Good luck trying to decipher this in explaining to your doctor how you want rehabilitation to occur.
A Control Framework of Lower Extremity Rehabilitation Exoskeleton based on Neuro-Muscular-Skeletal Model.pdf
Lei Shi*, Zhen Liua and Chao Zhang**
rehabilitation exoskeleton robot is presented. It is
based on the Neuro-Musculo-Skeletal biological
model. Its core composition moudle, the motion
intent parser part, mainly comprises of three distinct
parts.The first part is signal acquisition of surface
electromyography (sEMG) that is the summation
of motor unit action potential (MUAP) starting from
central nervous system (CNS). sEMG can be used
to decode action intent of operator to make the
patient actively participate in specific training. As
another composition part, a muscle dynamics model
that is comprised of activation and contraction
dynamic model is developed. It is mainly used to
calculate muscle force. The last part is the skeletal
dynamic model that is simplified as a linked segment
mechanics. Combined with muscle dynamic model,
the joint torque exerted by internal muscles can be
exported, which can be ued to do a exoskeleton
controller design. The developed control framework
can make exoskeleton offer assistance to operators
during rehabilitation by guiding motions on correct
training rehabilitation trajectories, or give force
support to be able to perform certain motions.
Though the presentation is orientated towards the
lower extremity exoskeleton, itis generic and can be
applied to almost any part of the human body.
1. Introduction
With the rapid arrival of the aging society and the increase
of physical movement disorder patients caused by
various disease, the demand for occupational therapists
has increased drastically. The traditionalrehabilitation
mainly relies on therapist’s one-by-one rehabilitation
therapy, obviously whichis gradually not in conformity
with the needs. In parallel to this situation, researchers
have been using robotic technologies to develop many
kinds of assistive and rehabilitative devices for people
with disabilities or to develop medical devices used by
caregivers. Our research team’s core work is just to use
robot technology to develop a intelligent rehabilitative
tranning system that can be used to do lower limb gait
rehabilitation for patients following a disease or a
neurological condition. For this purpose, we develop a
exoskeleton device that can be worn around human lower
limb to offer assistance to patients during rehabilitation
of the locomotor system. Its recovery principle is that
robot drives patients to simulate normal subjects walking
to complete the rehabilitation training mission under the
control of control system
To date, this kind of rehabilitation has been developed
for many rehabilitation purposes by other researchers
and many of the clinical trials also verify they are valid.
Among them, the lokomat exoskeleton is an example of
the early gait trainer (Jezernik, Colombo, Keller, Frueh, &
Morari, 2003, Riener, 2012). Evidence based data shows
that lokomat therapy can improve gait symmetry, walking
ability, increases muscle strength and so on compared
to conventional physical therapy in stroke patients
(Husemann, Müller, Krewer, Heller, & Koenig, 2007;
Westlake & Patten, 2009). Moreover, there are many other
exoskeletons besides LOKOMAT. They were generally
divided into two categories: treadmill gait trainer and overground gait trainer. Other treadmill exoskeletons besides
LOKOMAT have LOPES (van Asseldonk & van der
Kooij, 2012; Veneman et al., 2007), ALEX (Banala, Kim,
Agrawal, & Scholz, 2009), ANdROS (Unluhisarcikli,
Pietrusinski, Weinberg, Bonato, & Mavroidis, 2011)
and so on. The developed over-ground gait trainers
have Hybrid Assistive Limb (HAL) (Sankai, 2011) from
University of Tsukuba, EXPOS from Sogang University
(Kong & Jeon, 2006) and Vanderbilt exoskeleton (Farris,
Quintero, & Goldfarb, 2011). For these exoskeleton
devices, regardless of their different mechanical types,
some common considerations must be paid on the design
of their control system.
As a kind of wearable robot (Pons, 2008), the distinctive,
specific and singular aspect of exoskeleton is its kinematic
chain maps on to the human limb anatomy. Thus its
controller design must be imposed strict requirements as
regards safety, effectiveness and dependability. It must be
designed as person-oriented device and is under the control
of operators at all times. For control of the exoskeleton
rehabilitation robot, of course, a large number of control
system have been proposed in earlier studies using various
approaches, such as machine learning, decoders, pattern
recognition, and proportional control (Park & Khatib,
2006; Schultz & Kuiken, 2011). Except for proportional
control, these control methods have two inherent
drawbacks: (1) they only allow the subject to perform
predetermined movements and (2) they limit the user’s
ability to control the magnitude of torque production.
