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Development of an FES-Based Ankle Orthosis

Permanent Link: http://ufdc.ufl.edu/UFE0041734/00001

Material Information

Title: Development of an FES-Based Ankle Orthosis
Physical Description: 1 online resource (59 p.)
Language: english
Creator: Jayaraman, Ganeshram
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: control, fes, gait, orthosis, pushoff, stroke, training, treadmill, walking
Mechanical and Aerospace Engineering -- Dissertations, Academic -- UF
Genre: Mechanical Engineering thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Individuals suffering from stroke and Spinal Cord Injuries (SCI) require rehabilitation to perform normal gait like motion with more independence. Recently developed robot assisted devices for rehabilitation includes a device powered with an actuator at the joints and a simple control program to enable the limbs of a person to track a predefined trajectory. Muscle training through Functional Electrical Stimulation (FES) is another rehabilitation tool which involves the application of voltage to the surface of the skin through electrodes, thereby producing a desired functional muscle contraction. The first commercially available FES device for walking known as the Parastep-I system involved open-loop stimulation wherein the stimulation current is adjusted by the patient using two pushbuttons attached to the left and right handles of a walking frame or crutches. One of the disadvantages of using FES assisted walking device is that it is not possible to stimulate the Hip flexors directly and requires voluntary effort which could be absent in paraplegic patients. The robotic orthosis Lokomat has four linear actuators to control the two hip joint motions and the two knee motions and four potentiometers to measure the joint angles. However the Lokomat does not allow for the control of the ankle-foot complex. A good ankle joint articulation and control is necessary for balancing while standing and walking. A hybrid orthosis device consisting of a combination of the Lokomat and FES is developed where the ankle-foot complex is controlled using FES. The advantage of using a hybrid orthosis device is that it greatly reduces the number of degrees of freedom that contribute to the walking motion, and hence, can negate the effect of muscle weakness from the remaining degrees of freedom. The closed-loop proportional integral control method is developed using FES which combines preset timing and tracking control. The toe event switch is used to trigger the control program and the goniometer sensor is used as an ankle joint angle feedback for tracking during walking. The controller was tested on a healthy individual and produced the required plantar flexion moment during ankle push-off to prevent individual?s from tripping on their toes.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Ganeshram Jayaraman.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Dixon, Warren E.
Local: Co-adviser: Gregory, Chris.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041734:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041734/00001

Material Information

Title: Development of an FES-Based Ankle Orthosis
Physical Description: 1 online resource (59 p.)
Language: english
Creator: Jayaraman, Ganeshram
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: control, fes, gait, orthosis, pushoff, stroke, training, treadmill, walking
Mechanical and Aerospace Engineering -- Dissertations, Academic -- UF
Genre: Mechanical Engineering thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Individuals suffering from stroke and Spinal Cord Injuries (SCI) require rehabilitation to perform normal gait like motion with more independence. Recently developed robot assisted devices for rehabilitation includes a device powered with an actuator at the joints and a simple control program to enable the limbs of a person to track a predefined trajectory. Muscle training through Functional Electrical Stimulation (FES) is another rehabilitation tool which involves the application of voltage to the surface of the skin through electrodes, thereby producing a desired functional muscle contraction. The first commercially available FES device for walking known as the Parastep-I system involved open-loop stimulation wherein the stimulation current is adjusted by the patient using two pushbuttons attached to the left and right handles of a walking frame or crutches. One of the disadvantages of using FES assisted walking device is that it is not possible to stimulate the Hip flexors directly and requires voluntary effort which could be absent in paraplegic patients. The robotic orthosis Lokomat has four linear actuators to control the two hip joint motions and the two knee motions and four potentiometers to measure the joint angles. However the Lokomat does not allow for the control of the ankle-foot complex. A good ankle joint articulation and control is necessary for balancing while standing and walking. A hybrid orthosis device consisting of a combination of the Lokomat and FES is developed where the ankle-foot complex is controlled using FES. The advantage of using a hybrid orthosis device is that it greatly reduces the number of degrees of freedom that contribute to the walking motion, and hence, can negate the effect of muscle weakness from the remaining degrees of freedom. The closed-loop proportional integral control method is developed using FES which combines preset timing and tracking control. The toe event switch is used to trigger the control program and the goniometer sensor is used as an ankle joint angle feedback for tracking during walking. The controller was tested on a healthy individual and produced the required plantar flexion moment during ankle push-off to prevent individual?s from tripping on their toes.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Ganeshram Jayaraman.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Dixon, Warren E.
Local: Co-adviser: Gregory, Chris.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0041734:00001


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1 DEVELOPMENT OF AN FES BASED ANKLE ORTHOSIS By GANESHRAM JAYARAMAN A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNI VERSITY OF FLORIDA 2010

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2 2010 Ganeshram Jayaraman

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3 To my parents, M. Jayaraman and Geetha Jayaraman; my sister Ramya; and my friends and family members, who constantly provided me with motivation, encouragement and joy

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4 ACKNOWLEDGMENTS I wo uld like to express my gratitude to my supervisory committee chair and mentor Dr. Warren E. Dixon for giving me the opportunity to work on this project and for the advice and encouragement that he had provided me with during the course of my study at Unive rsity of Florida. I would also like to extend my gratitude to my coadvisor Dr. Chris M. Greg ory for helping me with the setting up and conducting the experiments at the VA hospital and for his valuable inputs at various stages of the project. I also thank my committee member Dr. Scott A Banks for lending his knowledge and support. I thank my colleagues for helping me with the thesis research. I especially thank Dr. Qiang Wang and Nitin Sharma for sharing their knowledge and for helping me out when I was c onducting my experiments. I also thank members of the Human Motor Performance Laboratory at the VA hospital for their help in running the Lokomat and the treadmill. Finally I would like to thank my parents for their understanding, encouragement and patienc e.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF FIGURES .......................................................................................................... 6 ABSTRACT ..................................................................................................................... 8 CHAPTER 1 INTRODUCTION .................................................................................................... 10 2 EXPERIMENTAL SETUP ....................................................................................... 15 2.1 Components Description ................................................................................ 16 2.1.1 Lokomat ............................................................................................... 16 2.1.2 Goniometer .......................................................................................... 16 2.1.3 Signal Conditioning .............................................................................. 16 2.1.4 Event Switch ........................................................................................ 17 2.1.5 Computing System ............................................................................... 17 2.1.6 Stimulatio n Circuit ................................................................................ 17 2.2 Robotic Orthosis Lokomat .............................................................................. 18 2.2.1 Lokomat Setup ..................................................................................... 18 2.2.2 Linear Drives ........................................................................................ 20 2.2.3 Lokomat Control Setup ......................................................................... 21 2.3 Goniometer Sensor Integration ...................................................................... 21 2.3.1 Angle Measurement ............................................................................. 24 2.3.2 Moving Average Filter Implementation ................................................. 2 4 2.4 Data Acquisition and Computing System ....................................................... 27 2.5 Event Switch .................................................................................................. 33 2.6 Stimulation Circuit .......................................................................................... 36 3 EXPERIMENTAL METHODS ................................................................................. 37 3.1 Desired Trajectory Setup ................................................................................ 37 3.2 Desired Trajectory Offset ............................................................................... 38 3.3 Experimental Procedure ................................................................................. 41 4 RESULTS AND DISCUSSIONS ............................................................................. 43 5 CONCLUSION ........................................................................................................ 53 REFERENCES .............................................................................................................. 54 BIOGRAPHICAL SKETCH ............................................................................................ 59

