A Method to Examine Passive and Active Force Production, and their Correlations with Muscle Morphological Parameters for Healthy Children

DEGREE PROJECT IN MEDICAL

ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2019

A method to examine passive and active force production, and their correlations with muscle morphological parameters for health children

ELIF DUMLU

En metod for att undersoka passiv och

aktiv kraftproduktion och deras

samband med muskelmorfologiska

parametrar hos friska barn

A method to examine passive and active force production, and their correlations with muscle morphological parameters for healthy children.

En metod för att undersöka passiv och aktiv kraftproduktion och deras samband med muskelmorfologiska parametrar hos friska barn

ELIF DUMLU

Degree Project in Medical Engineering Stockholm, Sweden 2019

Supervisor: Ruoli Wang Reviewer: Svein Kleiven

School of Engineering Sciences in Chemistry, Biotechnology and Health

KTH Royal Institute of Technology

Abstract

Muscle morphological and mechanical properties play a crucial role in explaining the mechanisms underlying the development and progression of muscle weakness, joint stiffness, muscle contraction, and the resultant loss of motor function in children.

Information in the literature about how muscle architecture correlate with muscle force

production in passive and active conditions in children is very limited. Therefore, new

information regarding muscle mechanics and morphology has the possibility to

contribute to the improvement of more targeted and more effective treatments for

children. The goal of this project is to develop a feasible experimental method to examine

passive and active muscle force production capacity in the lower limbs in healthy

children and to analyse the correlations of muscle morphological parameters obtained

from diffusion tensor images(DTI) and force generation capacity in passive and active

conditions. For this project, 10 healthy children were recruited and tested in Astrid

Lindgren Children’s Hospital, Karolinska University Hospital. The chosen muscles to

examine was medial gastrocnemius, soleus, and tibialis anterior. Neuroflexor device was

used for the passive force measurements. A fixed version of a hand-held dynamometer

was utilized for the active measurements. In order to capture the muscle activities during

the movement, surface electromyography was collected simultaneously. The findings

from both measurements gave consistent results. In terms of the passive resistance force

measured by NF, the characteristic force peaks can be further analyzed to separate

different contributions for more informative results. Regarding the correlations, stable

and high correlations were determined between the volume(v) and both force

measurements except the medial posterior SOL for MVC. Fascicle length (FL)

correlations showed more of a variety since high correlation was observed for PF and FL

while negligible correlation was found between P3 and FL. Further research with more

parameters is needed to obtain more reliable results. Overall, not only healthy subjects

but also children who suffer from muscle weakness and disabilities should be

investigated for further examination.

Sammanfattning

Muskelmorfologiska och mekaniska egenskaper spelar en avgörande roll för att förklara de mekanismer som ligger till grund för utveckling och progression av muskelsvaghet, stelhet, muskelkontraktur och den förlusten av motorisk funktion hos barn. Litteraturen om hur muskelarkitektur korrelerar med muskelkraft-produktion, i passiva och aktiva förhållanden, hos barn är mycket begränsad. Därför har ny information om muskelmekanik och morfologi möjlighet att bidra till en förbättrad, bättre riktad vård och mer effektiva behandlingar för barn. Målet med projektet är att utveckla en genomförbar experimentell metod för att undersöka passiv och aktiv muskelproduktionskraft i de nedre extremiteterna hos friska barn och att analysera sambanden av muskelmorfologiska parametrar som erhållits från diffusion tensor bilder (DTI) och muskelproduktionskraft passiva och aktiva tillstånd. För detta projekt var 10 friska barn rekryterade och testats i Astrid Lindgrens Barnsjukhus, Karolinska Universitetssjukhuset. De valda musklerna att undersöka var mediala gastrocnemius, m. soleus och m. tibialis anterior. En Neuroflexor- anordning (NF) användes för de passiva kraftmätningarna. En handhållen dynamometer fastmonterad i en ram användes för de aktiva muskelkrafts-mätningarna. För att fånga muskelaktiviteter under rörelsen samlades elektromyografi (EMG) in samtidigt.

Resultaten från båda mätningarna gav konsekvent resultat. I termer av det passiva motståndet kraft som mäts av NF, kan de karakteristiska krafttoppar analyseras ytterligare för att separera olika bidragande faktorer för ett mer informativt resultat. Det fanns en stabil och hög korrelation mellan volym (v) och båda kraftmätningar, förutom den mediala posteriora SOL för MVC. Korrelationen för Fascialängd (FL) visade eftersom hög korrelation observerades för PF och FL medan försumbar korrelation hittades mellan P3 och FL. Det krävs ytterligare forskning med fler parametrar för att få mer tillförlitliga resultat. Sammantaget bör inte bara friska försökspersoner men även barn som lider avmuskelsvaghet och handikapp undersökas för vidare undersökning. I termer av det passiva motståndet kraft som mäts av NF, kan de karakteristiska krafttoppar analyseras ytterligare för att separera olika avgifter för mer informativa resultat.

Beträffande korrelation, var stabila och höga korrelationer bestämmas mellan volymen

(v) och båda kraftmätningar utom den mediala posteriora SOL för MVC. Fascicle längd

(FL) korrelationer visade en större variabilitet eftersom hög korrelation observerades för

PF och FL medan försumbar korrelation hittades mellan P3 och FL. Det krävs ytterligare

forskning med fler parametrar för att få mer tillförlitliga resultat. Sammantaget bör även

barn som lider av muskelsvaghet eller annan funktionsnedsättning inkluderas för vidare

forskning.

Acknowledgements

I would like to thank my project supervisor Ruoli Wang for giving me the opportunity to work with such an interest project and her continuous support throughout this period.

I would also like to thank Cecilia Lidbeck, Michael Remeringen, Ferdinand von Walden and Alexandra Palmcrantz at Astrid Lindgren Children’s Hospital, Karolinska University Hospital that helped in subject recruitment and data collection.

Thanks to Antea Destro for providing me the 3D muscle fascicle parameters for such short amount of time.

I am very thankful to my parents Melih and Dilek Dumlu for their continuous support and making this program possible.

I would finally like to thank my boyfriend Utku for his support and always being by my

side.

