb function, with kinematic
analysis revealing benefits in movement time, path
and smoothness of reach (Fasoli et al., 2008; Huang
and Krakauer, 2009; Fluet et al., 2010, Weightman
et al., 2011, Norouzi-Gheidari et al., 2012).
Currently, three types of rehabilitation robot
have been described: i) end point attachment; ii)
multiple point attachment and iii) exoskeletons
(Reinkensmeyer et al., 2004; Jackson et al., 2007,
Scott and Dukelow, 2011; Weightman et al., 2011).
End point attachment robots are limited in that they
can only promote desirable trajectories (spatial and
temporal characteristics) of the hand and cannot
control the corresponding position of the elbow and
shoulder. However, they are likely to be more cost
effective than multiple point of attachment robots
and exoskeletons. Multiple point of attachment
robots and exoskeletons can control the full
kinematics of the arm (end point, elbow, shoulder)
but are usually significantly larger and more
expensive and as such are less likely to be utilized
outside the clinical environment; for example in
home rehabilitation applications where size and
price can be significant consideration factors for
employing such technology.
The control strategy i.e. the manner of
interaction between user and the powered
joysticks/robotics, implemented is critical for the
promotion of improved upper limb function
(Reinkensmeyer et al., 2004; Marchal-Crespo and
Reinkensmeyer, 2009) and different control
strategies have been utilised in the current literature.
Marchal-Crespo et al. (Marchal-Crespo and
Reinkensmeyer, 2009) suggested they can broadly
be divided into three groups. Firstly, assisting
control strategies help to move the impaired upper
limb in aiming type movements, this is similar to the
“active assist” type exercises utilised by therapists
(Marchal-Crespo and Reinkensmeyer, 2009;
Weightman et al., 2011). Secondly, challenge based
control strategies can make movements more
difficult, for example augmenting error between
actual and desired trajectory or promoting increased
effort (resistance training) from the participant.
Thirdly, haptic simulation strategies involve the user
practising activities of daily living within a virtual
haptic environment (Montagner et al., 2007,
Marchal-Crespo and Reinkensmeyer, 2009).
Challenge based algorithms such as, error
augmenting, are based on the concept that errors in
performance and hence results of aiming and
prehensile movements of the upper limb influence
motor adaptation (Wolpert et al., 1995; Patton et al.,
2006b). These strategies have been shown to
improve motor function in adults suffering from
stroke (Morris et al., 2004; Patton et al., 2006b).
Moreover, there have been early indications that
error augmented visual feedback can induce motor
learning in able bodied and possibly in impaired
individuals (Wei et al., 2005). In the last twenty
years, substantial work has been done in robotic
rehabilitation. Error augmentation seems to be a
relatively new modality and to our knowledge there
has not been an attempt to gather and collectively
report the findings of such studies. Therefore, the
purpose of this paper is to present a systematic
literature review of research regarding the use of
error augmented feedback in the robotic
rehabilitation of the upper limb and determine its
potential for promoting improved upper limb
function in those who have suffered a neurological
impairment.
2 METHODS
A systematic literature search up to May 2013 was
conducted in one citation index, the Web of
Knowledge, and in two individual databases:
PubMed and Scopus. In order to ensure that the
search would return as many results as possible two
different sets of keywords were used in each
database. No lower end in year was used in any
search. The keywords for the first set were: robot,
rehabilitation, upper, limb, error and the keywords
for the second set were: rehabilitation, upper, limb,
error. Papers identified in either search were
included for further investigation. To make sure that
significant publications were not missed during the
initial search the references of the retrieved studies
were checked for relevant publications. After
identifying and excluding duplicates, all abstracts
were reviewed and when necessary a full review of
the manuscript was undertaken.
The inclusion criteria for the review were studies
i) with upper limb robotic rehabilitation; ii) utilizing
error augmentation as a training modality, including
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all types of distorted feedback (haptic or visual); iii)
where trials on humans (impaired or able bodied)
were performed. Only papers reporting new
experimental data were included, however it should
be noted that the systematic search returned two
review papers referring to error augmented robotic
therapy in upper limb rehabilitation (Johnson, 2006;
Reinkensmeyer, 2009).
3 RESULTS
Out of 60 papers originally identified 12 met the
inclusion criteria. An exception was made with
study (Patton et al., 2006b) which didn’t meet the set
criteria for the review, because viscous force fields
were used in the study not an error augmentation.
However, the authors discussed the effects of the
treatment in the directions of the movement where
error was amplified. As such the study was
considered suitable for the purposes of this review
and therefore a total of 13 papers were reviewed.
An overview on the contents of the selected
papers can be found in Table 1.
3.1 Overview of Selected Studies
Error augmented robotic therapy for the
rehabilitation of the upper limb is a relatively new
rehabilitation modality, as the first relevant study
was undertaken in 2004 (Patton and Mussa-Ivaldi,
2004). Since then publications regarding this subject
are published with an average rate of 1.5
publications per year (Figure 1).
3.1.1 Clinical Characteristics
of the Participants
All included studies employed human participants
for clinical trials. The conditions that were addressed
varied significantly, with six studies focusing on
upper limb rehabilitation in stroke patients (Patton et
al., 2006a; Patton et al., 2006b; Cesqui et al., 2008;
Rozario et al., 2009; Abdollahi et al., 2011; Molier
et al., 2011), two studies employing participants
with multiple sclerosis (Squeri et al., 2007b; Vergaro
et al., 2010) and one study (Casellato et al., 2012)
employing error augmented robotic therapy in
children with primary dystonia. Furthermore, four
studies experimented in the effects of error
augmented robotic therapy with the participation of
only able bodied, healthy adults (Patton and Mussa-
Ivaldi, 2004; Matsuoka et al., 2007; Wang et al.,
2010; Shirzad et al., 2012).
Figure 1: Number of publications on error augmented
robotic therapy on the upper limb.