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6 LIST OF FIGURES Figure page 2 1 Various components of the experimental setup. ................................................. 15 2 2 Schematic diagram of a Lokomat setup [6]. ....................................................... 19 2 3 A person walking with the help of a Lokomat. .................................................... 19 2 4 Various adjustments made in the Lokomat are indicated by double sided arrows [6]. ........................................................................................................... 20 2 5 Control setup for the Lokomat [6]. ...................................................................... 22 2 6 Biometrics Ltd., goniometer sensors. ................................................................. 23 2 7 Goniometer sensor placement across the ankle joint. ........................................ 23 2 8 Block diagram for the implement ation of Moving Average Filter algorithm. ........ 26 2 9 Raw goniometer reading from the ADC port with and without Moving Average F ilter at steady state. .......................................................................................... 28 2 10 Calibrated angle reading from the goniometer at steady state plotted over a period of 0.25 sec. The final measuring angle is a combination of the effect of the first and additional 71 point MA Filter on angle measurement for 0.142 sec and after 0.142 sec respectively. ................................................................. 29 2 11 Calibrated angle reading from the goniometer at steady state plotted over a period of 15 seconds. The final measuring angle is a combination of the effect of the first and additional 71 point Moving Average Filter on angle measurement for 0.142 sec and after 0.142 sec respec tively. ............................ 30 2 12 Raw goniometer reading from the ADC port with and without the Moving Average Filter while tracking. .............................................................................. 31 2 13 Calibrated angle reading from the goniometer while tracking showing the effect of Moving Average Filter. .......................................................................... 32 2 14 Motion lab system event switch. ......................................................................... 33 2 15 Digital input circuit. ............................................................................................. 34 2 16 Digital input reading of toe switch. ...................................................................... 35 2 17 Toe switch state .................................................................................................. 35

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7 2 18 Stimulation circuit board ..................................................................................... 36 3 1 Toe switch counter for recording desired trajectory ............................................ 38 3 2 Desired trajectory recorded of the subject walking on treadmill with the support of the Lokomat. ...................................................................................... 39 3 3 Desired trajectory recorded of the subject walking on treadmill wi thout the support of the Lokomat. ...................................................................................... 39 3 4 Coordinate system for measuring the ankle and knee angle .............................. 40 3 5 Actual measurement of t he ankle and knee angle .............................................. 40 3 6 Experimental setup with the Lokomat and computing system ............................ 42 4 1 Actual and desired trajectory plot of a person walking on the treadmill without the support of the Lokomat. ................................................................................ 45 4 2 Error plot of a person walking on the treadmill without the support of the Lokomat. ............................................................................................................. 46 4 3 Switch state plot of a person walking on the treadmill without the support of the Lokomat. ....................................................................................................... 47 4 4 Stimulation voltage plot of a person walking on t he treadmill without the support of the Lokomat. ...................................................................................... 48 4 5 Actual and desired trajectory plot of a person walking on the treadmill with the support of the Lokomat. ................................................................................ 49 4 6 Error plot of a person walking on the treadmill with the support of the Lokomat. ............................................................................................................. 50 4 7 Switch state of a person walking on the treadmill with the support of the Lokomat. ............................................................................................................. 51 4 8 Stimulation voltage plot of a person walking on the treadmill with the support of the Lokomat. ................................................................................................... 52

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8 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science DEVELOPMENT OF AN FESBASED ANKLE ORTHOSIS By Ganeshram Jayaraman M ay 2010 Chair: Warren E. Dixon Co C hair: C hris M. Gregory Major: Mechanical Engineering I ndividuals suffering from stroke and Spinal Cord Injuries (SCI) require rehabilitation to perform normal gait like motion with more independence. Recently developed robot assisted devices for rehabilitation i ncludes a device powered with an actuator at the joints and a simple control program to enable the limbs of a person to track a predefined trajectory Muscle training through Functional Electrical Stimulation (FES) is another rehabilitation tool which involves the application of voltage to the surface of the skin through electrodes thereby producing a desired functional muscle contraction. The first commercially available FES device for walking known as the ParastepI system involved openloop stimulation wherein the stimulation current is adjusted by the patient using two pushbuttons attached to the left and right handles of a walking frame or crutches. One of the disadvantages of using FES assisted walking device is that it is not possible to stimulate th e Hip flexors directly and requires voluntary effort which could be absent in paraplegic patients. The r obotic orthosis Lokomat has four linear actuators to control the two hip joint motion s and the two knee motions and four potentiometers to measure the joint angles. However the Lokomat does not allow for the control of the anklef oot complex. A good ankle joint articulation and control is

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9 necessary for balancing while standing and walking. A hybrid orthosis device consisting of a combination of the Lokom at and FES is developed where the anklefoot complex is controlled using F ES. The advantage of using a hybrid orthosis device is that it greatly reduces the number of degrees of freedom that contribute to the walking motion, and hence, can negate the effec t of muscle weakness from the remaining degrees of freedom. The closedloop proportional integral control method is developed using FES which combines preset timing and tracking control. The toe event switch is used to trigger the control program and the g oniometer sensor is used as an ankle joint angle feedback for tracking during walking. The controller was tested on a healthy individual and produced the required plantar flexi on moment during ankle pushoff to prevent individuals from tripping on their t oes.

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10 CHAPTER 1 INTRODUCTION More than 700,000 people suffer a stroke each year in the United States, and approximately twothirds of these individuals survive and require rehabilitation [ 1 ]. Stroke related costs in 2005 were nearly $57 billion in the Uni ted States. The Christopher Reeve F oundation estimates that up to 1,275,000 Americans may be living in the United States with Spinal Cord Injuries (SCI) in 2008, and data from the National Spinal Cord Injury Statistical Center [ 2 ] indicate projections of an annual incidence of 40 cases per million of population [ 3 ]. For 61% of the SCI population (under 30 years old) the lifetime costs of SCI range from $624,441 for incomplete motor function at any level to $2,801,642 for high quadriplegia [ 2 ]. The objectiv e of rehabilitation is to help the survivors to perform normal gait like motion with more independence. Such objectives can be achieved through r obotic assistanc e during rehabilitation. Typical robotic assistance would include a device powered with an actuator at the joints and a simple control program to enable the limbs of a person to track a predefined trajectory. This assistance can provide a more quantifiable and repetitiv e diagnostic and training tool and could be real ized through advancements in m eas urement techniques to accurately measure the joint angles in a local coordinate system and new robotic system s able to perform rehabilitation effic iently and in a safe manner. Early research in rehabilitation has been carried out using intense step training on a treadmill using a body weight support and manually assisted movements f or repetitive tasks. The persons weight is partially unloaded by a body weight suspension system and the person is placed on a treadmill with physical therapists helping to mov e the