Table of Contents

List of figures List of tables

List of abbreviations

1 INTRODUCTION ... 14

2 METHODS ... 16

2.1 ^P

ARTICIPANTS

... 16

2.2 ^M

EASUREMENT

P

ROTOCOL

... 16

2.2.1 The NF method ... 17

2.2.2 Maximum Voluntary Contraction(MVC) Measurement ... 18

2.3 D

ATA

A

NALYSIS

... 20

2.3.1 Data Extraction ... 20

2.3.2 Synchronization ... 20

2.3.3 Averaging and Peak Points ... 21

2.3.4 EMG Post-Processing ... 21

2.3.5 Normalization ... 25

2.3.6 Correlation of muscle force and morphology ... 26

3 RESULTS ... 29

3.1 R

AW

N

EUROFLEXOR DATA

... 29

3.2 C

ORRESPONDING

EMG

FOR FAST AND SLOW MOVEMENT

... 30

3.3 O

VERALL

A

VERAGED

N

EUROFLEXOR

D

ATA

... 31

3.4 MVC F

ORCE

M

EASUREMENTS

... 32

3.5 C

ORRELATION OF THE

M

USCLE

P

ARAMETERS FROM

DTI I

MAGING AND

F

ORCE

M

EASUREMENTS

... 33

4 DISCUSSION ... 38

4.1 T

HE

NF

MEASUREMENTS

... 38

4.2 MVC

MEASUREMENTS

... 38

4.3 C

ORRELATION OF THE

M

USCLE

P

ARAMETERS FROM

DTI I

MAGING AND

F

ORCE

M

EASUREMENTS

... 39

4.4 C

LINICAL CONSIDERATIONS AND LIMITATIONS

... 39

4.5 ^F

UTURE WORK AND POSSIBLE APPLICATIONS

... 39

5 CONCLUSION ... 42

Appendix A-State of the Art A.1 INTRODUCTION ... 46

A.2 FUNCTIONAL ANATOMY OF ANKLE DORSI / PLANTARFLEXORS .... 46

A.3 MUSCLE MORPHOLOGY OF THE LOWER LIMBS ... 47

A.4 CEREBRAL PALSY IN CHILDREN ... 48

A.4.1 S

PASTIC

CP ... 48

A.5.1 K

INEMATICS

... 49

A.5.2 M

USCLE

D

YNAMICS

... 50

A.5.3 ^H

AND HELD DYNAMOMETER

(HHD) ... 54

A.6 IN VIVO IMAGING TECHNIQUES TO MEASURE MORPHOLOGICAL PARAMETERS ... 55

A.6.1 M

AGNETIC

R

ESONANCE

I

MAGING IN SKELETAL MUSCLES

... 55

A.6.2 D

IFFUSION

T

ENSION

I

MAGING IN SKELETAL MUSCLES

... 55

A.7 SUMMARY ... 56

List of Figures

Figure 2.1: Side view of NF with one of the subjects (a) and top view of the

Neuroflexor device (b) (Aggero MedTech AB, Stockholm) ... 18 Figure 2.2: Front (a) and the side view(b) of the custom-made fixation rig for the Hand-

Held Dynamometer ... 19 Figure 2.3: Marker data for fast and slow movement of NF. ... 21 Figure 2.4:EMG post-processing steps for the fast movement of NF. Three muscles as

GA (red), SO (blue), and TA (yellow) is represented. First graph shows the raw EMG signal, second one is the bandpass filtered EMG signal with high pass of 5 Hz cut-off and low pass of 500 Hz cut-off, third graph is the absolute value of the bandpass filtered EMG. The last graph depicts low pass filtered of 50 Hz cut-off, hence the linear envelope of the EMG signal. ... 22 Figure 2.5: EMG post-processing steps for the slow movement of NF. Three muscles as

GA (red), SO (blue), and TA (yellow) is represented. First graph shows the raw EMG signal, second one is the bandpass filtered EMG signal with high pass of 5 Hz cut-off and low pass of 500 Hz cut-off, third graph is the absolute value of the bandpass filtered EMG. The last graph depicts low pass filtered of 10 Hz cut-off, hence the linear envelope of the EMG signal. ... 23 Figure 2.6: EMG post-processing steps for MVC plantarflexion. GA(blue) and

SOL(red) muscles are presented. First graph shows the raw EMG signal, second one is the bandpass filtered EMG signal with high pass of 5Hz cut-off and low pass of 500 Hz cut-off, third graph is the absolute value of the bandpass filtered EMG. The last graph depicts low pass filter of 10 Hz cut-off, hence the linear envelope of the EMG signal. ... 24 Figure 2.7:Normalized EMG signal of GA during fast movement ... 25 Figure 2.8: Normalized EMG signal of GA during slow movement ... 25 Figure 2.9: The process of obtaining muscle morphological parameters from diffusion

tension images (DTI) [52]. 3D slicer was used to obtain the surface model of the

MRI data. DSI studio was then used to attain the fascicle tracts of the DTI data.

length and pennation angle. ... 27 Figure 3.1: Raw NF data for fast and slow movement. The blue line depicts force data

in Newton, whereas the red line denotes angle data in degrees. Time is represented on the y-axis in milliseconds. For the fast movement, 2 force points were selected as P1 and P2 where P1 is the preliminary peak and P2 is the late peak during stretch motion where as for the slow movement, a third peak (P3) was shown for the representation of the fully stretched point [46]. ... 29 Figure 3.2: Corresponding EMG for the raw data represented for fast and slow

movement. The first graph(a) illustrates the raw EMG signal and linear enveloped signal plotted together. Red, blue and, yellow lines show the raw data for GA, SOL and, TA respectively. Green, purple and, the light blue lines demonstrate the linear enveloped data GA, SOL and, TA respectively. The second graph(b) presented in the second row display the linear enveloped data for GA (green), SOL (purple) and, TA (light blue) whereas, the noisy background shows the raw data. ... 30 Figure 3.3:The overall averaged NF data for fast and slow movements. ... 31 Figure 3.4: Correlation of MVC force measurements(PF) and fascicle length(FL) for

medial GA(a) and medial posterior SOL (b) ... 33 Figure 3.5: Correlation of MVC force measurements (PF) and volume for medial

GA(a) and medial posterior SOL(b) ... 34 Figure 3.6: Correlation of NF slow movement force measurement(P3) and fascicle

length(FL) for medial GA(a) and medial posterior SOL(b) ... 35 Figure 3.7: Correlation of NF slow movement force measurement(P3) and volume for

medial GA(a) and medial posterior SOL(b) ... 36 Figure A.16 - Representation of the gastrocnemius(GA), soleus(SO), and tibialis

anterior(TA) muscles. (modified from [3]) ... 46 Figure A.17 - Representation of dorsi/plantarflexion movements with fixed shinbone

(modified from [4]) ... 47 Figure A.5.3- Length-tension curve of muscle showing active, passive and total

force (modified from [25]). ... 52

List of tables

Table 2.1: Characteristics of participants as mean ± SD ... 16

Table 2.2: Protocol for testing muscle group ... 20

Table 2.3: FL, V and P3 values used for the analysis of medial GA. ... 26

Table 2.4:FL,V and P3 values used for the analysis of medial posterior SOL. ... 26

Table 3.1: Force measurements for MVC(mean±SD) ... 32

Table 3.2: Force measurements for MVC. Table below displays 3 plantarflexion and 3

dorsiflexion data for 10 subjects. ... 32

List of abbreviations

2D Two-dimensional
 3D Three-dimensional
 CP Cerebral palsy


DTI Diffusion tensor imaging
 EMG Electromyography FL Fascicle length
 GA Gastrocnemius

HHD Hand-Held Dynamometer

ISEK The International Society for Electrophysiology and Kinesiology MRI Magnetic resonance imaging


MVC Maximum voluntary contraction NF Neuroflexor

PA Pennation angle


PCSA Physiological cross-sectional area PF Plantarflexors


ROM Range of Motion SOL Soleus

TA Tibialis Anterior

V Volume

1 Introduction

Muscle morphological and mechanical properties play a crucial role in explaining the mechanisms underlying the development and progression of muscle weakness, joint stiffness, muscle contraction, and the resultant loss of motor function in children [6].

