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a_ Significance


									a) Significance

       i) Identification and Significance of the Problem or Opportunity

a. Significance
         a.i.i    The Problem
    Motor impairments of the upper extremity (UE) commonly accompany many neurological or
musculoskeletal conditions and injuries, including stroke, cerebral palsy, Parkinson’s, injury to
the central nervous system (CNS), rheumatoid arthritis, and many others. The stroke population
alone numbers over 5 million persons in the U.S., most of whom have hemiplegia, involving at
least partial sensorimotor impairment on one side [1]. Throughout the world, there are 15 million
strokes annually[2]. These misfortunes limit fundamental activities of daily living (ADL) such
as eating, dressing, driving a car, writing or typing, telephone usage, equipment operation, and
self-care. Beyond functional loss, people may experience chronic pain and distortion in the
affected limb, as well as inexorable musculoskeletal deterioration through learned disuse [3].

    In general, the recovery outlook for UE motor impairments is poor; rehabilitation is costly,
constrained by time, practicality, travel distance, and is variably applied by individual providers.
Restoration of UE function is particularly problematic, due to its enormous complexity and the
higher priorities given to ambulation and learning ADL-related compensatory strategies that may
inhibit recovery. Moreover, formal rehabilitation programs are usually curtailed after the acute
phase. This neglect of UE is counterproductive, since disuse patterns and associated pathology
commence soon after immobilization of limbs. As a result, clients are unlikely to recover much
functionality. For example, a study of stroke patients entering rehabilitation with non-functional
arms revealed that 61% showed no improvement after 2 years [4]. Many patients who do
recover significant arm function experience episodic relapses, making it risky for them to carry

        a.i.ii The Opportunity
        This project proposes to develop a proprioceptive augmentation and measurement
interface (PAMI) that focuses on re-training reaching and grasping motions. PAMI answers the
need for a simple, relatively inexpensive neurorehabilitation tool that allows clients at home to
exploit their residual functions. By giving intuitive biofeedback to users in an engaging
environment, and providing progress cues, PAMI is designed to motivate the long, arduous and
tedious re-training process.

Neurorehabilitation from paralysis is a daunting uphill battle, but mounting evidence suggests
that the central nervous system (CNS) is plastic and, given the proper stimuli, can regain
significant motor control [5-18]. A primary stimulus of CNS reorganization is repetitive
functional task practice (RFTP), whereby clients repeatedly attempt specific tasks with
their limb, as prescribed by therapists, for many sessions. A variety of technological
aids have been developed to encourage and help clients through the RFTP process.
PAMI will have a unique niche among these several approaches (outlined below), due to its
simplicity and proven effectiveness in biofeedback.
Myriad strategies, devices, and protocols have been developed to promote neurorehabilitation of
the UE, each of which may in fact be efficacious for neurorehabilitation, but none are universally
applicable to the disabled population, or even to a particular subset of hemiplegics. The
emerging and competing therapy options rely on two basic assumptions: (1) the CNS is
sufficiently plastic to reorganize itself and regain sensorimotor control after injury, and (2)
sustained and repetitive movements of the affected limbs, i.e. RFTP, can promote re-learning by
the CNS, leading to sensorimotor recovery. There is a large body of evidence to support these
ideas [11, 19-39], and much interest in it on the part of the stroke community [40]. Beyond
these, there is insufficient theory upon which to select optimal therapies, since CNS damage and
its sequellae are enormously complex, involving sensorimotor control, muscles, joints,
metabolism, as well as perception and cognition. For this reason, it has been suggested that the
most promising therapy regimens should be open to empirically combining several protocols and
a variety of approaches [41]. PAMI offers a unique delivery mode of RFTP for recovery of
manipulation, and addresses the opportunity of improving the social and economic abilities of
persons with motor paralysis. RFTTP is effective and has been shown to, “have an impact on
activities of daily living” [18].

ii) Related Research or R&D

       a.2 Related Research
    a.2.1 Existing Tools
     a.2.1.1 General Overview
    While advanced neurorehabilitation strategies differ widely, and often overlap in methods,
they generally can be distinguished as those that work on: (1) strengthening individual muscles,
or (2) promoting movements. The first category includes standard physiotherapeutic exercises.
The second category includes tools such as constraint-induced movement therapy, biofeedback
devices, and movement assistive devices, often robotic, which can induce both passive and active
motions. The goal of all RFTP approaches is to coerce reorganization of the damaged motor
cortex by helping the client associate his volitions and possible subsequent movements with their
sensory consequences, including, proprioceptive, tactile, and visual modalities. RFTP requires
some active volition by the client, whether or not mechanical robots assist him/her.