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11 limbs properly. Rec ently r obot assisted devices have been developed to augment the manual training methods. Robot devices can i mprove the quality of training, and assist in performing repetitive tasks with less supervision. Robotic devices such as th e Lokomat [ 4 8 ], the similar AutoAmbulator [ 9 ], the Mechanized Gait Trainer [ 10 11], the direct drive robot ARTHuR [ 1214 ], and a robotic device for rodent gait training in [ 15] are designed to be in constant contact with an individual and guide the indivi duals legs through a preprogrammed gait. Specifically, technologies such as [ 16 35] have been developed as powered orthosis or exoskeletons for individuals with more walking capabilities. Muscle training through Functional Electrical Stimulation (FES) is another rehabilitation tool applied to individuals suffering from stroke and spinal injury Typically F ES methods involve the application of voltage to the surface of the skin through electrodes thereby producing a desired functional muscle contraction. In addition to moving a persons limb with robotic assistance or therapist, F ES method s can enable muscle training. Although F ES methods can serve as an adjunct for gait training the stimulation duration is reduced due to muscle fatigue. Early work by Kr alj et al. [ 36 37] in FES involved openloop stimulation wherein the stimulation current is adjusted by the patient using two pushbuttons attached to the left and right handles of a walking frame or crutches. This technique was later utilized in developing the first commercially available FES device for walking known as the ParastepI system [ 38 ]. More advanced systems have been developed in [ 39] and [ 40 ] focusing on automatic FES control for assisted walking. The energy requirement by FES assisted walking has been studied in [ 41]. In [42 ] it was reported that one of the disadvantages of using FES assisted

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12 walking device is that it is not possible to stimulate the Hip flexors directly and requires voluntary effort which could be absent in paraplegic patients. A stimulation control method had been developed in [ 43 ] to stimulate the triceps surae for achieving better pushoffs in stroke subjects. The open loop control method in [ 43] is based on preset timing and uses a uniaxial gyroscope to measure angular v elocity which is integrated to determine the shank angle that triggers the controller during swing phase. A PD and an optimal closed loop control of an ankle joint using FES was developed in [ 44 ] and [ 45] for balancing the leg during arm free standing. How ever, closedloop control of an ankle joint for tracking during walking requires gait phase detection and a dynamic ankle joint angle sensor feedback. The closedloop proportional integral control method developed in this thesis using FES combines preset t iming and tracking control. The toe event switch is used to trigger the control program and the goniometer sensor is used as an ankle joint angle feedback for tracking during walking. The robotic orthosis Lokomat provides an initial testbed to investigate FES augmentation to treadmill training. The r obotic orthosis Lokomat has four linear actuators to control the two hip joint motion s and the two knee motions and four potentiometers to measure the joint angles. The actuators are controlled by a PD joint mot ion control program. However the Lokomat does not allow for the control of the anklef oot complex. As stated in [ 46 ], good ankle joint articulation and control is necessary for balancing while standing and walking. Also most of the commercially available a nkle foot orthosis may have rigid ankle stiffness which may not change with respect to change in a terrain or walking speed [ 47 ]. In this thesis, a hybrid orthosis device consisting of a combination of the Lokomat and FES is developed where the anklefoot complex is controlled using F ES.

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13 In [42 ] it has been reported that the advantage of using a hybrid orthosis device greatly reduces the number of degrees of freedom that contribute to the walking motion, and hence, can negate the effect of muscle weakness f rom the remaining degrees of freedom. Successful implementation of the F ES ankle control requires developments in sensor i ntegration, control system design and stimulation parameter variations. Sensors play a crucial role in developing the closed loop cont rol system. They are essential to measure the ankle joint angle which is then compared with a reference trajectory to generate an error signal. Then the controller acts on this error signal to produce the required control torque to satisfy the control objective. Hence, successful sensor integration is a must for any closedloop control system design. The proper choice of a sensor depends on the type of application it is intended for. The FES ankle control method has been carried out using an encoder [ 45] to measure the ankle joint angle. However such measurements are made in a global coordinate system Hence in this project a position measuring goniometer sensor, Biometrics Ltd, is utilized to successfully measure the joint ankle in the local coordinate syst em. This sensor is attached directly to a persons leg to measure the angle. The lightweight and good dynamic response of the g oniometer makes it suitable for tracking the ankle position while walking or standing with toeup. However most of the sensors i n general are affected by noise and possible delay s. Both problems can be controlled to certain ex tent through careful design of f ilters to remove the noise. While designing the filters care should be taken that it does not introduce t oo much delay in the system To reduce the noise in the goniometer readings used in this thesis, a double pass 71 point Moving Average Filter is used. The

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14 position measuring g oniometer without filtering was not suitable for dynamic measurement. T he details of the developed mov ing average filter are discussed in Chapter 2 FES methods consist of applying a stimulation voltage across a muscle group through stimulation electrodes to generate a desired muscle force. F ES can be implemented using invasive stimulation electrodes like needle electrodes wherein the electrodes are inserted into individual muscle. However efforts in this thesis used surface electrodes where the stimulation voltage is applied to the surface of the skin. The force produced in the muscle depends on the firing rate and the recruitment of the motor units. Current and past research has focused on the effect of stimulation parameter modulation on the force generated by the muscle. The muscle force can be controlled by modulating the volt age amplitude, pulse widt h and f requency The pulse width modulation (PWM) and pulse amplitude modulation (PAM) controls the motor recruitment whereas the pulse frequency modulation controls the firing rate of the motor units [ 4850 ] O ur Central Nervous system (CNS) uses a combin ation of motor recruitment/firing rate to control the different muscles [ 51 ] The objective of this thesis is to develop and test a hybrid orthosis device to enable a person to generate a sufficient plantar flexion moment for ankle pushoff to prevent indi viduals from tripping on their toes A toe switch is used to detect the mid stance of the gait and also to trigger the stimulation voltage. T he g oniometer sensor feedback is used to track a desired trajectory once the toe switch is on. These concepts along with hardware description, experimental methods and computing system have been discussed in the following chapters .

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15 CHAPTER 2 EXPERIMENTAL SETUP The F ES an kle control was implemented using a PI control algorithm with pulse amplitude modulation (PAM) The controller was tested on a healthy subject at the Human Motor Performance Laboratory (HMPL) housed in the Brain Rehabilitation Research Center (BRRC) located at the Veterans Affairs (VA) Hospital in Gainesville. The various components used in the experiment are illustrated in Figure 2 1 Figure 21. Various components of the experimental setup. Ankle Angle Goniometer Hip Angle Knee Angle Lokomat Toe Switch A/D & D/A Board Host PC Stimulato r Electrodes

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16 2.1 Components Description 2.1.1 Lokomat The Lokomat is a Swiss made robotic orthosis instrument primarily used for rehabilitation. The Lokomat provides treadmill step training for pati ents suffering from stroke and spinal c ord i njury. The Lokomat is equipped with four linear drives to move the hip and the knee joints. The joint angles are measured using four potentiometers and the dri ves are controlled by a PD position control program while the patient is partially suspended by means of a harness onto the treadmill platform. The ankle joint is not controlled by the Lokomat, hence it is held by a passive foot lifter that assists only in dorsiflexion during stance phase. For this experiment the foot lifter is removed and the plantar flexor moment of the ankle joint is controlled by FES of the calf muscle. 2.1.2 Goniometer For the experiment s in this thesis a voltage controller is designed to stimulate the ankle joint to track a desired trajectory. Any closed loop control requires sensor feedback to compare with the reference trajectories to generate an error signal. An a ngular position measuring g oniometer sensor developed by Biometrics Ltd, is used to measure knee flexion and the angular position of the ankle. These sensors are attached directly across the joint using double sided adhesive tape to measure joint angular movement. S ignals from the goniometer require additional instrumentati on to process the measurements 2.1.3 Signal Conditioning The goniometer is connected to an angle display unit ( ADU) made by Biometrics Ltd, to further process the signals. The ADU unit amplifies the signals from the goniometer with a 9v DC battery. The m easuring range falls between 04.5 V which has