Muscle morphology is defined as the internal arrangement of muscle fibers within a muscle and has been described as the primary determinant of muscle function in adults.

The active and passive mechanical properties of the muscle and tendon, such as the force- length relationship of muscle and the strain and stiffness of the muscle-tendon unit influence joint compliance and force production which in turn affect joint function.

Information in the literature about how muscle architecture correlates muscle force production in passive and active conditions in children is very limited. Therefore, new information regarding muscle morphology and muscle mechanics has the possibility to contribute to the improvement of more targeted and more effective treatments for children [6].

Anatomy, physiology and biomechanical conditions of muscles are significant factors that determines the overall force production capacity. The total muscle force capacity is defined as the sum of the passive force (resistive force) and the active muscle force (see figure A.3) [9]. In order to predict physical function and contribution to the total force production, muscle stiffness, muscle strength, and joint range of motion(ROM) are some of the most fundamental parameters to observe [18].

Measurement of an object’s or a person’s motion and force production is feasible through various in vivo biomechanical measurement techniques. For this project, surface electromyography was used to capture muscle activities during the passive movement and active movement. Passive resistance measurements were conducted using the Neuroflexor instrument. A customized hand-held dynamometer was utilized for the active measurements.

The aim of this project is to develop a feasible experimental method to examine passive

and active muscle force production capacity in the lower limbs in healthy children and

to analyse the correlations of muscle morphological parameters obtained from diffusion

tensor images(DTI) and force generation capacity in passive and active conditions.

2 Methods

This chapter focuses on experimental data collection and data analysis.

2.1 Participants

10 healthy children were recruited from Astrid Lindgren Children’s Hospital, Karolinska University Hospital (see Table 2.1). The study was approved by Regional Ethics Committee, Karolinska Institute, Stockholm, Sweden. All subjects and their parents signed an informed consent prior experiment.

Table 2.1: Characteristics of participants as mean ± SD

Characteristics Value

Age (years) Height (cm)

9.5 ±2.2 134.9±13.7 Weight (kg) 32±6.9 Foot length (cm) 17.1±10.2 Lower Limb Length (cm) 28.2±8.2 Lever arm (cm) 22.9±0.7

2.2 Measurement Protocol

Experiments took place as passive resistance testing using Neuroflexor device (Aggero MedTech AB, Stockholm) and maximum isometric voluntary contraction(MVC) testing using a customized Hand-Held Dynamometer (HHD) for each subject. Both tests were done only for the right limb and surface EMG’s were recorded for both tests simultaneously.

Prior to the data collection, all the preparations were done in the following order;

1.HHD is charged.

2.EMG’s are charged and prepared for testing.

3.VICON system is calibrated.

4.Four reflective markers are placed on the footplate of the Neuroflexor (NF), which are used for synchronization.

5.Consent is signed for both the patients.

6.Anthropometric measures such as weight, height, leg length, foot length and range of motion (ROM) are measured.

7.Skin preparations were done for the placement of EMG electrodes.

8.EMG electrodes are ready to be placed on medial gastrocnemius(GA), soleus(SOL) and tibialis anterior (TA).

9.Signal for the EMG is tested.

2.2.1 The NF method

Following the preparations, the resistance force was measured using the Neuroflexor instrument (Aggero MedTech AB, Stockholm) at a slow (5◦/s) and a fast (236◦/s) velocity during constant movements. The tested leg rested on the calf support while the foot was placed on the foot plate (see figure 2.1). A relaxed environment was provided where the subject was seated comfortably. The device performed fast and slow movements at a ROM ( -35°flexion to 5° extension) with the knee at 30° and the hip at 90°. For each subject, 5 slow(5◦/s) and 10 fast(236◦/s) movements were performed.

The method for NF has been validated previously on other studies regarding the upper limb [20–22,49-50]. This device calculates passive resistance by using a biomechanical model [45]. The main idea behind this model is the ability of separating passive mechanical components from active components formed by the stretch reflex. Three peak points (P1, P2, P3) at the force-angle-time curve were stated in order to approximate the neural(NC), viscosity(VC), elasticity(EC), and inertial components(IC). The first peak (P1), and the second peak (P2) for the fast movement; the third peak (P3) for the fully stretched position during slow movement were defined [49].

(a) Side view of NF with one of the subject’s lower limb

The calf support part where children rest the lower limb on the device was 3D printed for fitting smaller sizes (see figure 2.1). Participants were told to relax and do not perform any voluntary movement during this measurement. During both the slow and fast movements, surface EMG signals from GA, SOL and TA were also measured simultaneously. Marker data was tracked at 100 Hz using a 12-camera, 3D motion capture system (Vicon Motion Systems, Oxford, UK).

2.2.2 Maximum Voluntary Contraction(MVC) Measurement

The second test was the MVC measurement. A portable, hand held dynamometer (HHD) is one of the common methods for measuring maximum voluntary contraction(MVC) for children [35-37] in the clinical environment. The reliability of measuring plantarflexor MVC using HHD is very low [35]. Therefore, a rig was built for better stabilization of the HHD during the measurement. Maximum isometric muscle strength was measured to present active force capacity using the custom made HHD (see figure 2.2).

Figure 2.1: Side view of NF with one of the subjects (a) and top view of the Neuroflexor device (b) (Aggero MedTech AB, Stockholm) Reflective Marker

Calf support

Foot plate

(b) Top view of NF

For both PF and DF measurements were done at a sitting position in order to eliminate the passive insufficiency during a lie down position and to achieve the optimal conditions for MVC measurements (see table 2.2). While the examiner was getting ready for the trial, each movement’s desired orientation was explained to the subject. Isometric

‘‘make’’ tests were used, in which the examiner held the dynamometer still while the Figure 2.2: Front (a) and the side view(b) of the custom-made

fixation rig for the Hand-Held Dynamometer

Foot plate The rig

The obtained force values were shown here

(a) Front view of the HHD

(b) Side view of the HHD

group. Throughout the test, oral encouragement was given. 3 plantarflexion and 3 dorsiflexion measurements were done, and surface EMG’s were also measured simultaneously. 3 muscle groups as gastrocnemius(GA), soleus(SOL), and tibialis anterior(TA) were tested.