Alternatively, proportional myoelectric controllers use
the subject’s muscle activation to control the magnitude
of joint torque for the powered device, which may be
more beneficial in lower-limb control (Ferris & Lewis,
2009). But most of the previous work proposes complex
mechanisms or systems of sensors. Meanwhile, many
researchers also use the EMG directly to generate machine
control commands for robot (Lee & Lee, 2005). However,
most of the previous works decode only finite lower limb
postures from surface electromyography (sEMG) signals,
which can cause many problems regarding smoothness of
motion, especially in the cases where the robot performs
everyday life tasks. Therefore, effective controller entails
the necessity for continuous and smooth control. Besides,
studies have shown that active involvement for operators
in the production of a motor pattern results in greater
motor learning and retention than passive movement
(Kaelin-Lang, Sawaki, & Cohen, 2004; Lotze, Braun,
Birbaumer, Anders, & Cohen, 2003; Schouenborg, 2004).
So in our system design, in order to make the patient
actively participate in the task specific training, sEMG is
adopted to decode intent of operator.
In the work, we construct a hybrid control scheme that
combines the model-based control system and sensor based control system. For the design of sensor based control system, you can complete the design by referring
to the design methods of control system of traditional
robots. Therefore, the work mainly talk about the other
sub-control system of model-based control system. In the
system, Neuro-Musculo-Skeletal model is adopted. The
sub-control system is an intuitive interactive interface
between exoskeleton and operator. Compared to the
traditional control by way of an external device, for
example, a keypad or a wheel and so on that has to be
manipulated, intuitive interface can reduce operator’s
mental load, that is,the operator can focus on fulfilling
a task with the exoskeleton rather than focus on mere
control of the device (Fleischer, 2007).
This paper is organized as follows: Section II provides
a brief description of physiology of Neuro-MusculoSkeletal and human motion control; Section III focus on
the description about the control framework based on
neuro-musculo-skeletal model developed in the work;
Section IV gives a closing remarks and future work
Abstract
A control system framework of lower extremityrehabilitation exoskeleton robot is presented. It is
based on the Neuro-Musculo-Skeletal biological
model. Its core composition moudle, the motion
intent parser part, mainly comprises of three distinct
parts.The first part is signal acquisition of surface
electromyography (sEMG) that is the summation
of motor unit action potential (MUAP) starting from
central nervous system (CNS). sEMG can be used
to decode action intent of operator to make the
patient actively participate in specific training. As
another composition part, a muscle dynamics model
that is comprised of activation and contraction
dynamic model is developed. It is mainly used to
calculate muscle force. The last part is the skeletal
dynamic model that is simplified as a linked segment
mechanics. Combined with muscle dynamic model,
the joint torque exerted by internal muscles can be
exported, which can be ued to do a exoskeleton
controller design. The developed control framework
can make exoskeleton offer assistance to operators
during rehabilitation by guiding motions on correct
training rehabilitation trajectories, or give force
support to be able to perform certain motions.
Though the presentation is orientated towards the
lower extremity exoskeleton, itis generic and can be
applied to almost any part of the human body.
1. Introduction
With the rapid arrival of the aging society and the increase
of physical movement disorder patients caused by
various disease, the demand for occupational therapists
has increased drastically. The traditionalrehabilitation
mainly relies on therapist’s one-by-one rehabilitation
therapy, obviously whichis gradually not in conformity
with the needs. In parallel to this situation, researchers
have been using robotic technologies to develop many
kinds of assistive and rehabilitative devices for people
with disabilities or to develop medical devices used by
caregivers. Our research team’s core work is just to use
robot technology to develop a intelligent rehabilitative
tranning system that can be used to do lower limb gait
rehabilitation for patients following a disease or a
neurological condition. For this purpose, we develop a
exoskeleton device that can be worn around human lower
limb to offer assistance to patients during rehabilitation
of the locomotor system. Its recovery principle is that
robot drives patients to simulate normal subjects walking
to complete the rehabilitation training mission under the
control of control system
To date, this kind of rehabilitation has been developed
for many rehabilitation purposes by other researchers
and many of the clinical trials also verify they are valid.