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17 to be calibrated to interpret the actual ankle joint angle. A ServoToGo I/O card is used to process A/D and D/A conversions. The board can perform A/D and D/A operations up to 8 channels and 32 bits combination of Digital I/O at a sampling frequency of 1000 Hz. The goniometer signals were affected by noise and hence a double pass 71 point Moving Average (MA) filter has been utilized to remove the noise. The MA filter is implemented by software through a code wr itten in C++. 2.1.4 Event Switch The event switch used i n this experiment is a product of Motion Lab System. The event switch connected to the toe is used as an OnOff switch by connecting to the digital input of a ServoToGo I/O card. When the toe is on t he ground, the switch is on and triggers the control algorithm and terminates once the switch is off. 2.1.5 Computing System FES control methods and the sensor data acquisition and filtering are implemented on a Pentium IV PC running on QNX, a deterministi c operating system. The control algorithm is implemented using Qmotor 3.0, a framework for implementing control program s which is developed for the QNX operating system and uses Photon for graphical user interface. 2.1.6 Stimulation Circuit Control algorit hms are encoded in C++ and an executable produces a voltage to stimulate the calf muscle to move the ankle joint along a desired trajectory. The stimulation voltage is generated using a custom made stimulation circuit board based on the generated error sig nal represented as the difference between the desired position and the actual ankle position measured by the goniometer. The voltage controller is implemented through a Pulse Amplitude Modulation (PAM) with a fixed

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18 pulse width of 100 sec and a fixed freq uency of 100 Hz. A voltage to frequency circuit was implemented to control the frequency of the pulse. 2.2 Robotic Orthosis Lokomat 2.2.1 Lokomat Setup The Lokomat is a robotic orthosis device which can be fitted to the patient and strapped around the wais t and the legs. The patient is partially suspended by means of a harness onto the treadmill platform. A schematic diagram of the Lokomat and a picture of person mount ed on the Lokomat are shown in Fi g ure 2 2 and 2 3 The passive foot lifter (Figure 2 2 ) as sists in dorsiflexion of the ankle joint during the stance phase. This leads to rigid ankle joint stiffness which may not be desirable since ankle joint control is required for balancing in walking and standing. In this experiment the ankle joint is contro lled using a FES based proportional integral voltage controller to stimulate the calf muscle while the knee and hip joints are controlled by the Lokomat. The ankle joint angl e feedback is measured using a g oniometer which is attached across the joint. The upper body of a person has to be balanced in the vertical direction during treadmill training and muscle stimulation to prevent slipping. A parallelogram is used to attach the lokomat to the railings of the treadmill which enables the Lokomat to move only in the vertical direction. This configuration provides an initial testbed to investigate FES augmentation to treadmill training, while constraining movements to the sagittal plane. Any orthosis has to be flexible in its design since it has to be fitted to various people with different anatomical features. Figure 2 4 illustrates the various adjustments

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19 Figure 22 Schematic diagram of a Lokomat setup [ 6 ]. Figure 23 A person walking with the help of a Lokomat.

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20 that can be made with the Lokomat such as width of the hip and position and size of the leg braces. A small cushion pad is placed between the braces and the skin to prevent skin soreness. Figure 24 Various adjustments made in the Lokomat are indicated by double sided arrows [ 6 ]. 2.2.2 L inear Drives The Lokomat consists of four DC drives at the hip and the knee joints which are driven by a precision ball screw (KGT 1234, Steinmeyer GmbH & Co., Germany) where the nut on the screw is driven by a DC motor (MaxonTM RE40, Interelectric AG, Sw itzerland) using a toothed belt with a Mechanical p ower of 150 W as indicated in [ 6 ]. The drives are designed to move the hip and knee joints along a gait pattern.

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21 2.2.3 Lokomat Control Setup The control se tup of the Lokomat is shown in F igure 2 5 which co nsists of a user interface, target PC and a controller. The therapist user interface is used to record all the adjustments made in the Lokomat for a particular person which could be used to setup the Lokomat quickly for the next training period. The user i nterface also has an interface to the Lokomat using LabView s oftware and can be controlled by the therapist. The four joints comprising the hip and knee joints are controlled separately through independent closedloop proportional derivative position contr ol to generate various gait trajectories. The joint angles are measured using potentiometers and processed using an analog to digital converter for real time use in the current controller. The position controller is implemented using a real time system (Re alLink TM) which runs on the target PC. A serial bus (RS 232) is used to communicate between the host and the target PC. The speed of the Lokomat can be changed at the user interface which is transferred to the target PC to adjust the gait pattern and to c hange the speed of the treadmill. 2.3 Goniometer Sensor Integration Sensor feedback plays an important role in developing the closedloop control for the FES In general the sensors are used to measure the system response which is then compared with a ref erence input to generate an error signal. Then most control methods use this feedback to generate a control input based on the error signal with the objective of reducing the error to zero. In the experiment s in this thesis, the ankle joint angle is used as feedback to develop a proportional integral controller to stimulate the calf muscle. F ES based ankle joint control has been carried out using encoder [ 45 ] However measurements from such sensors are made in the global coordinate system.

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22 Figure 25 C ontrol setup for the Lokomat [ 6 ].

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23 In the developed experiment a g oniometer angular position measuring sensor is used to measure joint angles in th e local coordinate system. The g oniometer (Figure 2 6 ) is attached directly across the ankle joint angle (Fi gure 2 7 ) using a double sided adhesive tape. Figure 26 Biometrics Ltd., goniometer sensors. Figure 27 Goniometer sensor placement across the ankle joint.

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24 2.3.1 Angle Measurement The two end blocks of the g oniometer are connected by a protect ive spring which houses a thin composite wire inside. The composite wire has a series of strain gauges attached on its circumference. Hence, when the angl e between the two ends change, the change in strain measured along the length of the wire is equated t o the angle. The output from the strain gauge is very low and instrumentation is needed to obtain a reasonable output. The goniometer is connected to an angle display unit which is used t o amplify the signals from the g oniometer. The primary use of the angle display unit is to display the angle measured by goniometer using a LCD. However the angle display unit is powered by a 9V DC alkaline battery which is used to amplify the goniometer signal by a gain factor of 90. The measurement range falls between 0.5 V to 4.5 V. The angle display unit is then connected to an A/D board to further process the signals. The sensor calibration is performed dynamically by software during the ex ecution of the control program. 2.3.2 Moving Average Filter Implementation The sensor feedback is affected by noise in the measurement. The noise can be reduced to a greater extent through careful design of the f ilters. While designing filters care should be taken that they do not introduce delay in the signal measur ement. The Movin g Average F ilter is a simple filter to remove random noise and to maintain sharp step response for signals i n the time domain. However the Moving Average F ilter is not ideal for signals in the frequency domain as it cannot effectively separate one frequenc y band from the other. The Gaussian, Blackman and Multiple Pass Filters are relatives of the Moving Average F ilter which improves the frequency response to some extent with increas ed computation time. In the developed experiment al testbed, a double pass 71