3.1.2 Types of Rehabilitation Robots
Interestingly all but two (Patton et al., 2006b; Molier
et al., 2011), studies used single point of attachment
robotic systems (endpoint). In one study two
endpoint robotic devices were utilized to control the
thumb and the index finger of the participants in
pinching movements (Matsuoka et al., 2007) while
in another study a multiple point of attachment
system (exoskeleton) was used for the control of arm
movements (Molier et al., 2011).
3.1.3 Types of Error augmented Feedback
Two different types of feedback, where error was
augmented, were identified among the selected
studies. The approaches can be categorized as: a)
Error augmented haptic feedback, where forces
perturbed upper limb movement when a certain level
of error away from the desired trajectory was
reached (Patton and Mussa-Ivaldi, 2004; Patton et
al., 2006a; Patton et al., 2006b; Squeri et al., 2007b,
Cesqui et al., 2008; Vergaro et al., 2010; Abdollahi
et al., 2011; Molier et al., 2011; Casellato et al.,
2012); b) Error augmented visual feedback, where
the visual output of the system was distorted by a
factor (ε) in order for the actual distance between the
arm and the target, to differ from the one perceived
by the user (Matsuoka et al., 2007; Wang et al.,
2010); c) A combination of a and b where error in
visual and haptic feedback was augmented (Rozario
et al., 2009; Shirzad et al., 2012).
3.2 Intervention Modalities
The main concept of the intervention behind all the
reviewed studies was that a user was positioned in
front of a computer screen while a robotic
manipulandum was attached to/held by the
participant’s upper limb. A target would be
displayed while visual feedback about the current
position of the arm was provided to the user. The
user was asked to perform movements towards
0
1
2
3
2000 2002 2004 2006 2008 2010 2012
Yearpublished
Numberofpublications
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predefined targets while the system responded to the
users’ movement by augmenting any error.
In some of the reviewed studies (Patton and
Mussa-Ivaldi, 2004; Squeri et al., 2007b; Cesqui et
al., 2008; Vergaro et al., 2010; Casellato et al., 2012)
haptic error augmenting algorithms were compared
against other types of haptic algorithms namely,
error reducing haptic algorithms. Error reducing
algorithms are adaptive assistive algorithms which
apply forces towards the optimal trajectory when a
threshold of error is reached. In the aforementioned
studies the two different types of haptic algorithms
were either administered to different trial groups or
in the same group but in different stages of the trial
in order for a comparison between the two training
modalities to be feasible. There was one study
(Molier et al., 2011) where restraining forces only
occurred when a certain amount of error was
reached in order to provide position feedback to the
user. In this case the forces were turned off when the
user didn’t exceed a predefined error threshold.
There was great variance in the number of
sessions and the total exercise time the participants
undertook, among the studies. In several cases the
total intervention time was administered in one
session (Johnson, 2006; Patton et al., 2006b;
Matsuoka et al., 2007; Casellato et al., 2012; Shirzad
et al., 2012) while in others the number of sessions
varied from a minimum of 2 sessions (Wang et al.,
2010; Molier et al., 2011) to a maximum 10 sessions
(Cesqui et al., 2008). Moreover, the total time of
exercise administered varied significantly from as
little as 90 minutes (Shirzad et al., 2012) to as much
as 20hours (Cesqui et al., 2008). Additionally, some
studies induced a washout component in the practice
regime either by including a washout cycle in the
practice session where the perturbative forces were
gradually removed (Patton and Mussa-Ivaldi, 2004;
Patton et al., 2006a; Casellato et al., 2012), or by
setting a washout period between trials where no
practice was undertaken (Cesqui et al., 2008;
Rozario et al., 2009, Wang et al., 2010).
Table 2 provides an overview on the practice
schemes administered in the reviewed studies.
3.3 Outcome Measures
The most common clinical measures among the
studies that were used to evaluate outcome on stroke
patients were the Fugl-Meyer scale, the Modified
Ashworth scale for spasticity and the Box and Block
test. Other clinical measures used can be found in
Table 3. In the above-mentioned studies kinematic
data were collected namely, error that is to say the
deviation between the actual and desired trajectory,
jerk index (Squeri et al., 2007a), Jerk (Teulings’)
index (Teulings et al., 1997), and strength (Patton et
al., 2006a; Patton et al., 2006b; Cesqui et al., 2008;
Rozario et al., 2009; Abdollahi et al., 2011; Molier
et al., 2011).
Both studies that performed trials in patients with
multiple sclerosis (Squeri et al., 2007a; Vergaro et
al., 2010) evaluated performance with clinical
measures such as Expanded Disability Status Scale
(EDSS), Ataxia and Tremor scale, Nine Hole Peg
Test, Visual Analogue Scale (VAS) and the Tremor
and Activity of Daily Life (TADL) questionnaire as
well as kinematic such as, lateral deviation (root
mean square value) from the nominal path,
movement duration (seconds), symmetry (ration
between acceleration and deceleration phases) and
smoothness.
Likewise, studies that employed only able-
bodied participants used only kinematic measures
like error (distance between actual and desired
directory, lateral deviation etc.), mean lag (Matsuoka
et al., 2007) and times needed assistance. The times
needed assistance measure was used in one study
(Wang et al., 2010) where visual error augmentation
was utilized. In this studies training scheme the
system would assist movement only when a
threshold in error was reached. In one study
(Shirzad et al., 2012) a
Self-Assessment Manikin
(SAM) affect questionnaire was administered.
Finally, in the study where trials on children with
primary dystonia were performed (Casellato et al.,
2012) only clinical measures were used that is to
say, Burke-Fahn-Marsden Dystonia Rating Scale
(BFMDRS).
A more detailed overview of the outcome
measures used according to condition can be found
in Table 3.
3.4 Impact on Motor Learning
and Upper Limb Function
Out of the 13 reviewed studies 12 report positive
impact on upper limb function, five of which report
conclusive results (Squeri et al., 2007a; Cesqui et al.,
2008; Vergaro et al., 2010; Abdollahi et al., 2011;
Molier et al., 2011) and seven (Patton and Mussa-
Ivaldi, 2004; Patton et al., 2006a; Patton et al.,
2006b; Matsuoka et al., 2007; Rozario et al., 2009;
Wang et al., 2010; Casellato et al., 2012) report
inconclusive but positive results (Table 1).