Table 2.2: Protocol for testing muscle group

2.3 Data Analysis 2.3.1 Data Extraction

After the measurements, marker labelling was done using Vicon. All the c3d files for both passive and active measurements were further extracted using MATLAB R2018b and exported into EXCEL 2016. Only the three EMG (GA, SOL, TA) data and 4 (top middle, bottom, left, right) marker data were extracted. NF data was stored in files with extension .xml. This data was exported using xmlstruct MATLAB extension tool [47].

2.3.2 Synchronization

Synchronization was done for the Neuroflexor and EMG data using one of the markers on the NF device. The Y coordinate of the marker were used to identify starting (ts) and ending time (te) of the slow and fast movements. Difference regarding the frequency between NF (100Hz) and EMG (1000Hz) data was taken into consideration. The starting time of the marker was set as the start of EMG data, after this time point NF and EMG data were synchronized (see figure 2.3).

Muscle Group Position Limb/joint positions Ankle Dorsiflexors Sitting Knee 30° fixed, hip 90°

fixed, foot in neutral position

Ankle Plantarflexors Sitting Knee 30° fixed, hip 90°

fixed, foot in neutral

position

2.3.3 Averaging and Peak Points

Following the synchronization, all the NF data were plotted in MATLAB in order to eliminate the trials that did not look reasonable; for instance, subject actively move during the measurement. Out of 10 subjects, two were excluded due to the errors of device. From the remaining 8 subjects, 3 good fast movements and 3 good slow movements were selected for each subject according to [46] for identifying (see figure 3.3) characteristic peak points, each one’s mean were calculated and then all 8 subjects were averaged to get a group mean. For the fast movement, 2 force points were selected as P1 and P2 where P1 is the first peak and P2 is the late peak when the fast movement is completed. For the slow movement, P3 represents resistance force at the fully stretched position [46] (see figure 3.3).

2.3.4 EMG Post-Processing

EMG post-processing was done using MATLAB R2018b. The raw EMG signal was first, bandpass filtered according to [48]. High pass with 5 Hz and low pass with 500 Hz cut- off frequency were used as ISEK recommendations for surface EMG [48]. Bandpass filtered signal was then, rectified, low pass filtered and linear enveloped. For the slow movement of NF and the EMG data for MVC, 4

^th

order Butterworth low pass filter with 10 Hz cut-off frequency was used while for the fast movement of NF, 50 Hz cut-off frequency gave better results.

Plots were obtained as four consecutive graphs for the three muscles as GA (red), SOL Figure 2.3: Marker data for fast and slow movement of NF.

Y coordinate of marker data Y coordinate of marker data

Figure 2.4:EMG post-processing steps for the fast movement of NF. Three

muscles as GA (red), SO (blue), and TA (yellow) is represented. First graph

shows the raw EMG signal, second one is the bandpass filtered EMG signal

with high pass of 5 Hz cut-off and low pass of 500 Hz cut-off, third graph is

the absolute value of the bandpass filtered EMG. The last graph depicts low

pass filtered of 50 Hz cut-off, hence the linear envelope of the EMG signal.

Figure 2.5: EMG post-processing steps for the slow movement of NF. Three

muscles as GA (red), SO (blue), and TA (yellow) is represented. First graph shows

the raw EMG signal, second one is the bandpass filtered EMG signal with high

pass of 5 Hz cut-off and low pass of 500 Hz cut-off, third graph is the absolute

value of the bandpass filtered EMG. The last graph depicts low pass filtered of 10

Hz cut-off, hence the linear envelope of the EMG signal.

Figure 2.6: EMG post-processing steps for MVC plantarflexion. GA(blue) and

SOL(red) muscles are presented. First graph shows the raw EMG signal, second

one is the bandpass filtered EMG signal with high pass of 5Hz cut-off and low

pass of 500 Hz cut-off, third graph is the absolute value of the bandpass filtered

EMG. The last graph depicts low pass filter of 10 Hz cut-off, hence the linear

envelope of the EMG signal.

2.3.5 Normalization

In order to normalize the NF EMG data, EMG for the MVC measurements were averaged at the maximum peak through 1 second time window. EMG during NF measurements were divided by that averaged maximal EMG of MVC for both slow and fast movements (see figure 2.5, figure 2.7).

Figure 2.7:Normalized EMG signal of GA during fast movement

Figure 2.8: Normalized EMG signal of GA during slow

2.3.6 Correlation of muscle force and morphology

Muscle morphological parameters of 4 subjects were obtained from diffusion tension images(DTI) for the statistical analysis (see figure 2.9 for the process). Fascicle length (FL), and volume (V) were used to explore the correlations between force measurements and muscle parameters. In order to correlate parameters, PF measurements were used for the MVC. Concerning the passive resistance measurements, the slow movement of NF hence the third peak(P3) was used (see table 2.3 and 2.4). Due to the small sample size of 4 subject’s parameters, EXCEL’s linear regression analysis was used to compute the linear regression equation and the R

²

(coefficient of determination). Then, R (coefficient of correlation) was computed by taking the square root of R

²

in order to interpret the correlations according to [51].

Table 2.3: FL, V and P3 values used for the analysis of medial GA.

Table 2.4:FL,V and P3 values used for the analysis of medial posterior SOL.

Fascicle Length(mm) Volume(mm

³

) P3(N)

S1 55,6781 5,90E+08 34,5

S2 40,5169 9,12E+08 49,8

S3 47,1547 6,10E+08 24,9

S4 37,2229 4,17E+08 26,6

Fascicle Length(mm) Volume(mm

³

) P3(N)

S1 37,3564 5,62E+08 34,5

S2 33,0794 9,07E+08 49,8

S3 37,2849 5,34E+08 24,9

S4 29,7686 7,05E+08 26,6

Figure 2.9: The process of obtaining muscle morphological parameters from diffusion

tension images (DTI) [52]. 3D slicer was used to obtain the surface model of the MRI

data. DSI studio was then used to attain the fascicle tracts of the DTI data. Lastly,

MATLAB was used to calculate the muscle parameters such as fascicle length and

pennation angle.

3 Results

This chapter is divided into five sections. The first section displays the raw NF data for both fast and slow movements. Figure 3.1 was used to illustrate the raw data since the findings were very consistent for both movements. The second section demonstrates the corresponding EMG data for the same NF data as figure 3.2. The third section displays the overall (all 8 subjects) averaged NF data. Force values regarding the MVC measurements for all subjects with the mean and standard deviation calculations can be found in the section four. The fifth section presents correlations of muscle parameters (FL, V) from DTI imaging and force measurements as figure 3.4, figure 3.5, figure 3.6, and figure 3.7.

3.1 Raw Neuroflexor data

Figure 3.1: Raw NF data for fast and slow movement. The blue line depicts force data in Newton, whereas the red line denotes angle data in degrees. Time is represented on the y- axis in milliseconds. For the fast movement, 2 force points were selected as P1 and P2 where P1 is the preliminary peak and P2 is the late peak during stretch motion where as for the slow movement, a third peak (P3) was shown for the representation of the fully stretched point [46].