    Many studies have supported the use of task-oriented training, sometimes aided by
constraining the sound arm, for re-training motor control of the hand [3, 42-46]. There is some
controversy, however, over the relative long-term efficacy of encouraging complex, task-
oriented motions versus simpler point-to-point movements [46] [12, 47]. For many disabled
clients, whose capabilities end with simple motions, paradigms involving complex integrated
movements do not intersect with the patient’s capacity to generate volitional motor acts, and may
lead to the learning of compensatory motions rather than optimal re-training.

Many patients require isolated joint and muscle conditioning in order to retrain motor control in
the UL [48, 49]. Hemiparetic patients, in particular, demonstrate spasticity and an inability to
isolate movement to one or a few joints. [This impaired joint and muscle individuation explains a
significant proportion of the variance in the reach path curvature and end point error; muscle
strength explains most of the variance in reaching velocity. Thus, a training device that can
analyze these features of the hemiparetic reaching motion (velocity, path, target achievement),
and simultaneously provide an avenue for neuromotor retraining, would act as both a diagnostic
and rehabilitative tool.]

    The optimal time for starting RFTP is not know. However, no evidence contraindicates
commencement within a few days of the event, at least at a minimal level. Moreover, much
evidence supports the value of specific exercises throughout the recovery period [5, 8, 10, 11, 13,
19, 24, 35, 42, 44, 50-52].

   a.2.1.2 Mechanical Devices
    A variety of electromechanical RFTP tools have been applied to UE rehabilitation, and range
from completely passive (meaning the limb is moved by motors) or completely active (meaning
the user must move the device), or a combination of both. Robotic arms that continuously move
the affected arm passively, with some back-drive-ability to allow active motions, have been
developed [53-61]. These devices and physical interventions may help shoulder and elbow
function, and have supported the concept of sensorimotor training induced plasticity of the CNS
[54, 57, 60-64]. The simplest of these are motorized movers of the affected arm that are
controlled by motions of the sound arm. Since the paralyzed arm is often in a highly contracted
and stiff state, powerful motors are required to move it. Robotic arms can provide standardized
repetitive exercises for joints and muscles, and they attempt to work both at improving
biomechanics and neurorehabilitation by giving users some control over their assisted movement
through their residual muscle activity. Some advanced devices combine robotic motions with
functional electrical stimulation (FES) or with electromyographic (EMG) control signals from
the affected arm [64]. Several companies market robotic arms, with one of the earliest being
patterned after the MIT-MANUS and marketed by Interactive Motion Technologies, Inc. The T-
Wrex (Hocoma, Inc., Rockland, MA) is a robotic/orthotic device that is meant to be worn by the
user, providing reaching assistance as well as passive motion training [65]. The WaveFlex and
H3 Hand provide continuous passive motion to the paralyzed hand (Orthorehab, Inc).

    Drawbacks of the robotic devices are their complexity and expense (upwards of $100K),
restricting their usage to primarily large clinics. Whereas robotic rehabilitation technology is still
in its infancy, it is our belief that further development will be required before patient needs are
met in an affordable, intuitive package which properly blends active and passive activities
biomimetically, and provides reliable sensorimotor training.

    For training the hand specifically, there are devices that move the fingers while providing
haptic feedback. There are electromechanical devices into which the fingers are inserted and
passively moved [66-68]. The Rutgers Master II is a glove serving as an exoskeleton that moves
the fingers [69]. Gloves and devices placed on the hand or arm for training finger extension or
arm motions are potentially useful, however it is difficult for many hemiplegic users to don

    As an inexpensive alternative to custom robotic arm movers, a force feedback joystick has
been tried with stroke subjects [70]. This approach allows the user to manipulate a wide variety
of games with the joystick, depending on his ability, and, by interfacing to the computer, it can
provide a comprehensive tele-rehabilitation program. The joysticks, however, are not sufficiently
responsive for all but the least affected arms, and provide only a limited range of motion, and no
isolation of specific joints.

    a.2.1.3 Biofeedback
       Evidence for Efficacy
     RFTP systems have made use of visual or auditory feedback to guide motor activity.
Feedback can be categorized as one of two varieties: (1) inherent feedback uses successful
completion of tasks, (2) augmented feedback, measures underlying control layers – e.g.
electromyographic (EMG), kinetic, or kinematic – to guide subjects during activity [71]. A
computer interface, or other feedback device, allows for a visual or auditory representation of
performance according to the inherent or augmented feedback design.

