Presentation in a seminar

1. Research Methodology in Gait
Analysis
Dr. P.K.Lenka, PhD, NIOH

2. "Perfection of means and confusion of goals“ seem,
in my opinion, to characterize our age.”
Einstein,‘ Out of My Later Years'

3. What is Research?
• A systematic means of problem solving
(Tuckman 1978)
• 5 key characteristics:
Systematic – research process
Logical – induction/deduction
Empirical – evidence based
Reductive – generalisation
Replicable – methodology.

4. Research process
• Choosing a topic and a supervisor
• Reviewing the literature
• Stating the hypothesis
• Project outline
• Record keeping and note making
• Registering your research project
• Research protocol

5. • Ethical approval
• Funding
• Data collection
• Interpretation of data
• Writing the report
• Presenting a paper
• Writing a paper for publication
Research process

6. Research Process
Formulate a
Question
Select an Appropriate
Research Design
Collect Relevant
Data
Interpret
Findings
Publish
Findings
Review the Available
Literature

7. Types of Research
• Basic and Applied
• Descriptive and Analytical
• Qualitative and Quantitative
• Empirical and Conceptual

8. Research Methods versus
Methodology
Research methods may be understood as all those
methods/techniques that are used for conduction of research.
Research methodology is a way to systematically solve the
research problem. Researchers not only need to know how to
develop certain indices or tests, how to calculate the mean,
the mode, the median or the standard deviation or chi-square,
how to apply particular research techniques, but they also
need to know which of these methods or techniques, are
relevant and which are not, and what would they mean and
indicate and why.

9. Research Methodology in Gait Analysis
• It is an outcome measurements of different activities of
daily living
• Many parameters includes both subjective and objective
parameters
• May be a part of Clinical trial , testing , impact analysis,
motion analysis, modeling , simulation and
quantification.
• Falls under Experimental Research

10. Research Mythology
• Parametric and Non Parametric
• Discrete and Continuous
• Data types
– Nominal, ordinal , Interval and ratio
The number pinned on a sports person.
Pay bands in an organization, as denoted by A, B, C and D.
My level of happiness, rated from 1 to 10.
A person's weight is 50 kg.

11. Gait Data
• Temporal Parameters
• Spatial Parameters
• Anthropometric
Parameters
• Kinematic Parameters
Saggital, Frontal, Trans
– Ankle
– Knee
– Hip
• Metabolic Parameter
• Bio signals
– EMG, EEG, ECG
• Kinetics Parameter
– COM
– COP
– Moment parameters
– Power
– Fx, Fy, Fz

12. Research Design Continuum
Research Design
Analytical Research
Descriptive Research
Experimental Research
Reviews
Historical
Philosophical Case Study Survey
Cross-Sectional
Longitudinal
Correlational
Pre-designs
Quasi-designs
True-designs
Statistical-
designs
Meta-Analyses

13. Source: @Hicks

14. Important variables
• Independent Variable = this variable is the ‘cause’, can
be manipulated or allowed to vary,
• Dependent Variable = this variable is the ‘effect’, also
known as the criterion variable
• Extraneous variables (external to the experiment) must
be controlled to isolate the effect of the IV on the DV
• Confounding Variables = extraneous variables which
have co-varied with the IV

15. Experimental Research
Experimental research is an attempt by the
researcher to maintain control over all factors
that may affect the result of an experiment
• Pre-Experimental
• Quasi-Experimental
• True-Experimental

16. Pre-experimental designs
• Weak experimental designs in terms of control
• No random sampling
• Threats to internal and external validity are
significant problems
• Many definite weaknesses
• Example: One-group pretest/posttest design

17. True experimental designs
• Best type of research design because of their ability
to control threats to internal validity
• Utilizes random selection of participants and random
assignment to groups
• Example: Pretest/posttest control group design

18. Quasi-experimental designs
• These designs lack either random selection of
participants or random assignment to groups
• They lack some of the control of true experimental
designs, but are generally considered to be fine
• Example: Nonequivalent group design