(a) Raw NF fast movement (b) Raw NF slow movement

3.2 Corresponding EMG for fast and slow movement

Figure 3.2: Corresponding EMG for the raw data represented for fast and slow movement. The first graph(a) illustrates the raw EMG signal and linear enveloped signal plotted together. Red, blue and, yellow lines show the raw data for GA, SOL and, TA respectively. Green, purple and, the light blue lines demonstrate the linear enveloped data GA, SOL and, TA respectively. The second graph(b) presented in the second row display the linear enveloped data for GA (green), SOL (purple) and, TA (light blue) whereas, the noisy background shows the raw data.

(a) EMG for fast movement

(b) EMG for slow movement

3.3 Overall Averaged Neuroflexor Data

Figure 3.3:The overall averaged NF data for fast and slow movements.

(a) Averaged NF fast movement

(b) Averaged NF slow movement

3.4 MVC Force Measurements

Table 3.1: Force measurements for MVC(mean±SD)

Table 3.2: Force measurements for MVC. Table below displays 3 plantarflexion and 3 dorsiflexion data for 10 subjects.

Subject Number

Plantarflexion

1 2 3

Dorsiflexion

1 2 3 S1

S2

104N 75N

117N 90N

92N 65N

38N 90N

54N 84N

50N 84N

S3 48N 63N 70N 57N 64N 58N

S4 57N 53N 75N 40N 23N 42N

S5 135N 144N 148N 110N 103N 111N

S6 95N 118N 153N 60N 79N 83N

S7 149N 148N 150N 88N 88N 89N

S8 S9 S10

154N 130N 260N

140N 136N 222N

195N 146N 247N

79N 81N 85N

71N 81N 84N

96N 87N 79N Muscle Group Mean ± SD (N)

PF 125.9 ±54.9

DF 74.6 ±21.7

3.5 Correlation of the Muscle Parameters from DTI Imaging and Force Measurements

Figure 3.4: Correlation of MVC force measurements(PF) and fascicle length(FL) for medial GA(a) and medial posterior SOL (b)

(a) Medial gastrocnemius

(b) Medial posterior soleus R=-0,82

R=-0,73

(a) Medial gastrocnemius

Figure 3.5: Correlation of MVC force measurements (PF) and volume for medial GA(a) and medial posterior SOL(b)

(b) Medial posterior soleus R=0,83

R=-0,67

(b) Medial posterior soleus (a) Medial gastrocnemius

Figure 3.6: Correlation of NF slow movement force measurement(P3) and fascicle length(FL) for medial GA(a) and medial posterior SOL(b)

R=-0,09

R=-0,1

(a) Medial gastrocnemius

(b) Medial posterior soleus

Figure 3.7: Correlation of NF slow movement force measurement(P3) and volume for medial GA(a) and medial posterior SOL(b)

R=0,81

R=0,88

4 Discussion

4.1 The NF measurements

The first part of the study was the resistance torque measurements using the NF instrument which can dorsiflex ankle joint at different velocities (stretch the ankle plantarflexors). Measured passive mechanical resistance include the contribution from the viscosity, the inertia, and the elasticity while the neural-related contribution) was characterized as the active component formed by the stretch reflex which is the contraction response to the passive stretching of a muscle.

In patients with spasticity, the elasticity is length-dependent and assumed only on the range of motion (ROM) while the viscous component is velocity dependent. Three peak points were selected to represent the passive resistance force from the NF data. P3 was the resistance force at the end of slow movement, which can represent the joint elasticity and identical in both slow and fast movement. A small partial of viscosity contribution was also included in P3.

Throughout the slow movement, a linear increase was observed when the foot was dorsiflexed passively from the initial position and a slight non-linear increase can be observed towards the end range of the motion.

During the fast movement, two large peaks P1 and P2 were identified (figure 3.1). P1, the initial peak ranged from 20N up to approximately 90N in all subjects, consisted of contributions from viscosity and also depending on the weight of the foot plate. The late peak(P2), represented the summed contribution from the stretch reflex related resistance, the viscosity and the elasticity.

Regarding EMG measurements for NF, we assumed that muscle activity for both ankle dorsiflexor and plantarflexors are negligible during the slow movement. The observation for EMG of SOL during muscle strength measurements was rather low compared to GA.

Since SOL did not reach its maximal capacity during the MVC test trials, it was not presented for this study (figure 2.7 and 2.8).

4.2 MVC measurements

The second part of this study was focused on investigating the active force production

for the lower limb. A simple and a low-cost examination was provided with using the

HHD. Beld et al. [26] provided evidence regarding MVC measurements using HHD for

children. The author stated 65.5 N mean value and 23.3 % standard deviation regarding

ankle dorsiflexors. The values presented in this study are very comparable but slightly

higher with 74.6 N mean and 21.7% standard deviation. This may be due to the difference

in values obtained from foot length, the lever arm (moment arm) length or the age (table 2.1). Dallmeijer et al. [50] also followed a procedure similar to ours and showed corresponding muscle strength values for ankle dorsiflexors and plantarflexors.

4.3 Correlation of the Muscle Parameters from DTI Imaging and Force Measurements

The rule of thumb is used for the interpretation of the size of a correlation coefficient “R”

[51] where values between 0.7 and 0.9 is considered high correlation, values between 0.5 and 0.7 is considered moderate correlation, values between 0.5 and 0.3 is considered low correlation, and values between 0.1 and 0.3 is considered negligible correlation.

Significant and high negative correlations between PF and FL were determined for both muscles (medial GA: R>0.8, medial posterior SOL: R>0.7, figure 3.4). Correlations between PF and volume(V) showed high positive correlations for medial GA (R=0.83, figure 3.5) while moderate negative correlation for medial posterior SOL (R=0.67, figure 3.5). Negligible correlations between P3 and FL (medial GA: R=0.09, medial posterior SOL: R=0.1, figure 3.6) were obtained. Also, high correlations were determined between P3 and V (medial GA: R=0.81, medial posterior SOL: R=0.88, figure 3.7).

4.4 Clinical considerations and limitations

In terms of the clinical considerations, the method this study proposes using the NF device may suggest an easier and a non-invasive way of measuring the passive muscle force production for children. Consistent findings were observed for all subjects except the last two where the server motor of NF was not working properly hence, these findings were excluded from the study. The advantages of using this method may be the differentiation of the passive mechanical and neural-related contributions of the resisting torque.

Regarding the HHD method for healthy children, the tested muscle group was considered resilient. There were some problems regarding the stability of the instrument when examining ankle plantarflexors. A rig was used in order to stabilize the instrument.

However, a more secular solution to this problem may be fixing the HHD to the wall.

This may provide more stability and less dependency of the inspector’s strength.

4.5 Future work and possible applications

The study of estimating muscle force production for passive and active components can

lead to better predictions of muscle mechanical properties which can help explaining the

mechanisms underlying the development and progression of muscle weakness, joint

stiffness, muscle contraction, and the resultant loss of motor function in children. Due to

In terms of the passive resistance force measured by NF, the characteristic force peaks can be further analyzed to separate different contributions for more informative results and for providing references data when examining children with neurological disorders.