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2 5 point Moving Average Filter is used to remove the random noise as well as to improve the frequency response. The Moving Average F ilter operates by averaging the number of points in the input signal to produce the output signal. The moving average filter is defined as: 1 0] [ 1 ] [M jj i x M i y ( 2 1) w here x [ ] is the input signal, y [ ] is the output signal and M is the number of points in the average. The moving average filter is a convolution of the input signal with a rectangular pulse of area one. Th e frequency response of the Moving Average F ilter is the Fourier t ransform of a rectangular pulse as ) sin( ) sin( ] [ f M fM f H ( 2 2) where f and H[f] denote the frequency and frequency response respectively. In the subsequently described experiment s, the M oving Average F ilter is implemented in a recursive fashion as the algorithm reduced the computation time considerably. I nstead of taking the sum of all 71 points every time, our testing indicated that it is best to setup a accumulator to store the sum for the first point in the filter and then for the next point in the filter, the new sample is added and the first sample is subtract ed to get the new sum. By this method the computation time reduces since there are only two computations per point. The flow di agram of the moving average algorithm used in the subsequently described experiment is shown in Figure 2 8 The raw goniometer reading whose sampling frequency is 1000 Hz is amplified by the 9V DC supply and then passed through a 71 point Moving Average F i lter. While performing the filter operation the first 71 samples

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26 Figure 28 Block diagram for the implement ation of Moving Average Filter algorithm. Goniometer Low Output Reading Angle Display Unit 9V DC supply Gain K = 90 Sampling Frequency 1000 Hz Calibrated angle wit h one pass 71 point Moving Average Filter 71 point Moving Average Filter Final Calibrated Angle with two pass 71 point Moving Average Filter Joint Angle Sensor Feedback First 71 Samples 10 point Moving Average Filter 71 point Moving Average Filter

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27 were too noisy and hence they were passed to a 10 point Moving Average F ilter. However even after the application of the filter the calibrated angle had considerable noise. This is due to the fact that the signal from the angle display unit had a high sensitivity as calibration required multiplication by a gain of 90. Hen ce even a very small change in voltage like a +0.01 V lead to an angle deviation of 9 0 when calibrated. The uncalibrated goniometer voltage was varying in the range of 210 V and so applying a higher number of points averaging did not help the cause. Hence the calibrated angle is passed through another 71 point moving average filter. The filtered angle had a deviation of 1 0 which will be used as a joint angle sensor feedback. The effect of filtering on t he uncalibrated and calibrated g oniometer reading (see F i gure 2 9 2 13) has been tested under steady state and transient conditions. The Moving Average F ilters are not the only filters to remove the noise in sensor feedback. However, the Moving Aver age Filter performed adequately as a smoothing filter in the time domain. 2.4 Data Acquisition and Computing System The goniometer signals from the angle display unit are processed with an A/D converter to be used as a sensor feedback in the closedloop control system. The A/D and D/A processing are carried out on a ServoToGo I/O card at a sampling rate of 1000 Hz. The implementation of a PI voltage controller is carried out on a Pentium IV PC running on QNX The control algorithm is implemented by QMotor 3. 0, a framework for implementing control program which is developed for QNX operating system and uses Photon for graphical user interface. The QMotor allows the implementation of advanced control algorithm as C++ programs which improve execution speed. The components of QMotor include control program library, Hardware servers and

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28 Figure 29 Raw goniometer reading from the ADC port with and without Moving Average F ilter at steady state.

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29 Figure 210. Calibrated angle reading from the goniometer at steady state plotted over a period of 0.25 sec. The final measuring angle is a combination of the effect of the first and additional 71 point MA Filter on angle measurement for 0.142 sec and after 0.142 sec respectively.

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30 Figure 211. Calibrated angl e reading from the goniometer at steady state plotted over a period of 15 seconds. The final measuring angle is a combination of the effect of the first and additional 71 point Moving Average Filter on angle measurement for 0.142 sec and after 0.142 sec respectively.

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31 Figure 212. Raw goniometer reading from the ADC port with and without the Moving Average Filter while tracking.

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32 Figure 213. Calibrated angle reading from the goniometer while tracking showing the effect of Moving Average Filter.

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33 clients and a Graphical User Interface (GUI). The control program library contains control program class which is used to implement the control algorithms. The functions included in the control programs class allow control initiations, control calculation a nd termination of control. The hardware server is used to connect and access the ServoToGo I/O board. The clients are used to facilitate communication between the control programs and the ServoToGo I/O card. The graphical user interface (GUI) provides a us er interface to the control programs. It consists of a main window, log v ariable window, control window and plot window. The main window is used to load the control program and to set the duration of the control execution. The log variable window is used to log certain variables such as error signal and control signals. The control window contains variable that can be used to tune the controllers like control gains and control frequency. The plot window is used to plot the log variables in real time and th ese plots can be exported with Matlab capabilities for further data analysis. 2.5 Event Switch The event switch sensor used in this project is a product of Motion Lab System. The sensor is small and compact and can be easily attached to the toe. The sensor has a two pin lead which can be connected to the event switch cable and then onto the signal processing board. Figure 2 14 shows a standard event switch sensor. Figure 214. Motion lab system event switch.

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34 The event switch sensor is used as an onof f switch by connecting to the digital input of ServoToGo I/O card using a simple circuit as shown in Figure 2 15 Figure 215. Digital input circuit. As it can be seen from Figure 2 15, the event switch is connected to a 5v power supply and a resistor When load is applied on the event sensor the switch closes and voltage flows through the circuit and the digital input reads 0, and when load is removed the switch opens and the digital input reads 1. Since the ServoToGo houses 32 bits combination of Dig ital I/O spread over four ports of 8 bits each, it had been difficult to read a single bit. So in this experiment the event switch was connected to Port D and the digital input was read as a whole byte after converting from the binary to decimal. In the case of a toe switch when the toe is off the ground, the switch state is open and the byte reading from Port D was 11111111 which when converted to decimal read 255. When the toe strikes the ground the switch state is closed and the byte reading from Por t D was 11111101 which when converted to decimal read 253. Figure 2 16 and 2 17 shows a typical event switch response during a gait cycle.

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35 0 1 2 3 4 5 6 7 8 9 10 11 252 252.5 253 253.5 254 254.5 255 255.5 256 Toe Switch Reading from Digital Input Time in SecsByte Reading (Binary to Decimal)OFF ON Figure 216. Digital input reading of toe switch. 0 1 2 3 4 5 6 7 8 9 10 11 -1 -0.5 0 0.5 1 1.5 2 Toe Switch State Time in SecsSwitch StateON OFF Figure 217. Toe switch state

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36 2.6 Stimulation Circuit The voltage needed for FES of the calf muscle is calculated by the PI control algorithm implemented in C++. The calculated voltage and a reference voltage needed to generate a frequency of 800 Hz are written on to the two D/A channel along with the power s upply voltage. The data from the D/A channel are used as input to design a custom made s timulation circuit. The functions of the circuit are to produce a stimulation voltage and a desired frequency using a voltage to frequency scheme. The stimulation circu it board used for this experiment is shown i n Figure 2.18. The voltage controller is implemented through a Pulse Amplitude Modulation (PAM) with a variable amplitude positive square wave with a fixed pulse width of 100 sec and a fixed frequency of 100 Hz Figure 218. Stimulation circuit board

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37 CHAPTER 3 EXPERIMENTAL METHODS This chapter describes the various steps needed to achieve the thesis objective. 3 1 Desired Trajectory Setup The objective of this experiment is to design a voltage controller to tr ack a desired angular trajectory of the ankle joint from mid stance to toeoff position. Developing the desired trajectory involves two steps; gait phase detection, and actual recording of the trajectory. The gait phase is detected by means of an event swi tch sensor as described in the previous chapter. In this experiment a toe switch is taped at the bottom of the subjects shoe. The switch state is set to ON when the toe strikes the ground and OFF when the toe leaves the ground. The desired trajectory can be recorded every time the switch state is ON; however, it will be difficult to develop a controller to control a varying trajectory within a short period for every gait cycle. For this experiment, the longest duration trajectory for any time the swi tch is ON is chosen as the desired trajectory for developing the closedloop control. This is achieved by the use of a counter in the software program which increments from zero when the switch is ON and then set to zero again when switch is OFF. Onc e the trial experiment is completed the counter plot in Figure 3 1 tells the number of times the toe switch is ON and also the duration for which the switch is ON for each gait cycle. The actual trajectory is recorded from the goniometer sensor feedba ck which is written into a data file during the trial experiment whenever the switch is ON. Once the longest counter duration is chosen from the counter plot (Figure 3 1 ) then it is a simple process of locating that data in the data file and copying it i nto the new data file which will be used as the reference trajectory for developing the closedloop control algorithm.