Inconclusive results were considered as, those
results where the experiment did not have significant
statistical power for definitive conclusions to be
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drawn and the results where the authors couldn’t
definitively link improvements to the error
augmented treatment.
In one study (Shirzad et al., 2012) the authors
concluded that there was no significant impact of the
intervention on motor learning but when the
different training modes employed in the study were
compared, motor learning was improved only when
haptic error augmentation was combined with visual
error augmentation. In the study where a viscous
force field were used (Patton et al., 2006b), the
authors concluded that significant positive effects
were only encountered in the directions where error
was amplified.
4 DISCUSSION
In this review 13 studies were qualitatively analysed
regarding the effects of error augmented feedback on
robotic rehabilitation of the upper limb. The
reviewed studies employed error augmented therapy
either in the form of haptic or visual feedback or a
combination of the two. Trials were conducted on
healthy participants or on adult participants suffering
from the effects of stroke or multiple sclerosis or
children with primary dystonia.
The first identified study utilizing error
augmentation in the robotic rehabilitation of the
upper limb was published in 2004 (Patton and
Mussa-Ivaldi, 2004). In the nine years since this first
study by Patton et. al was published we could only
retrieve twelve additional studies regarding error
augmentation in the rehabilitation of the upper limb.
4.1 Clinical Trial Protocols
The design of the trial protocols implemented in the
reviewed studies varied significantly as did the
intervention time and group formation. Five of the
studies (Squeri et al., 2007a; Cesqui et al., 2008;
Rozario et al., 2009; Vergaro et al., 2010, Abdollahi
et al., 2011) employed a crossover protocol where
the same group was exposed to different training
modalities with a two week washout period between
the two. Furthermore, six studies used single session
trials (Patton and Mussa-Ivaldi, 2004; Patton et al.,
2006a; Patton et al., 2006b; Matsuoka et al., 2007;
Casellato et al., 2012; Shirzad et al., 2012) with the
total practice time spanning from as little as 22 min
(Patton and Mussa-Ivaldi, 2004) to as much as 96
min (Patton et al., 2006a). Interestingly, only one
study utilized a randomized control clinical trial
(RCT) protocol (Patton et al., 2006b).
Although, the reviewed studies have presented
positive indications of the benefits of the error
augmented robotic therapy to the rehabilitation of
the upper limb, many of the authors argue that more
conclusive outcomes could have been produced if
their studies had larger numbers of participants and
provided more sessions with more practice intensive
protocols. Furthermore, the design of the trial
protocols seems to be a significant factor that
influences the trial outcome. As such trials designed
under a Random Control Trial (RCT) protocol,
where a well-established haptic control algorithm
would be compared to an error augmenting haptic
algorithm, could potentially provide more definitive
results (Dobkin, 2004).
4.2 Error Augmented Feedback
in Upper Limb Rehabilitation
4.2.1 Success of Error Augmented Haptic
Feedback Trials
By studying the results of the trials that utilized
haptic error augmentation one can conclude that the
different conditions are affected differently by this
modality. Stroke patients seem to be more positively
affected by haptic error augmentation exercises as
all studies that performed such experiments on
stroke patients conclude that the group that received
error augmented therapy showed improvement in the
function of the paretic limb. However, such a
statement cannot be definitively made as from the
reviewed studies, the ones that performed trials on
participants suffering from primary dystonia and
multiple sclerosis were significantly less than the
trials on stroke patients. Therefore, the reviewed
studies cannot be compared directly in terms of the
outcome for individuals with different conditions.
More specifically, studies (Cesqui et al., 2008,
Abdollahi et al., 2011; Molier et al., 2011)
conclusively report that the patients who received
error augmented therapy were positively affected. In
study (Rozario et al., 2009) the authors report that
while the kinematic measures indicate improvement,
clinical measures did not provide any measurable
change in the performance and they suggest that
results were probably hindered by the small trial
group and the small number of sessions. The
difference in the outcome of kinematic and the
clinical measures may be due to the fact that
kinematic measures in most cases provide better
responsiveness, that is they are more capable of
accurately detecting changes over time, than clinical
scales (Sivan et al., 2011), hence are more sensitive
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detectors of change.
Both studies that employed participants with
multiple sclerosis report positive outcomes. Study
(Squeri et al., 2007a) concluded that at the end of the
sessions the participants exhibited faster, smoother
and more symmetric movement. On the other hand,
study (Vergaro et al., 2010) presents similar results
but did not indicate significant differences on the
outcome between error reducing and error
augmenting therapy, with the only exception being a
reduction in a tremor related clinical measure which
occurred only after error augmented therapy. As
such, the improvement presented in both studies
may be due to the fact that the participants
experienced the positive effects of adaptation in a
dynamic environment regardless of the conditions
applied within that environment.
With regards to children suffering from primary
dystonia (Casellato et al., 2012) results indicate
improvement in terms of optimal path control which
as the authors suggest may be due to a refinement in
the existing sensorimotor patterns of the impaired
participants rather than due to motor learning. In the
trials involving participation of able bodied
individuals (Patton and Mussa-Ivaldi, 2004; Shirzad
et al., 2012), the participants could adapt their
movement to the altered environment. However, in
(Patton and Mussa-Ivaldi, 2004) there was no clear
difference of the effects of error augmenting therapy
when compared to those from error reducing
therapy. Finally, subjects in (Shirzad et al., 2012)
showed improved in satisfaction, attentiveness and
dominance when they were introduced to augmented
error conditions despite of the type of feedback
where error was augmented, but didn’t show
improvements on their performance. Both trials
utilized a single session training scheme with
relatively small number of repetitions that may have
not allowed significant changes in motor adaptation
to occur.