Also, more flexible design for fitting children with different sizes can be obtained for the measurement set-up.

Regarding the correlations, only four subject’s muscle parameters were used for the

analysis. Further research with more parameters is needed to obtain more reliable results.

5 Conclusion

The aim of this study was to develop a feasible experimental method to examine passive

and active muscle force production capacity in the lower limbs in healthy children, and

to analyse the correlations of muscle morphological parameters obtained from diffusion

tensor images and force generation capacity in passive and active conditions. The

findings from both measurements gave consistent results. In terms of the passive

resistance force measured by NF, the characteristic force peaks can be further analyzed

to separate different contributions for more informative results as mentioned in the

discussion section. Regarding the correlations, stable and high correlations were

determined between the volume(v) and both force measurements except the medial

posterior SOL for MVC. Fascicle length (FL) correlations showed more of a variety since

high correlation was observed for PF and FL while negligible correlation was found

between P3 and FL. Further research with more parameters is needed to obtain more

reliable results in terms of the correlations. Overall, not only healthy subjects but also

children who suffer from muscle weakness and disabilities should be investigated for

further examination.

Appendix

Appendix A State of Art A.1 Introduction

Information in the literature about how muscle architecture predicts muscle force production in passive and active conditions in healthy children and children with CP is very limited. The aim of this project is to develop a feasible experimental method to examine passive and active muscle force production capacity in the lower limbs in able- bodied children and children with cerebral palsy (CP); assist in data collection and to analyse the correlations of muscle morphological parameters obtained from diffusion tensor images and force generation capacity in passive and active conditions.

The state of the art chapter will briefly summarize cerebral palsy in children, muscle morphology and the functional anatomy of the lower limb with some of the in vivo imaging techniques that can be used to measure morphological parameters. Additionally, it will give an overview of several of the existing biomechanical measurement techniques namely, motion capture system, electromyography, Neuroflexor device and muscle strength measurements.

A.2 Functional anatomy of ankle dorsi / plantarflexors

The main functions of the lower limb are locomotion, supporting the weight of the entire upper body and adapting to gravity [1]. The morphology of the lower limb is designed by the requirement of retaining stability and strength in order to provide these functions.

The foot has the most significant role of the lower limb as to support the weight and locomotion with extension and flexion motions. Dorsiflexion and plantarflexion indicate the flexion or extension of the foot at the ankle. Some of the most important muscles located at the leg are tibialis anterior which is responsible for dorsiflexion of the foot and is antagonistic to gastrocnemius and soleus muscles which are responsible for plantarflexion motion [1,2]. Figure 1 shows the gastrocnemius (GA), soleus (SO) and tibialis anterior (TA) muscles of the lower limb [3].

Figure A.17 - Representation of

the gastrocnemius(GA),

soleus(SO), and tibialis

anterior(TA) muscles. (modified

from [3])

Dorsiflexion

Dorsiflexion is raising of the foot in the upwards direction. The main muscle responsible for this motion is the tibialis anterior. Range of motion is less compared to plantarflexion between 10-30 degrees hence the force generation capacity is 25% lower than plantarflexion motion [1,2].

Plantarflexion

Plantarflexion is the counter action to dorsiflexion that moves the foot at the ground direction. Plantarflexion strikes when the foot is moved downwards between 20-50 degrees therefore the force generation capacity is higher. Main muscles that are responsible for this motion are gastrocnemius and soleus which generate 93% of force during this motion [1,2].

A.3 Muscle morphology of the lower limbs

Skeletal muscle consists muscle fibers and their surrounding connective tissue. Muscle morphology is defined as the internal arrangement of these muscle fibers within a muscle and has been described as the primary determinant of muscle function in children. Muscle fibers can be divided into two categories as type I slow-twitch fibers which are responsible for low tension activities and type II fast-twitch fibers which are more suited for rapid contractions [5]. Groups of muscle fibers are organized in motor units, controlled by one motor neuron.

Understanding the morphological and mechanical properties of the muscle-tendon unit is essential in order to explain the mechanisms underlying the development and progression of muscle weakness, joint stiffness, muscle contracture and the resultant loss of motor function in able bodied children and children with CP. New information of

Figure A.18 - Representation of dorsi/plantarflexion movements with

fixed shinbone (modified from [4])

One of the factors that affect the performance capacity of a muscle is skeletal muscle morphology. Fibers of the human striated muscle can be in parallel or pennate [7]. The most significant parameters to examine this functionality are physiological cross- sectional area (PCSA), muscle thickness (tm), fascicle length (FL) and pennation angle (PA) [7].

PCSA is the main parameter for predicting force capacity [7]. For the two different types of muscle, there are two ways to compute PCSA. PCSA for the pennate muscles can be calculated as the ratio of muscle volume(V) to fascicle length (see equation A.1) whereas PCSA parallel can be calculated as the ratio of muscle mass (m) divided by density (r) times fascicle length (FL) (see equation A.2) [7].

PCSA(pennate)=

#$∗&'( (*+)^!

(A.1) PCSA(parallel)=

_#$∗r^-

(A.2)

A.4 Cerebral Palsy in children

Cerebral palsy (CP) is a group of disorders of movement and posture, often characterized by impairments such as muscle weakness, spasticity and stiffness [5]. CP has lifelong effects on quality of life and daily function therefore, better prediction of outcomes is significant to both the children and their families [8]. The origin of CP is neural however, it is inevitable that muscular and tendinous factors also play a crucial role [9].

A.4.1 Spastic CP

Spastic CP is the most common type of CP and accounts for 80% of all diagnosed cases

[10]. Suffering from spastic CP means an increased muscle tension which can lead to

pain and reduce mobility. The affected muscles are stiffer compared to normal muscles

and this is a result of damaged brain sending incorrect signals to the muscles [10]. Some

of the effects on the lower limbs include: flexion at the knees causing changes in standing

posture, tightness in the calf muscles that results unusual foot posture, flexion at the hip

and adduction at the thighs that causes legs to pull together [11]. Although there is no

cure for spastic CP, there are possible treatment alternatives that can help in control of

the indicators namely; exercise, physical therapy and resistance training. In addition,

Botulinum toxin type A (BoTN-A) injection has been used in the management of CP,

which aims to improve the joint range of motion to achieve a better overall joint function

during motion [12].

A.5 In vivo biomechanical measurement techniques in human movement

A.5.1 Kinematics

A.5.1.1 3D Motion Capture System

Measurement of an object’s or a person’s motion is feasible through various in vivo biomechanical measurement techniques. Motion capture is a system which transfers the movement of a person or an object to digital character, and can be used for human motion analysis [13]. Examining the movement of the whole body or certain joints in three- dimensional space is possible through the usage of at least two cameras [13]. Even though, it is possible to track movements through space with the usage of two cameras, the most common motion caption systems usually use six to twelve cameras [13]. The main reason for this is to increase the accuracy through eliminating potentially blocked movements and provide more clear vision from many angles.