19. Quantitative versus Qualitative
Quantitative Research Strategy
•Investigation aims to assess a pre-stated
theory (Deductive Reasoning)
•Often involves hypothesis testing
•Attempts to minimise the influence of
the researcher on the outcome
•Quantitative data infers statistics
•Data collection therefore requires
‘closed’ responses
Qualitative Research Strategy
•Investigation aims to create a novel
theory (Inductive Reasoning)
•Researcher becomes an inherent part of
the study - ethnography
•Qualitative data infers complex
statements or opinions
•Data collection therefore permits ‘open’
responses

20. Experimental Study Steps
• Identify and define the problem.
• Formulate hypothesis and deduce its consequence.
• Construct an experimental that represents all the elements,
conditions, and relations to the consequence.
• Conduct the experiment.
• Compile raw data and reduce to usable form.
• Apply an appropriate test of significance.

21. Research Essentials
• Manipulation of an independent variable.
• All variables except the dependent variable are
held constant (control).
• Manipulation of the dependent variable by the
independent variable is observed (observation).

22. Control Methods
• Physical control
• Selective control
• Statistical control

23. Physical Control
• Gives all subjects equal exposure to the
independent variable.
• Controls non-experimental variables that effect
the dependent variable.

24. Selective Control
• Indirectly manipulate by selecting in or out
variables that cannot be controlled.

25. Statistical Control
• Variables not conducive to physical or selective
manipulation may be controlled by statistical techniques.

26. Internal Validity
Did the experimental treatment make the
difference in this specific instance rather than
extraneous variables?

27. • History
• Maturation
• Pre-testing
• Measuring instruments
• Statistical regression
• Differential selection
• Experimental mortality
• Interaction of factors
Internal Validity Factors

28. History
• The events occurring between the first and
second measurements in addition to the
experimental variable which might affect the
measurement.

29. Maturation
• The process of maturing which takes place in the
individual during the duration of the experiment
which is not a result of specific events but of
simply growing older, growing tired or similar
changes.

30. Pre-Testing
• The effect created on the second
measurement by having a measurement
before the experiment.

31. Measuring Instruments
• Changes in instruments, calibration of
instruments, observers or scorers may cause
changes in the measurements.

32. Statistical Regression
• Where groups are chosen because of extreme
scores of measurements, those scores tend to move
toward the mean with repeated measurements
even without an experimental variable.

33. Differential Selection
• Different individuals or groups have different
previous knowledge or ability which would
affect the final measurement if not taken into
account.

34. Experimental Mortality
• The loss of subjects from comparison groups
could greatly affect the comparisons because of
unique characteristics of those subjects. Groups
to be compared need to be the same as before
the experiment.

35. Interaction of Factors
• Combinations of many of these factors may
interact especially in multiple group comparisons
to produce erroneous measurements.

36. External Validity
To what populations, settings, treatment variables
and measurement variables can this observed
effect be generalized?

37. • Pre-testing
• Differential selection
• Experimental procedures
• Multiple treatment interference
External Validity Factors

38. Pre-Testing
• Individuals who were pre-tested might be less or
more sensitive to the experimental variable or
might have learned from the pre-test making
them unrepresentative of the population who had
not been pre-tested.

39. Differential Selection
• The selection of the subjects determines how the
findings may be generalized. Subjects selected
from a small group or one with particular
characteristics would limit generalizability.

40. Experimental Procedures
• The experimental procedures and arrangements
have a certain amount of effect on the subjects
in the experimental settings.

41. Multiple Treatment Interference
• If the subjects are exposed to more than one
treatment, then the findings could only be
generalized to individuals exposed to the same
treatments in the same order of presentation.