4.2.2 Success of Error augmented Visual
Feedback Trials
The studies that used error augmentation in visual
feedback, were significantly less than the ones that
made use of error augmented haptic feedback. It
should be noted that in three out four studies where
visual feedback distortion was used, only able
bodied participants were employed as such it is
difficult to draw conclusions on whether the results
would transfer to the motor impaired.
Nevertheless, only one study (Matsuoka et al.,
2007) reports positive outcome when error was
augmented in the visual feedback as it allowed a
new coordination pattern to transfer to the trials with
no feedback distortion and reduced error. Study
(Rozario et al., 2009) didn’t provide statistically
significant results but indicates that for some of the
participants’, error was reduced when they were
exposed to error augmented training.
4.3 Comparison of Haptic Error
Augmented Therapy to other
Haptic Therapy
In the studies where the performance of haptic error
augmented therapy was compared with haptic error
reducing therapy (Squeri et al., 2007a; Cesqui et al.,
2008; Vergaro et al., 2010) all studies report that
there was no clear indication for the prevalence of
one approach over the other. An interesting outcome
came from the study where viscous force fields were
used (Patton et al., 2006b) as the authors conclude
that most of the improvement in function occurred in
the directions of the field where errors where
amplified.
5 CONCLUSSIONS
Error augmentation in upper limb robotic
rehabilitation is a relatively new area of study,
counting almost nine years since the first relevant
publication, and a rather understudied one. Despite
the small number of publications that have employed
this modality, there are some clear indications about
its potential benefits. The evidence gathered from
this review indicate that stroke patients received the
most benefit from haptic error augmented therapy
but no clear conclusions were drawn whether this
training modality has significant benefits on stroke
patients, over other established modalities such as
error reducing or assistive therapy.
We suggest that large scale randomized control
trials be undertaken in order to explore the prospects
of haptic error augmentation and fully evaluate its
effectiveness on upper limb robotic rehabilitation. In
these trials error augmented therapy should be
compared against other, more established training
schemes. Furthermore, we suggest that the impact of
error augmented therapy should be explored in
conditions that share similar symptoms related to
neuromuscular control to stroke, such as cerebral
palsy. Understanding the neurological mechanisms
targeted by different therapies, in terms of both
learning and motor performance, could provide
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greater insight into their potential efficacy in a range
of different pathologies and is an important
consideration for future studies. Likewise, we would
like to encourage scientists to perform trials on
impaired subjects where error augmentation on
visual feedback will be implemented as the results of
this review indicate that this modality hasn’t been
researched to its full capacity.
Guidelines on trial design and dose
administration for rehabilitation of the upper limb in
conditions such as stroke have been presented in
literature (Dobkin, 2004). To the author’s
knowledge, reviews on the outcome measures for
robotic rehabilitation of the upper limb in conditions
such as cerebral palsy, primary dystonia and
multiple sclerosis have not yet been conducted,
while one review regarding such measures has been
undertaken for the rehabilitation for the upper limb
in stroke patients (Sivan et al., 2011).
This review has identified that there is no
uniform condition-specific trial design or evaluation
protocol as different intervention protocols and
different measures have been used in trials with
participants of the same condition. As a result of this
a comparison between trials and their outcomes is
difficult. Adoption of standard outcome measures
would enable inter-study evaluation and help to
progress this area of research significantly. As
robotic rehabilitation of the upper limb is getting
more and more accepted by the scientific community
as a valid rehabilitation modality, we believe that
uniform condition-specific trial protocol guidelines
should be established, in order to enable researchers
to easily evaluate the outcome of relevant studies in
literature and allow them to compare the outcome of
their studies against that of others.
REFERENCES
2005/2006 S.S.C.A., 2001 Stroke incidence and risk
factors in a population based cohort study. ONS
Health Stats Quart (12).
Abdollahi, F., Rozario, S. V., Kenyon, R. V., Patton, J. L.,
Case, E., Kovic, M. & Listenberger, M., 2011. Arm
control recovery enhanced by error augmentation.
IEEE Int Conf Rehabil Robot, 2011, 5975504.
Casellato, C., Pedrocchi, A., Zorzi, G., Rizzi, G., Ferrigno,
G. & Nardocci, N., 2012. Error-enhancing robot
therapy to induce motor control improvement in
childhood onset primary dystonia. Journal of
Neuroengineering and Rehabilitation, 9.
Cesqui, B., Aliboni, S., Mazzoleni, S., Carrozza, M. C.,
Posteraro, F., Micera, S. & Ieee, 2008. On the Use of
Divergent Force Fields in Robot-Mediated
Neurorehabilitation. 2008 2nd Ieee Ras & Embs
International Conference on Biomedical Robotics and
Biomechatronics. 942-949.
Dobkin, B. H., 2004. Strategies for stroke rehabilitation.
Lancet Neurology, 3, 528-536.
Fasoli, S. E., Fragala-Pinkham, M., Hughes, R., Hogan,
N., Krebs, H. I. & Stein, J., 2008. Upper limb robotic
therapy for children with hemiplegia. Am J Phys Med
Rehabil, 87, 929-36.
Fluet, G. G., Qiu, Q., Kelly, D., Parikh, H. D., Ramirez,
D., Saleh, S. & Adamovich, S. V., 2010. Interfacing a
haptic robotic system with complex virtual
environments to treat impaired upper extremity motor
function in children with cerebral palsy. Dev
Neurorehabil, 13, 335-45.
French, B., Thomas, L. H., Leathley, M. J., Sutton, C. J.,
Mcadam, J., Forster, A., Langhorne, P., Price, C. I. M.,
Walker, A. & Watkins, C. L., 2007. Repetitive task
training for improving functional ability after stroke.
Cochrane Database of Systematic Reviews.
Huang, V. S. & Krakauer, J. W., 2009. Robotic
neurorehabilitation: a computational motor learning
perspective. J Neuroeng Rehabil, 6, 5.
Imms, C., 2008. Children with cerebral palsy participate:
A review of the literature. Disability and
Rehabilitation, 30, 1867-1884.