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38 0 5 10 15 20 25 0 200 400 600 800 1000 1200 Toe Switch Counter Time in SecsSwitch CounterON OFF Figure 31 Toe switch counter for recording desired trajectory The trajectory recording experiment was carried out for two circum sta nces; the subject walked on the treadmill with the support of the orthosis Loko mat, and the subject walked on the treadmil l without the support of the Lokomat. The two different trajectories obtained from the experiment are shown in Figure 3 2 and Figure 3 3 3 2 Desired Trajectory Offset In this experiment there are two systems that have to be executed simultaneously; the Lokomat and treadmill system and the voltage controller system. Each system is controlled by a different person, which leads to an offset in measuring the ankle joint angle every time. Also, the ankle joint angle feedback from the goniometer is described between the foot and the shank of the leg. The local coordinate system used for measuring the ankle and knee joint angle in the sagittal plan e using goniometer is described in Figure 3 4

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39 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 -8 -6 -4 -2 0 2 4 6 8 10 12 14 Desired Trajectory Time in SecsAnkle Angle in Degs Figure 32 Desired trajectory recorded of the subject walking on treadmill with the support of the Lokomat. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 -20 -15 -10 -5 0 5 10 Desired Trajectory Time in SecsAnkle Angle in Degs Figure 33 Desired trajectory recorded of the subject walking on treadmill without the sup port of the Lokomat.

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40 Figure 34 Coordinate system for measuring the ankle and knee angle Figure 35 Actual measurement of the ankle and knee angle Figure 3 5 shows how the ankle and knee angles are measured using the above coordinate system wi th the X axis corresponding to the foot and the Z axis to the shank. Plantar flexion is when the angle between the X and Z axis increases and dorsiflexion is vice versa. Although the foot was always on the ground during the experiment, the orientation of t he shank varied. The subject tends to orient the shank differently at the

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41 start of every experiment which leads to an offset in the ankle angle reading from the goniometer. The ranges of ankle angle remained the same but the maximum and minimum angle varied. The desired trajectory was measured only once so this offset reflected in the computed error during every gait cycle and lead to saturation of the controller after some time. This problem was corrected by software wherein the code captures the first sam ple of the ankle angle from the goniometer when the switch is ON and compares it with the first sample of the desired trajectory. The difference is then added to every point in the desired trajectory to shift the curve to match the actual trajectory. Thi s strategy proved extremely useful in conducting experiments without having to worry about matching the shank orientation every time. 3 3 Experimental Procedure The experiments were conducted on a healthy person at the Human Motor Performance Laboratory (HMPL) housed within the Brain Rehabilitation Research Center (BRRC) located at the Veterans Affairs (VA) hospital in Gainesville, Fl. In this project, two sets of experiments were performed. In the first experiment the voltage controller was tested with the su bject walking on the treadmill without the support of the orthosis Lokomat and with the support of the Lokomat in the second set of experiments. In both experiments, a set of procedures were followed to obtain the final result. At first the goniometer sens ors and the event switches were attached to the subject and the leads were connected to the signal conditioning board ServoToGo. Then for testing with Lokomat (see Figure 3 6 ), additional braces were attached to the subject hip and knee and then suspended over the treadmill platform by means of a harness. Both sets of experiments require the subject to walk on the treadmill without stimulation initially to record the desired trajectory. Once the desired trajectory is recorded the subject is then

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42 allowed to stand without walking on the treadmill platform and stimulation voltage is applied gradually in increments to determine the maximum bound for the voltage. Then the surface electrodes are attached across the subjects calf muscle and the control program is executed which delivers the required voltage based on a proportional integral control method every time the switch is ON to track the ankle angle along a desired trajectory. Figure 36 Experimental setup with the Lokomat and computing system

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43 CHAPTER 4 RESULTS AND DISCUSSIONS In this project two sets of experiment were conducted where the subjects ankle undergoes stimulation while walking on the treadmill without the support of the Lokomat for the first set of experiments and with the support of Lok omat for the second set of experiments. The plots for the first set of experiments (see Figure 4 1 4 4 ) are obtained with kp = 2.0, k i = 0.5, and the maximum voltage is set at 25 volts. The plots for the second set of experiments (see Figure 4 5 4 8 ) are obtained with kp = 5.0, ki = 4.0, and the maximum voltage is set at 27 volts. The initial voltage and the frequency of the controller for both the experiments are set as 10 volts and 25 Hz. The following section presents the results of the two experiments with the following plots 1) actual and desired trajectory 2) error 3) switch state 4) stimulation voltage. From the error plots in Figure 4 2 and 4 6 one can observe that although the error does not vary much between the two experiments, there is an increase in the error at some points in Figure 4 2 Efforts in [42 ] report that the advantage of using a hybrid orthosis device greatly reduces the number of degrees of freedom that contribute to the walking motion. A person spends energy in moving the hip and knee joint while the ankle undergoes muscle stimulation when walking on the treadmill without the Lokomat. During some point of the experiment the muscle fatigue leads to a slow step during that

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44 particular gait cycle This increases the error to a very high value and leads to saturation of the controller (Figure 4 4 ). Variations in step size and duration over each gait cycle were reported when the subject walked on the treadmill without the Lokomat. This lead to difficulty in tuning the gains as the variations occurred over a small window of time. Although the plots (Figure 4 5 4 8 ) from the subject walking with the Lokomat look convincing, the person was capable of walking normally. During the experiment the subject was asked not to perform plantar flexion at some point to test whether the controller is able to produce the required plantar flexor moment. Although the subject reported that the controller produced a plantar flexor moment it is difficult to point out in the plots. The main objective of this project it to provide the required plantar flexor moment during ankle pushoff to prevent individuals from tripping on their toes. Hence, another experiment was performed wit h the person standing on the treadmill without walking and the control program was executed. The human subject was asked to bend the shank forward causing dorsiflexion while the switch was ON mimicking the motion of a person collapsing. However the contr oller produced enough stimulation voltage to generate the required plantar flexion moment to lift the leg causing plantar flexion.

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45 0 2 4 6 8 10 12 14 16 18 20 -20 -15 -10 -5 0 5 10 15 20 25 30 35 Trajectory plot for walking without Lokomat Time in SecsAnkle Angle in Degs Actual Trajectory Desired Trajectory Figure 41 Actual and desired trajectory plot of a person walking on the treadmill without the support of the Lokomat.

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46 0 5 10 15 -15 -10 -5 0 5 10 15 20 25 Error plot for walking without Lokomat Time in SecsAnkle Angle in Degs Figure 42 Error plot of a person walking on the treadmill without the support of the Lokomat.