42. Tools to Control Validity
Jeopardizing Factors
• Pre-test
• Control group
• Randomization
• Additional groups

43. Single Factor Designs
• Pretest-posttest (one-group)
• Pretest-posttest (control group)
• Posttest-only (control group)

44. Pretest-Posttest (one group)
• Quasi-experimental
• One set of measures
taken before and after
treatment or
intervention
• Compare pretest and
posttest scores
• Analysis
– paired t test
• Weakness
– No comparison or
control group

45. Pretest-Posttest (control group)
• Experimental design -
random assignment
• Two groups
– Control
– Experimental
• Measures on dependent
variable made on both
groups pre- and posttest
• Significant differences in
experimental group not
found in control group
attributable to treatment
• Analysis
– difference scores compared
with independent t test
– ANCOVA pretest score as
covariate

46. Multiple Factor Designs
• Two-way factorial
– e.g., 2 x 3
• Three-way factorial
– e.g., 2 x 2 x 3

47. Two-Way Factorial Design
• Studies multiple
independent variables
– Main effects (ME)
– Each with a number of
levels (L)
• Permits study of
interactions
• Analysis
– ANOVA
Example: 2 x 3
ME2
L1 L2 L3
L1
L2
ME1

48. Counterbalanced Design
• Possibility of order effects biasing data in a
repeated measures design
• Solutions
– Randomize order
– Counterbalance trials - order systematically varied
• Example - two treatments (T1 - T2) “Crossover design”
– Half of subjects - T1 then T2
– Half of subjects - T2 then T1

49. Latin Square Design
• Minimizes order effects Test session
Subject 1
Subject 2
Subject 3
A B C
B C A
C A B
1 2 3

50. Single Subject Design
• Permits analysis of effects of treatment in individual
subjects (or groups)
• Elements
– Subjects usually own control
– Repeated measures
– Design phases (times series analysis)

51. Single Subject Design
• Time series analysis
– Dependent measure is
continuous
– Establish baseline
– Measure treatment
effect over time Baseline
Treatment
Time

52. Case Report
• Subject a single individual
• Often uses a narrative format
• May be non-experimental or experimental
– Develops a profile of the subject using:
– Visual observation
– Interviews/surveys/questionnaires
– Objective data
• May provide generalizations about other subjects with similar
conditions

53. Statistical Tools
• Bivariate Correlation/Regression: One continuous Y, one X, both continuous.
• Independent Samples T Test: One continuous Y one dichotomous X,.
• Pearson Chi-Square, 2 x 2: One dichotomous Y, one dichotomous X.
• Multiple Regression: One continuous Y, two or more continuous X’s.
• Polynomial Regression: One continuous Y, one or more continuous X’s and their powers. This allows the
GLM to model data where the regression line is curved (as in quadratic, cubic, etc.)
• One-Way Independent Samples ANOVA: One continuous Y, one dummy coded categorical X.
• Factorial Independent Samples ANOVA: One continuous Y, two or more dummy coded categorical X’s.
• Correlated Samples T and ANOVA: Subjects or blocks represented as an additional X in a factorial design
(the univariate approach) or coded as differences in multiple Y’s (the multivariate approach).
• ANCOV: One continuous Y, one or more dummy coded categorical X’s, one or more continuous X’s.
• MANOVA: Two or more continuous Y’s, one or more dummy coded categorical X’s.
• Discriminant Function Analysis: One or more categorical Y’s, two or more continuous X’s.
• Logistic Regression: One categorical Y (usually dichotomous), one or more continuous X’s and, optionally,
one or more dummy-coded categorical X’s.
• Canonical Correlation/Regression: Two or more Y’s and two or more X’s.

54. Software/Package
• SPSS
• MATLAB
• Excel

56. CORRELATION
Correlation (often measured as a correlation coefficient, ρ)
indicates the strength and direction of a linear relationship
between two random variables.
The Pearson product-moment correlation coefficient
(typically denoted by r) is a measure of the correlation
(linear dependence) between two variables X and Y, giving
a value between +1 and −1 inclusive.
It was first introduced by Francis Galton in the 1880s, and
is named after Karl Pearson.

57. Correlation - Definition
The statistic is defined as the sum of the products of the
standard scores of the two measures divided by the degrees
of freedom.] Based on a sample of paired data (Xi, Yi), the
sample Pearson correlation coefficient can be calculated as
Where
are the standard score, sample mean, and sample standard
deviation (calculated using n − 1 in the denominator).