A large body of evidence supports the value of neuromuscular biofeedback for motor recovery
[11, 19-38, 72-74] [39, 75-83], and has generated much interest in the stroke community [84].
The benefits of repetitive training of the paretic hand have been demonstrated in numerous
studies[22, 26, 85] [21, 27, 28]. Many rehabilitative devices, including functional electrical
stimulation systems, require EMG monitoring of muscle activity for control and feedback [8, 11,
19, 20, 22, 23, 30, 34, 42, 64, 86, 87]. [Methods of biofeedback involve displaying EMG
recordings of specific muscles, while the subject tries to either amplify them for positive control
or to reduce them for relaxation and for anti-synergy. For example, a study compared groups
who either did or did not receive immediate feedback from EMG; the latter group performed
significantly more voluntary training than those without feedback [74]. In a randomized study
involving EMG feedback of arm muscles, it was shown that 20 days of practice improved wrist
range of motion and EMG potentials [22]. ]

    Another category of RFTP tools measure motions of relatively unconstrained limbs using
accelerometers (ACCs), goniometers, or optical means. ACC and goniometer technology has
been well established as an accurate and convenient method for registering limb motion in 3D in
healthy persons [88] [89] and athletes [90, 91], as well as those with stroke and other
neurological conditions [92-95]. For example, the stroke Upper-Limb Activity Monitor
(SULAM) applies an ACC and goniometer on each wrist and other joints and monitors daily
activities and motion parameters [96] [97] [98]. ACCs have also been used for estimation of
limb inertial forces [99]. An internet based wearable motion tracker system that is designed for
users with stroke to use at home has been primarily designed for telerehabilitation, and requires
several inertial sensors positioned near the wrist, and a belt-worn processor unit [100].

   The accuracy of ACCs alone as motion monitors has been established by comparing them
with differentiated signals from Vicon and other cameras, where a close match was found [101].
Other studies have correlated ACC and muscle activity during simple arm reaching movements
[102], as well as with overall activity levels in stroke subjects [92].

   Other modalities have been applied to RFTP, including video tracking with anatomical
markers [103] [104] [105] and a 3D optoelectronic system [106]. Sensorized gloves, such as the
Rutgers Master II and others have been developed to encourage specific finger exercises and
have demonstrated efficacy [107] [69]. A drawback of these technologies, such as wearable
goniometers, markers and gloves, is the difficulty of donning and inconvenience in wearing by
many persons with motor impairments.
Beyond ordinary biofeedback resembling computer games, virtual reality environments provide
more realistic biofeedback and encouragement and may improve the prospects of physical
exercise [108-122]. Motor imagery has been used as biofeedback, whereby users practice
watching a corrected image of their affected arm moving normally, done by projecting a mirror
image of their sound arm[123] [124].

Although the mechanisms of biofeedback efficacy are not fully known, and some literature
reviews have not found significant effects [27], there is no doubt that training must overcome
abnormal muscle patterns, synergies, contractures, as well as stiffness of joints, muscles and
tendons. Therapists usually begin by working with isolated muscles and movements, and
progress to more functionally oriented practice. Motivation is a key factor in neurorehabilitation,
and it has been shown that biofeedback promotes it. For example, a study compared groups who
either did or did not receive immediate feedback from EMG; the latter group performed
significantly more voluntary training than those without feedback [74]. Care-givers are likely to
appreciate the availability of highly interesting and motivating tools for their clients, that are
simple to apply and efficacious.

   Hand Retraining Tools

    Hand capabilities can be roughly divided into finger dexterity and grasping ability.
    Grip force control, for both precision and gross tasks, is a crucial aspect of dexterity that is
generally lost after injuries or conditions such as stroke [140], and is compromised by age [141].
One study tested the ability of stroke subjects to adjust their movement toward an object and the
force applied to lifting a load, and found these to be a useful measure of neurological deficit
[125]. Other studies have shown the direct relationship between grip force control in lift tasks
and functional measures of neurological impairment [142-144]. In fact, hand function measures,
such as grip strength, are the best predictors and main determinant of arm recovery post-stroke,
as measured by the Fugl-Meyer instrument [145]. Additionally, isometric grip strength and
active range-of-motion ( ROM) are much better predictors of outcome variability in reaching
tasks than standard clinical assessments of the UE in the acute phase of stroke [146]. Grip force
assessment is a fundamental diagnostic and therapeutic tool for many conditions, including
stroke [76-[125, 126], rheumatoid arthritis [127], COPD [128], therapeutic nerve block [129],
hand injuries [130], Parkinson’s [131], cerebral palsy [132], spinal cord injury [133-136], ADHD
[137], task-specific dystonia, Tourette's syndrome and cerebellar disease [138], myopathy [139],
and aging .