Among them, the lokomat exoskeleton is an example of
the early gait trainer (Jezernik, Colombo, Keller, Frueh, &
Morari, 2003, Riener, 2012). Evidence based data shows
that lokomat therapy can improve gait symmetry, walking
ability, increases muscle strength and so on compared
to conventional physical therapy in stroke patients
(Husemann, Müller, Krewer, Heller, & Koenig, 2007;
Westlake & Patten, 2009). Moreover, there are many other
exoskeletons besides LOKOMAT. They were generally
divided into two categories: treadmill gait trainer and overground gait trainer. Other treadmill exoskeletons besides
LOKOMAT have LOPES (van Asseldonk & van der
Kooij, 2012; Veneman et al., 2007), ALEX (Banala, Kim,
Agrawal, & Scholz, 2009), ANdROS (Unluhisarcikli,
Pietrusinski, Weinberg, Bonato, & Mavroidis, 2011)
and so on. The developed over-ground gait trainers
have Hybrid Assistive Limb (HAL) (Sankai, 2011) from
University of Tsukuba, EXPOS from Sogang University
(Kong & Jeon, 2006) and Vanderbilt exoskeleton (Farris,
Quintero, & Goldfarb, 2011). For these exoskeleton
devices, regardless of their different mechanical types,
some common considerations must be paid on the design
of their control system.
As a kind of wearable robot (Pons, 2008), the distinctive,
specific and singular aspect of exoskeleton is its kinematic
chain maps on to the human limb anatomy. Thus its
controller design must be imposed strict requirements as
regards safety, effectiveness and dependability. It must be
designed as person-oriented device and is under the control
of operators at all times. For control of the exoskeleton
rehabilitation robot, of course, a large number of control
system have been proposed in earlier studies using various
approaches, such as machine learning, decoders, pattern
recognition, and proportional control (Park & Khatib,
2006; Schultz & Kuiken, 2011). Except for proportional
control, these control methods have two inherent
drawbacks: (1) they only allow the subject to perform
predetermined movements and (2) they limit the user’s
ability to control the magnitude of torque production.
Alternatively, proportional myoelectric controllers use
the subject’s muscle activation to control the magnitude
of joint torque for the powered device, which may be
more beneficial in lower-limb control (Ferris & Lewis,
2009). But most of the previous work proposes complex
mechanisms or systems of sensors. Meanwhile, many
researchers also use the EMG directly to generate machine
control commands for robot (Lee & Lee, 2005). However,
most of the previous works decode only finite lower limb
postures from surface electromyography (sEMG) signals,
which can cause many problems regarding smoothness of
motion, especially in the cases where the robot performs
everyday life tasks. Therefore, effective controller entails
the necessity for continuous and smooth control. Besides,
studies have shown that active involvement for operators
in the production of a motor pattern results in greater
motor learning and retention than passive movement
(Kaelin-Lang, Sawaki, & Cohen, 2004; Lotze, Braun,
Birbaumer, Anders, & Cohen, 2003; Schouenborg, 2004).
So in our system design, in order to make the patient
actively participate in the task specific training, sEMG is
adopted to decode intent of operator.
In the work, we construct a hybrid control scheme that
combines the model-based control system and sensor based control system. For the design of sensor based control system, you can complete the design by referring
to the design methods of control system of traditional
robots. Therefore, the work mainly talk about the other
sub-control system of model-based control system. In the
system, Neuro-Musculo-Skeletal model is adopted. The
sub-control system is an intuitive interactive interface
between exoskeleton and operator. Compared to the
traditional control by way of an external device, for
example, a keypad or a wheel and so on that has to be
manipulated, intuitive interface can reduce operator’s
mental load, that is,the operator can focus on fulfilling
a task with the exoskeleton rather than focus on mere
control of the device (Fleischer, 2007).
This paper is organized as follows: Section II provides
a brief description of physiology of Neuro-MusculoSkeletal and human motion control; Section III focus on
the description about the control framework based on
neuro-musculo-skeletal model developed in the work;
Section IV gives a closing remarks and future work
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