58. The square of the sample correlation coefficient, which is also
known as the coefficient of determination, is the fraction of the
variance in yi that is accounted for by a linear fit of xi to yi .
This is written
where sy|x
2 is the square of the error of a linear regression
of xi on yi by the equation y = a + bx :
and sy
2 is just the variance of y:
CORRELATION

59. Spearman's rank correlation coefficient or Spearman's rho,
named after Charles Spearman and often denoted by the
Greek letter ρ (rho) or as rs, is a non-parametric measure of
correlation
Spearman's rank correlation coefficient
Sets of data Xi and Yi are converted to rankings xi and yi
before calculating the coefficient
where:
di = xi − yi = the difference between the ranks of corresponding
values Xi and Yi, and n = the number of values in each data set
(same for both sets).

60. Spearman's rank correlation coefficient
IQ, Xi Hours of TV per week, Yi
106 7
86 0
100 27
101 50
99 28
103 29
97 20
113 12
112 6
110 17
IQ, Xi Hours of TV per week, Yi rank xi rank yi
86 0 1
1 0 0
97 20 2 6 −4 16
99 28 3 8 −5 25
100 27 4 7 −3 9
101 50 5 10 −5 25
103 29 6 9 −3 9
106 7 7 3 4 16
110 17
8 5 3 9
112 6 9 2 7 49
113 12 10 4 6 36

61. The values in the column can now be added to find .
The value of n is 10. So these values can now be
substituted back into the equation,
Significance
Spearman's rank correlation coefficient

62. Kendall's rank correlation
Kendall's rank correlation provides a distribution free test of
independence and a measure of the strength of dependence
between two variables.
The Kendall tau coefficient (τ) has the following properties:
•If the agreement between the two rankings is perfect (i.e., the
two rankings are the same) the coefficient has value 1.
•If the disagreement between the two rankings is perfect
(i.e., one ranking is the reverse of the other) the coefficient
has value −1.
•For all other arrangements the value lies between −1 and 1,
and increasing values imply increasing agreement between
the rankings. If the rankings are completely independent,
the coefficient has value 0 on average.

63. Kendall tau coefficient is defined as
where nc is the number of concordant pairs, and nd is the
number of discordant pairs in the data set.
Kendall's rank correlation

64. Autocorrelation or Correlation of Time Series

65. Multiple Correlation
Multiple correlation is done when more than
one variable bears relation to one independent
Variable.
In this case each set of two variables has a ‘r’ value
and when all such correlated variables are combined
We get multiple correlation.
R1.23 = ((r2
12 + r2
13 – (2*r12*r13*r23)) /
(1- r2
23))^0.5

66. Partial Correlation
It is sometimes useful to measure the correlation
between a dependent variable and one particular
independent variable when all other variables are
kept constant. In this case the relationship is called
a partial correlation.
r12.3 = (r12-r13*r23)/((1-r2
13)*(1-r2
23))^0.5

67. Regression analysis refers to techniques for modeling and
analyzing several variables, when the focus is on the
relationship between a dependent variable and one or more
independent variables.
Regression analysis helps us understand how the typical value
of the dependent variable changes when any one of the
independent variables is varied, while the other independent
variables are held fixed.
Regression analysis

68. Regression analysis
Linear regression refers to any approach to modeling the
relationship between one or more variables denoted y and
one or more variables denoted X, such that the model
depends linearly on the unknown parameters to be
estimated from the data.
Such a model is called a "linear model."
Y= mX + C
m = slope of the trend line
C = intercept of the trend line on Y axis

69. Regression analysis
straight line:
parabola:

70. Linear least squares is a computational
approach to fitting a mathematical or
statistical model to data. It can be applied
when the idealized value provided by the
model for each data point is expressed
linearly in terms of the unknown
parameters of the model. The resulting
fitted model can be used to summarize
the data, to predict unobserved values
from the same system, and to understand
the mechanisms that may underlie the
system.
Linear least squares
A plot of the data points (in red), the least
squares line of best fit (in blue), and the
residuals (in green).