Jackson, A. E., Holt, R. J., Culmer, P. R., Makower, S. G.,
Levesley, M. C., Richardson, R.C., Cozens, J. A.,
Williams, M. M. & Bhakta, B. B., 2007. Dual robot
system for upper limb rehabilitation after stroke: The
design process. Proceedings of the Institution of
Mechanical Engineers, Part C: Journal of Mechanical
Engineering Science, 221, 845-857.
Johnson, M. J., 2006. Recent trends in robot-assisted
therapy environments to improve real-life functional
performance after stroke. Journal of Neuroengineering
and Rehabilitation, 3.
Kluzik, J., Fetters, L. & Coryell, J., 1990. Quantification
of control: a preliminary study of effects of
neurodevelopmental treatment on reaching in children
with spastic Cerebral Palsy. Physical Therapy, 70, 65-
76.
Maher, C. A., Williams, M. T., Olds, T. & Lane, A. E.,
2007. Physical and sedentary activity in adolescents
with cerebral palsy. Developmental Medicine and
Child Neurology, 49, 450-457.
Marchal-Crespo, L. & Reinkensmeyer, D. J., 2009.
Review of control strategies for robotic movement
training after neurologic injury. Journal of
Neuroengineering and Rehabilitation, 6.
Matsuoka, Y., Brewer, B. R. & Klatzky, R. L., 2007.
Using visual feedback distortion to alter coordinated
pinching patterns for robotic rehabilitation. J
Neuroeng Rehabil, 4, 17.
Molier, B. I., Prange, G. B., Krabben, T., Stienen, A. H.
A., Van Der Kooij, H., Buurke, J. H., Jannink, M. J.
A. & Hermens, H. J., 2011. Effect of position
feedback during task-oriented upper-limb training after
stroke: Five-case pilot study. Journal of Rehabilitation
Research and Development, 48, 1109-1117.
ErrorAugmentedRoboticRehabilitationoftheUpperLimb-AReview
173
Montagner, A., Frisoli, A., Borelli, L., Procopio, C.,
Bergamasco, M., Carboncini, M. C. & Rossi, B., Year.
A pilot clinical study on robotic assisted rehabilitation
in VR with an arm exoskeleton deviceed.^eds. Virtual
Rehabilitation, 2007, 57-64.
Morris, S. L., Dodd, K. J. & Morris, M. E., 2004.
Outcomes of progressive resistance strength training
following stroke: a systematic review. Clin Rehabil,
18, 27-39.
Norouzi-Gheidari, N., Archambault, P. S. & Fung, J.,
2012. Effects of robot-assisted therapy on stroke
rehabilitation in upper limbs: systematic review and
meta-analysis of the literature. Journal of Rehabilitation
Research and Development, 49, 479-96.
Patton, J. L., Kovic, M. & Mussa-Ivaldi, F. A., 2006a.
Custom-designed haptic training for restoring reaching
ability to individuals with poststroke hemiparesis. J
Rehabil Res Dev, 43, 643-56.
Patton, J. L. & Mussa-Ivaldi, F. A., 2004. Robot-assisted
adaptive training: custom force fields for teaching
movement patterns. IEEE Trans Biomed Eng, 51, 636-46.
Patton, J. L., Stoykov, M. E., Kovic, M. & Mussa-Ivaldi,
F. A., 2006b. Evaluation of robotic training forces that
either enhance or reduce error in chronic hemiparetic
stroke survivors. Experimental Brain Research, 168,
368-383.
Prange, G. B., Jannink, M. J. A., Groothuis-Oudshoorn, C.
G. M., Hermens, H.J. & Ijzerman, M. J., 2006.
Systematic review of the effect of robot-aided therapy
on recovery of the hemiparetic arm after stroke.
Journal of Rehabilitation Research and Development,
43, 171-183.
Reinkensmeyer, D. J., 2009. Robotic Assistance for Upper
Extremity Training after Stroke. In A. Gaggioli, E. A.
Keshner, P. L. Weiss & G. Riva (eds.) Advanced
Technologies in Rehabilitation: Empowering
Cognitive, Physical, Social and Communicative Skills
through Virtual Reality, Robots, Wearable Systems
and Brain-Computer Interfaces. 25-39.
Reinkensmeyer, D. J., Emken, J. L. & Cramer, S. C.,
2004. Robotics, motor learning, and neurologic
recovery. Annual Review of Biomedical Engineering,
6, 497-525.
Rozario, S. V., Housman, S., Kovic, M., Kenyon, R. V. &
Patton, J. L., 2009. Therapist-mediated post-stroke
rehabilitation using haptic/graphic error augmentation.
Conf Proc IEEE Eng Med Biol Soc, 2009, 1151-6.
Scott, S. H. & Dukelow, S. P., 2011. Potential of robots as
next-generation technology for clinical assessment of
neurological disorders and upper-limb therapy.
Journal of Rehabilitation Research and Development,
48, 335-353.
Shirzad, N., Van Der Loos, H. F. M. & Ieee, 2012. Error
Amplification to Promote Motor Learning and
Motivation in Therapy Robotics. 2012 Annual
International Conference of the Ieee Engineering in
Medicine and Biology Society. 3907-3910.
Sivan, M., O'connor, R. J., Makower, S., Levesley, M. &
Bhakta, B., 2011. Systematic review of outcome
measures used in the evaluation of robot-assisted
upper limb exercise in stroke. J Rehabil Med, 43, 181-
9.
Squeri, V., Vergaro, E., Brichetto, G., Casadio, M.,
Morasso, P. G., Solaro, C. & Sanguineti, V., Year.
Adaptive robot training in the rehabilitation of
incoordination in Multiple Sclerosis: A pilot
studyed.^eds., Noordwijk, 364-370.
Squeri, V., Vergaro, E., Brichetto, G., Casadio, M.,
Morasso, P. G., Solaro, C., Sanguineti, V. & Ieee,
2007b. Adaptive robot training in the rehabilitation of
incoordination in Multiple Sclerosis: a pilot study.
2007 Ieee 10th International Conference on
Rehabilitation Robotics, Vols 1 and 2. 364-370.