A single camera is able to capture data only in two dimensions as (u; v); hence, this 2D data of a point needs to be converted into three dimensional coordinates as (X; Y; and Z). Direct linear transform (DLT) method can be used to convert 2D data of a point into 3D coordinates. DLT technique uses two equations with eleven-camera parameters for the conversion as presented below [13].

𝑢 = 𝑎𝑋 + 𝑏𝑌 + 𝑐𝑍 + 𝑑 𝑖𝑋 + 𝑗𝑌 + 𝑘𝑍 + 1

𝑣 = 𝑒𝑋 + 𝑓𝑌 + 𝑔𝑍 + ℎ 𝑖𝑋 + 𝑗𝑌 + 𝑘𝑍 + 1

In current motion capture systems, two different camera based systems are used [14].

The first kind is the video-based system which requires the attachment of highly

reflective markers on to the muscle of interest where these markers are tracked by

cameras. These cameras are enclosed by infrared light radiating diodes that send pulses

with the usage of two or more cameras and markers, tracks the position of placed markers on body in 3D and assembles the data into an estimate of the person’s or objects motion.

The advantage of using an active optical motion system compared to a passive one is the automatic differentiation of the markers [14] where it is possible to arrange each markers LED pulse differently. However, the main difficulty of the active system is the need of a power source for each led marker. This may cause inferences during EMG recordings [14].

A.5.2 Muscle Dynamics

A.5.2.1 Electromyography

Electromyography (EMG) is a technique of recording electrical activity (measured in microvolts) of skeletal muscles [15] which can be used to detect problems related to peripheral nervous system and muscle disorders. The main purpose of EMG is to translate the obtained electrical signals (electrical potentials) into readable data as numbers and graphs. It is considered as a very powerful instrument in the clinical sector since it is possible to gain information regarding the motor disability by looking at the structure of the obtained signal. The size, shape and speed can give valuable information on how healthy the targeted muscle is [15]. However, understanding the relation between EMG signals and their contribution to muscle force production is one of the main challenges regarding EMG measurements. Obtaining an accurate EMG signal and electrical activity does not necessarily mean that force production of the muscle is precise. Muscle force is also highly dependent on other parameters such as velocity and fiber length [9].

An EMG signal has an amplitude range of 0-10 mV (+5 to -5). EMG activity is highly correlated with the number of contracted muscles thus, the greater the muscle contraction and the higher the number of triggered muscles, the higher the recorded voltage amplitude will be [16].

There are two methods for measuring EMG as the invasive and the non-invasive method.

The invasive method uses needle electrodes which can be inserted to the target muscle

whereas surface EMG is an entirely non-invasive technology that lets you to simply place

EMG electrodes with stickers on to the skin over the muscle groups of interest. To be

able to attain high-quality data, the recording sites should always be kept clean. Even

though the invasive method may give more accurate EMG-signals, non-invasive

electrodes provide measurement of functional procedures without getting in the way of

movement patterns and conventional routines which makes it an ideal and more

commonly used method [17].

A.5.2.2 Muscle stiffness

In order to predict physical function and contribution to the total force production; muscle stiffness, muscle strength and joint range of motion are some of the most fundamental parameters to observe [18]. The capability of skeletal muscles to stretch deprived of muscle activation can be referred as muscle stiffness [18]. There are a few different methods to estimate muscle stiffness in children such as Ashworth scale, manual muscle testing, palpation, ultrasound shear wave elastography(SWE), and with motorized apparatuses such as Neuroflexor device (Aggero MedTech AB, Stockholm). In most of the cases, qualitative measurements (Ashworth scale, manual muscle testing, palpation) are not considered as reliable as the other methods since it is impossible to distinguish muscle from tendon and joint stiffness [19].

Ultrasound shear wave elastography (SWE) is an evolving ultrasound imaging technique that allows for examining tissue elasticity [19]. One of the advantages of using SWE is that it can provide direct measurement hence, an isolation of the muscle from tendon and joint is feasible. Brandenburg et. al. examined passive muscle stiffness in able-bodied children and children with CP using SWE where fourteen children aged 2-12 were recruited [18]. For each child, physical examination methods (Modified Ashworth Scale (MAS) (children with CP only), ankle range of motion(ROM), Gross Motor Function Classification Scale (GMFCS) level (children with CP only) as well as SWE were conducted. The author concluded that passive muscle stiffness can be directly examined in children with CP using SWE and it is a promising tool for diagnosing musculoskeletal disorders [18].

A.5.2.3 Neuroflexor

Neuroflexor is a device that objectively measures muscle stiffness and elasticity during passive resistance for different velocities. The device uses a computerised biomechanical model to evaluate passive muscle resistance for different velocities produced by structural deviations in muscle and connective tissue [20]. It is a quick assessment which does not create a discomfort for the patient and provides a faster diagnosis compared to other techniques [20]. One of the biggest advantages of using a Neuroflexor device is that it can separate muscular and neuronal cause of spasticity which can help greatly when diagnosing disorders.

In the literature, most studies are conducted using a Neuroflexor is done for the upper

limb. Pontén et.al. investigated the correlation between viscosity, elasticity and the cross-

sectional area of wrist and finger-flexor muscles using a Neuroflexor. Fifteen healthy

subjects aged 8-18 years old were tested using the Neuroflexor device. Accordingly, the

device was only used for healthy children. Their study suggested that Neuroflexor

Kachmar et.al. studied changes in muscle spasticity in patients with Cerebral Palsy after spinal manipulation using a Neuroflexor [22]. Twenty-nine patients, aged from 8-18 years old were tested for spasticity of the wrist flexor using a Neuroflexor device that calculates only the neural components (NC) and reliable results were obtained [22].

A.5.2.4 Muscle strength

One of the characteristics that has the biggest influence on muscle performance is muscle strength. Muscle strength can be described as the capacity of skeletal muscle to generate force for the purpose of stipulating locomotion and stability [18]. Examination of muscle strength in vivo is usually determined by the muscle’s ability to produce a moment. This can be examined in two ways, either by calculating the firing rate of the motor neuron or the number of motor units generated. Data can be collected using a device that estimates the amount of resistance a child can withstand without joint rotation (under isometric contraction), or the direct measurement of moments with the help of a device such as an isokinetic machine [23]. In order to conduct a valid assessment of muscle strength, there are many factors to consider such as contraction velocity, muscle size, fiber types, muscle moment arm and muscle stretch [24].

Anatomy, physiology and biomechanical conditions of muscles are significant factors that determines the overall force production capacity. The total muscle force capacity is also defined as the sum of the passive force (resistive force) and the active muscle force (see figure A.3). This force is highly dependent on velocity and muscle fiber length [9].