71. Residual sum of squares
The residual sum of squares (RSS) is the sum of squares
of residuals. It is a measure of the discrepancy between the
data and an estimation model. A small RSS indicates a tight
fit of the model to the data.
In a standard regression model ,
where a and b are coefficients,
y and x are the regressand and the regressor, respectively, and
ε is the "error term." The sum of squares of residuals is the sum
of squares of estimates of εi, that is

72. Regression analysis
The standard error of the estimate is a measure of the
accuracy of predictions. Recall that the regression line
is the line that minimizes the sum of squared deviations
of prediction (also called the sum of squares error).
The standard error of the estimate is closely related to
this quantity and is defined below:
where sest is the standard error of the estimate, Y is an
actual score, Y' is a predicted score, and N is the number
of pairs of scores.
standard error of the estimate

73. The standard error of the mean (SEM) is the standard
deviation of the sample mean estimate of a population mean.
(It can also be viewed as the standard deviation of the error
in the sample mean relative to the true mean, since the
sample mean is an unbiased estimator.) SEM is usually
estimated by the sample estimate of the population
standard deviation (sample standard deviation) divided
by the square root of the sample size (assuming statistical
independence of the values in the sample):
Standard error of the mean
s is the sample standard deviation (i.e., the sample based
estimate of the standard deviation of the population), and
n is the size (number of observations) of the sample.

74. Common Sources of Error
• Many possible sources of error can cause the results of a
research study to be incorrectly interpreted. The following
sources of error are more specific threats to the validity of a
study than those described previously
• Selected examples:
– Hawthorne Effect
– Placebo Effect
– John Henry Effect
– Rating Effect
– Experimenter Bias Effect

75. Hawthorne Effect
• A specific type of reactive effect in which merely being a
research participant in an investigation may affect behavior
• Suggests that, as much as possible, participants should be
unaware they are in an experiment and unaware of the
hypothesized outcome

76. Placebo Effect
• Participants may believe that the experimental treatment is
supposed to change them, so they respond to the treatment
with a change in performance

77. John Henry Effect
• A threat to internal validity wherein research participants in
the control group try harder just because they are in the
control group

78. Rating Effect
• Variety of errors associated with ratings of a participant or
group
– Halo effect
– Overrater error
– Underrater error
– Central tendency error

79. Experimenter Bias Effect
• The intentional or unintentional influence that an
experimenter (researcher) may exert on a study

80. Statistical Significance
Type I and Type II errors
Ho True Ho False
Reject Ho Type I error Correct decision
Do Not Reject Ho Correct decision Type II error
Null Hypothesis = Ho

81. • 1. Statistical Significance level= .05 / .01(p
value). Reducing probability of finding effect
when it does not exist. TYPE I ERROR.
• 2. Power normally set at 80%. Probability of
finding an effect that does not exist. In low
power (inadequate sample size) important
effects may not be detected. TYPE II ERROR

82. Six different types of Prosthetic Foot
• A- SACH
• B- Multiplex, Dynamic
Response Foot 2
• C- Ranger, Endolite
• D- Jaipur
• E-Ottobock-D
• F- Grissinger
Friday, August 5, 2016 Welcome to NIOH

83. Data Recording Set up
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84. Subjects Detail
• 20 Young (29.14 ± 4.06), Active, Male , Unilateral Involvement
and Experienced of Minimum of 2 years in walking with
Prosthesis
• 20 Control Group(29.8±2.08 years)
Friday, August 5, 2016 Welcome to NIOH

85. Gait Features(27)
• Characteristics common to both Sound and Prosthetic limb (15)
– Age, Height, Weight, k- level ,Cadence, Velocity, Stance Time, Swing Time,
Symmetry Single support, Symmetry Double Supp.
– VCO2 , VO2 , Mean H.Rate, Mean Resp. Rate, E kcal/kgm/min
• Electromyography (EMG) of Sound Limb (3)
– Total Power, Median Frequency and Mean Frequency of average power spectrum
• EMG of Prosthetic Limb (3)
– Total Power, Median Frequency and Mean Frequency of average power spectrum
• Sound Limb loading(3)
– Load at Heel Strike, Load at Mid Stance and load during toe off
• Prosthetic Limb Loading(3)
– Load at Heel Strike, Load at Mid Stance and load during toe off
Friday, August 5, 2016 Welcome to NIOH