Teulings, H. L., Contrerasvidal, J. L., Stelmach, G. E. &
Adler, C. H., 1997. Parkinsonism reduces coordination
of fingers, wrist, and arm in fine motor control.
Experimental Neurology, 146, 159-170.
Vergaro, E., Squeri, V., Brichetto, G., Casadio, M.,
Morasso, P., Solaro, C. & Sanguineti, V., 2010.
Adaptive robot training for the treatment of
incoordination in Multiple Sclerosis. J Neuroeng
Rehabil, 7, 37.
Wang, F., Barkana, D. E. & Sarkar, N., 2010. Impact of
visual error augmentation when integrated with assist-
as-needed training method in robot-assisted
rehabilitation. IEEE Trans Neural Syst Rehabil Eng,
18, 571-9.
Wei, Y., Bajaj, P., Scheidt, R., Patton, J. & Ieee, 2005.
Visual error augmentation for enhancing motor
learning and rehabilitative relearning. 2005 Ieee 9th
International Conference on Rehabilitation Robotics.
New York: Ieee, 505-510.
Weightman, A., Preston, N., Levesley, M., Holt, R., Mon-
Williams, M., Clarke, M., Cozens, A. J. & Bhakta, B.,
2011. Home based computer-assisted upper limb
exercise for young children with cerebral palsy: a
feasibility study investigating impact on motor control
and functional outcome. Journal of Rehabilitation
Medicine, 43, 359-363.
Wolpert, D. M., Ghahramani, Z. & Jordan, M. I., 1995. An
internal model for sensorimotor integration. Science,
269, 1880-1882.
Wu, C. Y., Trombly, C. A., Lin, K. C. & Tickle-Degnen,
L., 2000. A kinematic study of contextual effects on
reaching performance in persons with and without
stroke: Influences of object availability. Archives of
Physical Medicine and Rehabilitation, 81, 95-101.
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APPENDIX
Table 1: Overview of the contents of the reviewed studies.
Study
Year
published
Type of
Condition
Type of
Robot
Type of
control
Type of
feedback
altered
Number of
participants (N)
(E = experimental
group
C = control group)
Time
post-
stroke
(months)
Amount of practice
Impact on
upper limb
function
(Patton and
Mussa-Ivaldi, 2004)
2004 n/a endpoint
error
reducing
error
enhancing
haptic 8 able bodied n/a
Total movements:
871 (1 session)
Total time practiced:
22 min (1 session for
22 min)
positive
inconclusive
(Patton et al.,
2006a)
2006 stroke endpoint
error
enhancing
haptic
15 impaired
E: 12 C: 9
19-132
Total movements: 742
movements (1 session)
Total time practiced:
180 min (1 session of
80 min)
positive
inconclusive
(Patton et al.,
2006b)
2006 stroke endpoint
viscous
force field
haptic
Total: 31
E:27 impaired
C:4 able bodied
16-173
Total movements: 834
(1 session)
Total time practiced:
57 min (1 session of
57 min)
positive
inconclusive
(Matsuoka et al.,
2007)
2007 n/a
endpoint
(fingers)
n/a visual 51 able bodied n/a
Total movements: 920
movements (1 session)
Total time practiced:
Unknown
positive
inconclusive
(Squeri et al.,
2007a)
2007
Multiple
sclerosis
endpoint
error
enhancing
error
reducing
haptic 4 impaired n/a
Total movements:
3680 (4 sessions)
Total time practiced:
240 min (4 sessions of
60 min)
positive
(Cesqui et al., 2008) 2008 stroke endpoint
active
assistive
error
enhancing
haptic 15 impaired n/a
Total movements:
Unknown
Total time practiced:
600 min (10 sessions
of 60 min)
positive
(Rozario et al.,
2009)
2009 stroke endpoint
error
enhancing
haptic/
visual
5 impaired 6 months
Total movements:
Unknown
Total time practiced:
240 min (6 sessions of
40 min)
positive
inconclusive
(Vergaro et al.,
2010)
2010
Multiple
sclerosis
endpoint
error
enhancing
error
reducing
haptic 8 impaired n/a
Total movements:
1992 (4 sessions)
Total time practiced:
240 min (4 sessions of
60 min)
positive
(Wang et al., 2010) 2010 n/a endpoint assistive visual 20 able bodied n/a
Total movements:
50 (2 sessions)
Total time practiced:
Unknown
positive
inconclusive
(Abdollahi et al.,
2011)
2011 stroke endpoint
error
enhancing
haptic 19 impaired 6-259
Total movements:
Unknown
Total time practiced:
360 min (6 sessions of
60 min)
positive
(Molier et al., 2011) 2011 stroke
exoskelet
on
none
(haptic
feedback
for error)
haptic 5 impaired 20-51
Total movements:
Unknown (2 sessions)
Total time practiced:
540 (18 sessions of 30
min)
positive
(Casellato et al.,
2012)
2012
primary
dystonia
endpoint
null
additive
force
constant
disturbing
force
haptic
Total: 22
11 impaired
11 able bodied
n/a
Total movements:
55 (1 session)
positive
inconclusive
(Shirzad et al.,
2012)
2012 n/a Endpoint
error
enhancing
haptic/
visual
10 able bodied n/a
Total movements:
129 (1 session)
Total time practiced:
90 minutes (1 session
of 90 min)
no effect
ErrorAugmentedRoboticRehabilitationoftheUpperLimb-AReview
175
Table 2: Overview of the contents of the reviewed studies.