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47 0 5 10 15 -0.2 0 0.2 0.4 0.6 0.8 1 Switch State for walking without Lokomat Time in Secs Figure 43 Switch state plot of a person walking on the treadmill without the support of the Lokomat.

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48 0 5 10 15 -40 -30 -20 -10 0 10 20 30 40 50 Stimulation Voltage plot for walking without Lokomat Time in SecsVoltage in volts PI Voltage Output Voltage Figure 44 S timulation voltage plot of a person walking on the treadmill without the support of the Lokomat.

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49 0 2 4 6 8 10 12 14 16 18 20 -20 -15 -10 -5 0 5 10 15 20 25 30 Trajectory plot for walking with Lokomat Time in SecsAnkle Angle in Degs Actual Trajectory Desired Trajectory Figure 45 Actual and desired trajectory plot of a person walking on the treadmill with the support of the Lokomat.

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50 0 2 4 6 8 10 12 14 16 18 20 -20 -15 -10 -5 0 5 10 Error plot for walking with Lokomat Time in SecsAnkle Angle in Degs Figure 46 Error plot of a person walking on the treadmill with the support of the Lokomat.

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51 0 2 4 6 8 10 12 14 16 18 20 -0.2 0 0.2 0.4 0.6 0.8 1 Switch State for walking with Lokomat Time in Secs Figure 47 Switch state of a person walking on the treadmill with the support of the Lokomat.

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52 0 2 4 6 8 10 12 14 16 18 20 -120 -100 -80 -60 -40 -20 0 20 40 Stimulation Voltage plot for walking with Lokomat Time in SecsVoltage in volts PI Voltage Output Voltage Figure 48 Stimulation voltage plot of a person walking on the treadmill with the support of the Lokomat.

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53 CHAPTER 5 CONCLUSION This project resulted in the design and validation of a system that can be used to trigger the stimulation of the human ankle joint and to track a desired trajectory efficiently in a safe manner. The toe switch was used to trigger the control algorithm and the goniometer position feedback was used for tracking. The results of the experiment reported in Chapter 4 were conducted on a healthy person who was capable of walking normally. One of the l imitations of this project is due to the use of a discontinuous desired trajectory. Polynomial curve fitting or extrapolation of the discontinuous desired trajectory can be used to obtain the desired velocity and its higher derivatives. The next step is to conduct the experiment on a person with an affected gait and test the performance of the controller. The results from this experiment could lead to a bet ter understanding of the humanL okomat interaction, and in the design of an advanced control system. T he person will be interacting with the Lokomat which could contribute up to some extent in providing the plantar flexor moment along with the FES. In general, the humanL okomat interaction is considered as a disturbance. However, the estimation of the hum an L okomat interaction as described in [ 35 ] can lead to information about the contribution of FES in the progress of rehabilitation. The force produced by the muscle contraction shares a nonlinear relationship with the stimulation voltage. In this project this relationship is assumed as linear. In the future additional controllers will be designed and implemented.

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54 REFERENCES [1] Post Stroke Rehabilitation Fact Sheet National Institute of Neurological Disorders And Stroke National Institutes of Healt h, October 17, 2006. [2] National Spinal Cord Injury Statistical Center, Spinal Cord Injury Facts and Figures at a Glance, Birmingham, Ala.: National SCI Statistical Center, University of Alabama at Birmingham, 2000. [3] B. K. Go M. J. DeVivo, and J S. Richards, The Epidemiology of Spinal Cord Injury, in Spinal Cord Injury: Clinical Outcomes from the Model Systems, eds. S. L. Stover, J. A. DeLisa and G. G. Whiteneck, Aspen, Gaithersburg, 1995. [4] J. Hidler and A. Wall, Alterations in Muscle A ctivation Patterns during Robotic Assisted W alking, Clinical Biomechanics vol. 20, pp. 184 193 2005. [5] S. Jezernik G. Columbo, T. Keller, H. Frueh, and M Morari, Robotic Orthosis Lokomat: A Rehabilitation and Research Tool, Neuromodulation vol. 6, no. 2, pp. 108115, Apr. 2003. [6] G. Colombo M. Joerg, R. Schreier, and V. Dietz, Treadmill Training of Paraplegic Patients Using a Robotic Orthosis, Journal of Rehabilitation Research and Development v ol. 37, no. 6, pp. 693700, 2000. [7] T G. Hornby D. H. Zemon, and D. Campbell, Robotic Assisted, Body Weight Supported Treadmill Training in Individuals Following Motor Incomplete Spi nal Cord Injury, Phys. Ther. v ol. 85, pp. 5266, 2005. [8] G. Colombo Treadmill Training with the Robotic Orthosis Lokomat: New Technical Features and Results from Multi Center Trial in Chronic Spinal Cord Injury, International Journal of Rehabilitation Research, v ol. 27, pp. 9293, 2004. [9] HealthSouth Corporation, AutoAmbulator, Accessed March 2009. Available at: http://www.healthsouth.com/what_we_do/inpatient_rehabilitation/rehabilitation_tec hnology/autoAmbulator.asp [10] S. Hesse, et al. An Electromechanical Gait Trainer for Restoration of Gait in Hemiparetic Stroke Patients: Preliminary Results, Neurorehabilitation and Neural Repair vol. 15, pp. 3950, 2001. [11] S. Hesse and D. Uhlenbrock, A Mechanized Gait Trainer for Restoration of Gait, Journal of Rehabilitation Res. And Dev ., vol. 37, no 6., pp. 701 708, 2000. [12] J L. Emken, J. H. Wynne, S. J. Harkema, and D. J. Reinkensmeyer, A Robotic Device for Manipulating Human S tepping, IEEE Transactions on Robot ics v ol 22, no. 1, pp. 185 189 Feb. 2006.

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55 [13] D. Reinkensmeyer, J. Wynne, and S. Harkema, A Robotic Tool for Studying Locomotor Adaptation and Rehabilitation, in Proc. IEEE EMBS/BMES, 2002, pp. 23532354. [14] J. L Emken a nd D. J. Reinkensmeyer Robot Enhanced Motor Learning: Accelerating Internal Model Formation during Locomotion by Transient Dynamic A mplification, IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 13, no. 1, pp. 33 39 Mar. 2005 [15] J. A. Nessler, et al., A Robotic Device for Studying Rodent Locomotion After Spinal Cord Injury, IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 13, no. 4, pp. 497 506 Dec. 2005 [16] G. Sawicki, K. Gordon, and D. Ferris, Powered Lower Limb Orthoses: Ap plications in Motor Adaptation and Rehabilitation, in Proc. IEEE Int. Conf. on Rehabilitation Robotics Chicago, IL, 2005, pp. 206211 [17] M. Vukobratovic, D. Hristic, and Z. Stojiljkovic, Development of Active Anthropomorphic Exoskeletons, Medical and Biological Eng ., v ol. 12, pp. 66 80, 1974. [18] M. Vukobratovic, B. Borovac, D. Surla, and D. Stokic, Biped Locomotion: Dynamics, Stability, Control and Application, vol. 7, Springer Verlag, 1990. [19] A. Seireg and J. G. Grundman, Design of a Mul titask Exoskeletal Walking Device for Paraplegics, in Biomechanics of Medical Devices D. Ghista, Ed. Marcel Dekker, 1981, pp. 569639. [20] B. J. Ruthenberg, N. Wasylewski, and J. Beard, An Experimental Device for Investigating the Force and Power Req uirements of a Powered Gait Orthosis, Journal of Rehabilitation Research and Development v ol. 34, pp. 203213, 1997. [21] J. A. Blaya and H. Herr, Adaptive Control of a Variable Impedance AnkleFoot Orthosis to Assist Drop Foot Gait, IEEE Trans. of N eural Systems, and Rehabilitation Eng., v ol. 12, pp. 2431, 2004. [22] D. P. Ferris, J. Czerniecki, and B. Hannaford, An AnkleFoot Orthosis Powered by Artificial Muscles, Annual Meeting of the American Society of Biomechanics, San Diego, CA, 2001. [23] G. Sawicki and D. P. Ferris, A kneeankle foot Orthosis (KAFO) Powered by Artificial Pneumatic Muscles, International Society of Biomechanics, Dunedin, New Zealand, 2003. [24] M. A. Townsend and R. J. Lepofsky, Powered Walking Machine Prosthesis for Paraplegics, Med. Biol. Eng., vol. 14, no. 4, pp. 436 444, 1976.