86. Stride Characteristics
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87. Kinetic Features(1 Subject, six foot)
Features Sound Side Prosthetic Side
SACH Jaipur Ottobock Grissinge
r
Multifle
x
Ranger SACH Jaipur Ottobock Grissinger Multiflex Rang
er
Load at Heel
Strike(N)
475 542 560 516 527 488 524 518 493 563 495 513
Load at Mid
Stance(N)
328 354 363 384 406 329 340 315 310 420 321 153
Load during toe
off(N)
440 488 612 491 522 424 653 590 421 602 694 615
Friday, August 5, 2016 Welcome to NIOH

88. Force Plate set up

89. Force Plate Features (11)
• AP Range
• ML range
• R.M.S Distance-AP
• R.M.S Distance-ML
• Mean Distance-AP
• Mean Distance-ML
• Mean Velocity AP
• Mean Velocity ML
• Sway Area
• Min Power Frequency AP
• Min Power Frequency ML
Friday, August 5, 2016 Welcome to NIOH

90. Feature Classification using ANN
• Step 1: To train the Net by the features extracted from SEMG
in different artificial feet (MATLAB tool box).
• Step 2: To validate neural network
• Step 3:To analyses the performance of neural network by
interpreting results.
• Step 4: To use the Net for evaluation of residual limb
conditions
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91. Multilayered Neuron Model
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92. Data Management
The extracted features from residual limb using SACH, Jaipur,
Ranger, Grissinger, Ottobock-D and Multiflex foot became the
target vector of main output vector as
a= 1 0 0 0 0 0
0 1 0 0 0 0
0 0 1 0 0 0
0 0 0 1 0 0
0 0 0 0 1 0
0 0 0 0 0 1
Friday, August 5, 2016 Welcome to NIOH

93. Features(38) Subject -1 Subject-2
SAC
H
Jaip
ur
Rang
er
Grissin
ger
M.Fl
ex
Ottobo
ck-D
Gait Common(15)
Height
……..
EMG Sound Limb(3)
R.M.S
………
EMG Prosthetic Limb(3)
R.M.S
………..
Force Sound Limb(3)
……….
Force Prosthetic Limb(3)
……….
Force Plate(11)
……….
Friday, August 5, 2016 Welcome to NIOH

94. Design & Training of NN
• One input layer (38 features, each from 10 subject )
• One Hidden Layer (Neurons & Non linear transfer functions-
tansigmoid)
• One Output Layer( Linear Transfer function, 6 output)
Friday, August 5, 2016 Welcome to NIOH

95. Training , Validation & Testing
• Training Algorithm- Levenberg Marquardt
• Number of epochs: 1000
• Performance goal: 0.001
• Maximum Performance gradient: 10-10
• Maximum No. of neurons tried=10, 7 no. give better results
• Subjects: 10 No. used for training, 5 for validation & 5 for Testing
Friday, August 5, 2016 Welcome to NIOH

96. MATLAB-codes(ANN)
% load input data vector %
p=xlsread('foot_data');
% Map them into interval [-1,1]
%The settings used to perform the linear mappings of
%inputs and targets are returned as ps and ts.
%The input processing settings ps can be used later with
%mapminmax to map other inputs for the network consistently.
[p2,ps]= mapminmax(p);
% set up target vector
n=10;
SACH=[1;0;0;0;0;0];
Jaipur=[0;1;0;0;0;0];
Ranger=[0;0;1;0;0;0];
Grissinger=[0;0;0;1;0;0];
Multiflex=[0;0;0;0;1;0];
Ottobock-D=[0;0;0;0;0;1];
Friday, August 5, 2016 Welcome to NIOH

97. Thank You