There is much evidence that the paralyzed arm can be improved through repetitive training of
isolated movements.. One of the first demonstrations of this was with 27 hemi-paretic patients
who improved hand performance after several days of training with repetitive hand and finger
flexions [147, 148]. Subjects who underwent 20 sessions of finger tracking exercises not only
improved their function, but experienced plastic reorganization within the motor cortex [148].
Subsequent studies have confirmed the value of repetitive finger tracking, however simple RFTP
exercises without tracking were also found to be efficacious [149]. Another study applied EMG
biofeedback in conjunction with Brunnstrom's exercises for the hand, and compared results
with a placebo group [150]; significant improvement was found in the biofeedback group. There
are, however, few commercial devices that specifically provide users with feedback to help them
regain fine motor control of the hand.

        Limitations of Current Methods
Most devices for biofeedback rely on the EMG, with the Brucker method being the most popular
[84]. While EMG biofeedback is clearly a major tool for neurorehabilitation, the technique has
drawbacks. EMG is generally not an in-home option due to its complexity, and patients can
only get formal treatment at one of a small number of rehabilitation clinics in the U.S. that
specialize in biofeedback. A readily accessible EMG feedback system is the “DJ Switch Kit”
(Don Johnston, Inc), that is an interface between user’s EMG and computer games that are
operated by on/off muscle signals. This device is popular and relatively inexpensive, but
provides only binary switching at best, and thus is not appropriate for continuous biofeedback of
muscular activity.

Not surprisingly, it is often difficult for persons with paralysis to reliably register residual muscle
activity from their affected limbs; likewise, it is sometimes challenging for their providers. EMG
has major deficiencies as an interface for the disabled population: it depends on precise and fixed
placements of sensors on the body, clean and dry conditions, and skin contact with electrode
pastes, or invasive wires [151]. Users move and sweat, causing degraded sensor performance
and irritating skin conditions. Moreover although the EMG approach can adequately recognize
binary volitions such as grasp/release, it is otherwise limited due to its fundamental and practical
limitations [152]. Feedback is generally restricted to the control of single muscles or joints,
which imposes a constraint on the applicability to rehabilitate fine motor control. Thus, the
EMG does not lend itself to easy interpretation, and cannot directly identify specific motions.

  Force Myography in Comparison to Electromyography
     A force myogram (FMG) registers the dynamic pressures at the skin surface generated by the
limb musculo-tendinous complex using sensors arrayed in a limb sleeve or bracelet [153]. In our
laboratory, FMG registered activity of the extrinsic muscles of the hand on the forearm and its
summed magnitude was compared with actual grip force, measured with a hand dynamometer.
An FMG sleeve with an array of circular force-sensitive resistors was implemented (Figure 2,
left). Details of processing have been previously reported and described [108, 154]. The resulting
signals represent dynamic images of muscular activity, whose patterns can be learned by an
adaptive processor, and associated with specific muscles.
        The subject was asked to grasp and
release the gripper 3 times rhythmically while
wearing the sensor sleeve, in order to compare                                                  FMG
output with grasp force, as shown in Figure 3.
        Next, the subject performed an
increasing series of force ramps to determine
correlation between FMG and grip force, as
shown below. Note that correlation is > 0.98.
These results (Figure 4) indicate that the FMG
sleeve can reasonably estimate the actual force
output of the hand.

                                                   Figure 3: Comparison of FMG output
                                                   with grasp force. Note the concordance
                                                  of the overall signals as well as the small
                                                            oscillations (arrows).
FMG sleeves are reliable measures of specific
motions and volitions, and are sufficiently accurate for biofeedback purposes [108]. For
example, Figure 5 shows records from both the affected and unaffected arm of a stroke subject.
The subject was asked to extend and relax his index finger rhythmically. Note that the rhythmic
pattern from the muscles controlling the sound finger is clear, while the forearm muscles of the
affected arm contracted strongly and relaxed slowly during the attempted motion. It is important
to emphasize that by properly positioning FMG sleeves on the arm it is possible to register and
differentiate distinct motions of the digits, wrist, elbow, and shoulder.

iii) Potential Commercial Applications/Anticipated Societal Benefits

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