Study
Number
of
sessions
Interven
tion time
in a
session
Total
number of
movements
in a session
Number of repetitions under
feedback distortion (haptic, visual
or both) generation in a session
Time trained in error
augmentation in a
session
Washout
(Patton and
Mussa-
Ivaldi,
2004)
1
21.95
min
871
a) with intermittent perturbations =
298
b) constant exposure = 330
c) random intermittent removal of
the force field
Total = 748
a) 7.50 min
b) 8.33 min
c) 3.00 min
Total = 18.83 min
75 movements (1.83 min) at the end of
the session
(Patton et
al., 2006a)
1
95.75
min
742
a) machine learning = 200
b) learning (opposite to the learned
forces) = 222
c) aftereffects catch intermittent
removal of the force field = 80
d) sane as c = 80
e) same as b = 2
Total = 584
a) 25.00 min
b) 30.00 min
c) 20.00 min
d) 10.00 min
e) 0.25 min
Total = 85.25 min
50 movements (3.00 min) at the end of
the session
(Patton et
al., 2006b)
1
57.00
min
834 n/a n/a
120 movements (8.00 min) at the end of
the session
(Matsuoka
et al.,
2007)
1 n/a 920
a) Index-Thumb-Both (ITB)
distortion =120
b) Thumb-Index-Both distortion
(TIB) =120
c) Thumb only condition mirroring
ITB = 40
d) Thumb only condition mirroring
TIB =40
Total = 320
n/a n/a
(Squeri et
al., 2007a)
4
60.00
min
498
a) Robot learning = 120
b) Trial = 120
c) Training and catch trials = 168
Total = 408
approx. 49.00 min
45 movements at the end of the session
2 weeks after 4 sessions before protocol
change
(Cesqui et
al., 2008)
20
60.00
min
n/a n/a 60.00 min
2 weeks after 10 sessions before
protocol change
(Rozario et
al., 2009)
6
40.00
min
n/a n/a 35.00 min
2 weeks after 6 sessions before protocol
change
(Vergaro et
al., 2010)
8
60.00
min
498
a) Robot training = 120
b) Subject training = 288
approx. 37.00 min
2 weeks after 4 sessions before protocol
change
(Wang et
al., 2010)
2 n/a 25 Total = 25 n/a n/a
(Abdollahi
et al.,
2011)
12
60.00
min
n/a n/a 30.00 min
2 weeks after 6 sessions before protocol
change
(Molier et
al., 2011)
18
30.00
min
n/a n/a 30.00 min n/a
(Casellato
et al.,
2012)
1 n/a 55
a) Null additive force = 15
b) Disturbing force =15
c) Deactivation of additive external
force = 15
Total = 45
n/a n/a
(Shirzad et
al., 2012)
1
90.00
min
129 Total = 65 approx. 45.00 min
10 movements at the beginning every
training block
(5 cycles/session)
NEUROTECHNIX2013-InternationalCongressonNeurotechnology,ElectronicsandInformatics
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Table 3: Overview of the practice administered in the reviewed studies.
Study
Type of
control
Type of
Condition
Outcome measures Statistical measurable impact Author conclusions
(Patton
and
Mussa-
Ivaldi,
2004)
error
enhancing
n/a
Kinematic:
Error, speed
• The subjects’ trajectories shifted significantly
towards the desired trajectories
(p< 0.05)
• Clinical improvement for adaptive therapy
• No clear difference between error reducing
and error enhancing therapy
(Patton
et al.,
2006a)
error
enhancing
stroke
Clinical:
Fugl-Meyer, MAS
Kinematics: Error
• All but one of the treatment groups movements
showed beneficial aftereffects
• Average reduction in error of –54%, (p < 0.05)
• Treatment group, FM scores marginally increased
an average of 1.6 (p = 0.06). No such
improvement was seen in the control group
(p > 0.27).
• The stroke group: movements showed
beneficial aftereffects after training (error
decreased) that persisted in all but three
patients
• This persistence was twice as long as for
nondisabled people
(Patton
et al.,
2006b)
viscous
force field
stroke
Clinical: Chedoke
stage of Arm, Elbow
modified Ashworth
Spasticity scale, F-M
• The after-effect was significant but 26% smaller
that the healthy subjects (confidence 95%)
• Significant improvements occurred only when the
training forces magnified the original errors
F(1,13) = 4,29 (p<0.001)
• For movement directions that begin with
significant errors, significant improvement
occurred only when the training forces
magnified the original errors
(Matsuo
ka et al.,
2007)
n/a n/a n/a
• The mean total absolute error for at the first
training block was significantly different for the
last block (p = 0.001)
• No significant results for final performance
change at the end of the trial (p = 0.99)
• Training under visual feedback allowed
new coordination pattern to transfer to no-
feedback trials
• Feedback distortion changed the amount
of error reduced for each finger separately,
and altogether
• Distorting individual fingers separately (or
together) did not affect the overall speed
of learning in movement error reduction.
(Squeri
et al.,
2007a)
error
enhancing
error
reducing
Multiple
sclerosis
Clinical: MMSE,
EDSS, NRS, Scripps',
Aswhorth, Ataxia and
Tremor scale, NHPT,
VAS, TADL
Kinematic: Max
lateral deviation,
movement duration,
symmetry, jerk index
• First to last session: highly significant
(p=0,000027) decrease in duration and significant
increase (p=0.031) in speed profile symmetry
• Error enhancing vs error reducing: first-last
sessions profile symmetry (p= 0.006) and
trajectory smoothness (p = 0.05) increased in
error enhancing
• Decrease in NHPT score overall F(1,3)=42.133
(p=0.007)
• Analysis of motor performance reveals
that, at the end of a training session,
movements are faster, smoother and have
a more symmetric speed profile
• Smoothness improved over sessions
(Cesqui
et al.,
2008)
active
assistive
error
enhancing
stroke
Clinical: MSS, MAS,
ROM, Mc-Master
Stroke Assessment
Kinematic:
Smoothness, accuracy,
path length ratio,
movements direction
variability
• Robotic-aided therapy led to a significant
reduction in impairment of the hemiparetic limbs,
as shown by the evolution of the MSS and MAS
throughout the therapy (no p-value was provided)
• Group I: final metric indexes were no different
(F=1.61, p = 0.194)
• Group II: final metric indexes were significantly
different (F=9,46, p = 0.006)
• Post-stroke patients were able to contrast
the perturbation field, i.e., they could reach
the target, and perform the exercise (varies
dependent on the severity of the
impairment)
• Improvements were higher depending on
the admission upper limb severity level
(Rozari
o et al.,
2009)
error
enhancing
stroke
Clinical: FM, WFMT,
FAS, box and blocks
Kinematic: Range of
motion error
• ROM assessment exhibited a floor effect, where
subjects that initially demonstrated fairly low
reaching errors did not significantly improve their
accuracy in reaching to target
• ROM test did reduce for three subjects in the
error augmented and control treatment groups.