The active and passive mechanical properties of the muscle and tendon are essential since the force-length relationship of muscle and stiffness of the muscle-tendon unit influence joint compliance and force production which in turn affect the overall joint function.

Figure A.5.3- Length-tension curve of muscle showing active, passive and total force (modified from [25]).

Muscle contractions can be split into three types as concentric, eccentric and isometric.

When the externally applied force is smaller than force generated by the muscle, muscle

will shorten which will lead to concentric contraction. However, when the external force

is larger, the muscle will lengthen to an eccentric contraction. When the external force is equal to the force generated by the muscle, isometric contraction will take place.

A.5.2.5 Maximum Voluntary Contraction(MVC) measurement in children

Measuring muscle strength in children is a challenge in biomechanics. Earlier studies regarding muscle strength tests for children is limited to field based assessments where sit up or press up tests were conducted in one minute. Not long after it was concluded that these tests measures muscle endurance rather than strength and does not take into account the age and sex difference. Currently, there are a few different existing methods to measure MVC for children for different purposes. The chosen measurement tool for measuring muscle strength should be able to give reliable data with easy to use design and aptitude to establish significant alterations [26,27]. The procedure has to be standardized with respect to the position of the patient, placement of the device, direction of the resistance and force moment arm, in order to be able to make comparisons between the trials [27]. It is crucial to determine the most valid and reliable instrument for muscle strength measurements in order to advance the diagnostic procedure.

A.5.2.6 Isokinetic testing

One of the ways to measure muscle strength in children is feasible through isokinetic testing. Isokinetic testing is done with the usage of a big, non-portable and an expensive isokinetic device with a resistance arm. The main advantage of using this technique is that both concentric and eccentric motion is possible where resistance can be set to different velocities [28,29]. However, the device may be too difficult to adapt for testing children due to its size, and the time required for the adjustment of testing for different muscles [30]. One of the major faults that can be done when choosing a device for MVC measurements is using equipment designed for adults and not taking the nature of growth of a child into consideration [30].

In the literature, many authors tried adjusting the isokinetic device from adults to children by designing an adjustable seat or placing a black pad behind younger children to allow for their lower leg to hang [31,32]. However, many researchers failed when they only adjusted the equipment for children rather than the whole protocol for the software in order to obtain reliable data [33].

Many studies have been performed for MVC measurements for children using isokinetic

testing, some prior findings stated good reliability for isokinetic testing of the knee in 6-

8 year olds (extension r = 0.95; flexion r = 0.85) and assessments of the elbow in 9-10

year olds (extension r = 0.97; flexion r = 0.87) [34]. Nevertheless, others have stated

restrictions regarding seeing no difference between the knee and elbow peak torque

measurements [35].

Ross et al. studied plantarflexor stiffness in children with CP using an altered isokinetic dynamometry and discovered that since children with CP have a reduced range of motion, stiffness arises earlier compared to in-able bodied children [35]. Pierce et al.

investigated isokinetic testing using children with CP and stated good reliability [34].

Yet, he also concluded that reliability reduced as the velocity declined.

A.5.3 Hand held dynamometer(HHD)

Another existing method to measure muscle strength in children is using a hand-held dynamometer. A portable, hand held dynamometer (HHD) is one of the reliable methods for measuring maximum voluntary contraction(MVC) for children [35,36,37]. HHD provides a simple and low-cost measurement while calculating the force production of the muscle movement and letting the operator to provide direct resistance to the movement of the limb [35]. This device provides a measurement of the isometric contraction.

Many studies have investigated the reliability and validity of the usage of HHD in children. Taylor et al. assessed 10 children aged 6-13 years old with spastic CP in order to evaluate the reliability of HHD on five different lower limb muscle groups as hip flexors, hip extensors, hip abductors, knee extensors, and ankle plantarflexors [36]. Berry et al. investigated the reliability of HHD with the recruitment of 15 CP children aged 8- 11 years old and worked on three muscle groups as hip abductors, knee flexors, and extensors [37]. Both studies stated good reliability [36,37].

Crompton et.al. also investigated the reliability of using HHD for MVC measurement in children with CP [38]. The goal of this study was to study the reliability of HHD for MVC measurements at the lower limb with recruitment of 21 CP children. The author concluded acceptable reliability for 5 different muscle groups as ankle dorsiflexors, knee flexors/extensors, and hip flexors/extensors [38].

HHD is a commonly used device for MVC measurements as stated above. However, most of the reliability and validity tests that were conducted regarding HHD is done for adults. There are few papers on validity of HHD measurements for children. Beld et.al.

studied validity and reproducibility of hand-held dynamometer in children aged from 4- 11 years old, all with a suspect of myopathy [26]. Authors measured MVC of 61 children with a calibrated HHD on 11 different muscle groups. Reproducibility was assessed by doing re-tests for a random sample of 40 children who returned for re-measurements.

Validity was evaluated by distinguishing patients with and without myopathy using

different analyzation techniques. As a conclusion, the performance of HHD varied in

different muscle groups with the highest performance gotten in the elbow flexors [26].

A.6 In vivo imaging techniques to measure morphological parameters

Previous studies for the children show the usage of few different non-invasive techniques investigating muscle morphological parameters namely; B-mode ultrasound, elastography and biomechanical measurements. Though, due to the complication of the muscle-tendon-joint system, opposing results were often reported using these different methods.

A.6.1 Magnetic Resonance Imaging in skeletal muscles

Magnetic Resonance Imaging (MRI) is a device that is used for diagnostic purposes which generates images of the bodies soft tissues using strong super conducting magnets and radio waves [39,40]. Usage of MRI for musculoskeletal imaging is becoming more and more common due to its good soft tissue contrast resolution compared to other imaging techniques such as ultrasound and computed tomography (CT). Primary applications for MRI in skeletal muscles are joint disorders, soft tissue tumours, and focal bone marrow diseases [40]. Previous MRI techniques (T1-weighted images (T1WI), T2- weighted images (T2WI)) have been often used to obtain anatomical structure of the muscle and can measure essential parameters such as muscle volume, PCSA etc.

However, the resolute does not give quantitative measurement of the microstructure of the muscle. On the other hand, the more recently developed techniques (Diffusion Tension Imaging(DTI), Diffusion weighted imaging(DWI), Dixon Imaging etc.) are able to distinguish an analysis of architecture, composition and mechanical properties of the both normal and damaged skeletal muscle on a microscopic level regarding the pediatric population [40].

Kim.et.al studied some of the MRI techniques advantages and clinical applications for children [41]. The author presented seven different MRI techniques with their detailed descriptions and advantages regarding clinical applications for the evaluation of muscle disorders. He pointed out that most of the studies are conducted for adults however, the area of muscle disorders regarding children can be very significant for future applications [41].

A.6.2 Diffusion Tension Imaging in skeletal muscles

Diffusion tension imaging also called as diffusion MRI is a diffusion-weighted magnetic

resonance imaging technique that uses diffusion of water molecules to create contrast in