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56 [25] D. Ferris, G. Sawicki, and A. Domingo, Powered Lower Limb Orthoses for Gait Rehabilitation, Top. Spinal Cord Inj. Rehabilitation, vol. 11, no. 2, pp. 34 49, 2005. [26] G. Belforte L. Gastalid, and M. Sorli, Pneumatic Active Gait Orthosis, Mechatronics., vol. 11, no. 3 pp. 301 323 2001. [27] H. Herr and A. Wilkenfeld, User adaptive Control of a Magnetorheological Prosth etic Knee, Indust. Robot., vol. 30 no. 1 pp. 42 55, 20 03. [28] Y. Sankai, Robot Suite HAL (Hybrid Ass istive Limb), Accessed April, 2009 Available at: http://sanlab.kz.tsukuba.ac.jp/english/r_hal.php [29] H. Kawamote and Y. Sa nkai, Power Assist Method Based on Phase Sequence Drive by Interaction Between Human and Robot Suit, IEEE International Work shop on Robot and Human Interactive Communication, 2004. [30] K. Kasaoka and Y. Sankai, Predictive Control Estimating Operators Intention for Stepping up Motion by Exoskeleton Type Power Assist System HAL, in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2001. [31] J. Boyd (2005,Apr.) Bionic Suit Offers Wearer Super Strength, New Scientist (2494), pp. 19. [32] J. E. Pratt, B. T. Krupp, C. J. Morse, and S. H. Collins, The RoboKnee: an Exoskeleton for Enhanced Strength and Endurance During Walking, in Proc. IEEE Int. Conf. on Robotics and Automation, New Orleans, LA 2004 [33] H. Kazerooni, Welcome to the BLEEX Project, Accessed April 20, 2005. Available at http://bleex.me.berkeley.edu/bleex.htm [34] H. Kazerooni, The Human Power Amplifier Technology at the University of California, Berkeley Robot Autonom. Systems., vol. 19, no. 2 pp. 179187 1996. [35] S. Jezernik, G. C olombo, and M. Morari, Automatic Gait Pattern Adaptation Algorithms for Rehabilitation with a 4DOF Robotic Orthosis, IEEE Trans. Robot Automat., vol. 20 no. 3 pp. 574 582 2004. [36] A. Kralj, T. Bajd, R. T urk, J. Krajnik, and H. Benko, Gait Restorati on in Paraplegic Patients: A Feasibility Demonstration Using Multichannel Surface E lectrode FES J Rehabil R D/Veterans Adm Dept of Med Sur Rehabil R&D Serv vol. 20, pp. 3 20, 1983. [37] A. Kralj T. Bajd and R. Turk Enhancement of Gait Restoration i n Spinal Injured Patients by Functional Electrical S timulation Clinical Orthop. vol. 233, pp. 34 43, 1988. [38] Sigmedics. Inc, The Parastep I System, Accessed May 2009. Available at: http://www.sigmedics.com/TheParastep/

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57 [39] D. Popovic, M. Radulovic, L. Schwirtlich, and N. Jaukovic, Automatic vs HandControlled Walking of P araplegics Med. Eng. Phys., vol. 25, pp. 63 73 2003 [40] F. Sepulveda, M. H. Granat, and A. Cliquet Jr ., Gait Restoration in a Spinal Cord Injured Subject via Neuromuscul ar Electrical Stimulation Controlled by an Artificial Neural N etwork Int. Journal of Artif. Organs, vol. 21, pp. 49 62, 1998. [41] R. Spadone, G. Merati, E. Bertocchi, E. Mevio, A. Veicsteinas, and A. Pedotti et al. Energy Consumption of Locomotion wi th Orthosis Versus P arastep Assisted Gait: A Single Case S tudy Spinal Cord, vol. 41, pp. 97 104 2003 [42] T. A. Thrasher and M. R. Popovic Functional Electrical Stimulation of Walking:Function, E xerc ise and Rehabilitation, Annales de readaptation et de medecine physique, vol. 51, pp. 452 460 2008 [43] C. C. Monaghan, W. J. B. M. Van Riel, and P. H. Veltink Control of Triceps S ura e Stimulation Based on Shank Orientation Using a Uniaxial Gyroscope during Gait, Med. Biol. Eng. Comput., vol. 47, pp. 1181 1188, 2009. [44] A. H. Vette K. Masani, J. Y. Kim, and M. R. Popovic Closed Loop Control of FES Assisted Arm Free Standing in Individuals with Spinal Cord Injury: A Feasibility Study Neuromodulation, vol. 12 pp. 22 32 2009 [45] K. J. Hunt, M. Munih, N. Donaldson, and F. M. D. Barr Optimal Control of Ankle Joint Moment: Toward Unsupported Standing in Paraplegia, IEEE Trans. on Automatic Control, vol. 43, pp. 81 9 832 1988. [46] E. C. M. Villalpando Estimation of Ground Reaction Force and Zero Moment Point on a Powered AnkleFoot Prosthesis, M.S. thesis, School of Architecture and Planning, Massachusets Insti. of Tech., Cambridge, M.A, 2006. [47] S. K. Au, P Dilworth, and H. Herr, An Ankle Foot Emulation System for the Study of Human Walking Biomechanics, in Proc. IEEE Int. Conf. on Robotics and Automation, 2006, pp. 29392945. [48] P. E. Crago, P. H. Peckham and G. B. Thrope, Modulation of Muscle Force by Recruitment During Intramuscular Stimulation IEEE Transactions on Biomedical Engineering, vol. 27, pp. 679 684 1980. [49] A. Thrasher, G. M. Graham, and M. R. Popovic Reducing Muscle Fatigue Due to Functional Electrical Stimulation Using Random M odulation of Stimulation Parameters Artificial Organs, vol. 29 pp. 453 458 2005. [50] D. J. Tyler and D. M. Durand Combined Modulation of Pulse Width and Pulse Amplitude to Enhance Functional Selectivity of Neural Stimulation in Proc. IEEEEMBC an d CMBEC, 1995, pp. 11091110.

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59 BIOGRAPHICAL SKETCH Ganeshram Jayaraman was born in Chennai, India in 1986. He receiv ed his bachelors in mechanical engineering from Sathyabama University Chennai, India in May 2007. He received his Master of Science degree in mechanical engineering in the Department of Mechanical and Aerospace Engineering at the University of Florida under the supervision of Dr. Warren E. Dixon in 2010. The primary focus of his research was to develop an FES based ankle orthosis for rehabilitation of individuals suffering from stroke and Spinal Cord Injuries (SCI).