• Error for two of the three subjects was
significantly decreased following error
augmentation treatment compared to control
treatment.
• Subjects provided with error augmentation during
the first phase of treatment produced greater
performance improvements
• No significant improvement, deterioration, or
notable trends were demonstrated with the
clinical measures.
(no statistical analysis was performed
• the two week treatment blocks might not
be sufficient to provide any measurable
change clinically, small number of
subjects (five) is not sufficient to draw any
definitive conclusion
• control treatment, while not providing
error-augmentation, still improved
functionality
• MS subjects adapt to unfamiliar dynamic
environments
• Improvements from error measures don’t
correlate with clinical measures
ErrorAugmentedRoboticRehabilitationoftheUpperLimb-AReview
177
Table 4: Overview of the practice administered in the reviewed studies (cont.).
Study
Type of
control
Type of
Condition
Outcome measures Statistical measurable impact Author conclusions
(Vergaro
et al.,
2010)
error
enhancing
error
reducing
Multiple
sclerosis
Clinical: EDSS and
Functional Systems
Score, Scripps’
,NRS, Ashworth
scale, Ataxia and
Tremor scales,
NHPT, VAS,TADL
Kinematic: Lateral
deviation, duration,
Symmetry, Jerk
• Significant effect of period (F(1,6) = 16.004;
p = 0.00283).
• NHPT change from baseline (T0) to the end of
the treatment (T3), irrespective of the training
mode. NHPT score decreased from 61 ± 14 s to
48 ± 20 s, a 24% change (F(1,6) =16.495, p =
0.007);
• Ataxia score decreased from T0 and T3,
irrespective of the training mode (F(1,6) =
6.1935, p = 0.04725). The decrease occurred
during the first four sessions (F(1,6) = 10.500,
p = 0.01768);
• Tremor: TADL score decreased in the first four
sessions, but only with error augmented training
(F(1,6) = 14.087, p = 0.00947);
• TADL secondary outcome that significantly
decreased only in error augmented training
(F(1,6) = 14.087, p = 0.00947).
• Adaptive robot training improves upper limb
function.
• No significant differences- neither short-term
(within session) nor long term (between
sessions) - between error-enhancing and
error-reducing training.
(Wang
et al.,
2010)
assistive n/a
Kinematic: Times
needed assistance,
position error
• Significant improvements were observed in both
AAN Session and INT Session (p < 0.001)
• 19/20 participants needed fewer times of robotic
assistance (no p-value provided)
• Tracking performances improved with error
augmented therapy (p = 0.0014)
• Participants became more capable of
executing the task when the visual error
augmentation training method had been
integrated with the assist-as-needed training
method/no statistically significant difference
in carryover effects was observed between
the two groups
(Abdolla
hi et al.,
2011)
error
enhancing
stroke
Clinical: Fugl-
Meyer, WMFT,
ASFR, Box and
b
locks
Kinematic: Rom
reach value/rom
error
• Six of nineteen subjects showed significant
improvement in ROM either immediately
following treatment (p = 0.04854) or at the
follow-up phase of error-augmented treatment
(p = 0.07056)
• Error augmentation elicited varied degrees of
performance improvement as measured by the
AMFM scores based on percentage change from
pre-treatment base line values to the follow-up
evaluation (95% confidence intervals)
• Fugl-Meyer score improved (95% confidence
intervals)
• On average, subjects performed better in 1-
week follow-up evaluations than they did at
the end of the two weeks of training. It may
be that the impaired nervous system does not
react to nor does it try to learn from smaller
errors, and the EA approach may promote
learning by simply intensifying the signal-to-
noise ratio for sensory systems, making
errors more noticeable
(Molier
et al.,
2011)
none
(haptic
feedback
for error)
stroke
Clinical: FMA-UL,
Motoricity index
MI/ARAT
Kinematic:
Circulant arm
movements,
isometric strength
• Four subjects improved on the FMA-UL by
between 1.0 and 9.5 points.
• MI, two subjects improved by 8 and 13 points
each
• Four subjects improved on the ARAT by between
0.5 and 5.0 points.
• Three subjects increased workspace by between
20.2% - 63.4%
(no statistical analysis was performed)
• Emphasis on errors at the moment they occur
may possibly stimulate motor learning when
patients perform movement tasks with
sufficiently high difficulty levels.
(Casellat
o et al.,
2012)
null
additive
force
constant
disturbing
force
primary
dystonia
Clinical: BFMDRS
• Disturbing force affected significantly the
movement outcomes in healthy but not in
dystonic subjects
• In the dystonic population the altered dynamic
exposure seems to induce a subsequent
improvement, i.e. a beneficial after-effect in
terms of optimal path control, compared with the
correspondent reference movement outcome
(p = 0.05)
• The short-time error-enhancing training in
dystonia could represent an effective
approach for motor performance
improvement, since the exposure to
controlled dynamic alterations induces a
refining of the existing but strongly
imprecise motor scheme and sensorimotor
patterns.
(Shirzad
et al.,
2012)
error
enhancing
n/a
Clinical: Self-
Assessment Mankin
questionnaire
Kinematic: Absolute
deviation of
trajectory, mean of
max deviation
• Increased satisfaction and attentiveness in error
augmented therapy and even more in visual and
haptic mode (no p-values provided)
• the means of each affect measure are significantly
different between almost all pairs of conditions
(p<0.05)
• High-gain visual plus haptic EA leads to a
significantly larger amount of learning a, in
comparison with both of the visual EA methods
(p<0.1)
• Significant differences in effect (specifically:
satisfaction, attentiveness and dominance)
between progressively more exaggerated
error amplification conditions, even when
presented in random order to subject
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