Observation as a Tool for Gait Assessment: Eye, Camera, Vision

and Viewing

Dalibor Kiseljak

1,2 a

and Igor Gruić

University of Applied Health Sciences, Mlinarska cesta 38, Zagreb, Croatia

University of Zagreb, Faculty of Kinesiology, Horvaćanski zavoj 15, Zagreb, Croatia

Keywords: Kinematics, Gait Analysis, Photography, Two-Dimensional Motion Analysis, Perception, Education.

Abstract: The aim of this research was to analyse observation skills through the assessment of human gait. The

hypothesis was that the observation of human gait, in the way experienced practitioners do, would not provide

sufficient results among novice students. The study was conducted retrospectively using the data collected

during Clinical Kinesiology course, in the first semester of the academic year 2020/2021 via on-line seminars.

A total of 190 first-year bachelor level physiotherapy students (120 female and 70 male) participated in the

study (90 full-time and 100 part-time). Within formulated protocol (i.e., defining the gait cycle and its eight

phases), each student made a video recording of a normal walk, in the sagittal plane, according to the left-to-

right convention. In the second and third timepoints, everyone watched a recording of one subject, made in

laboratory. Best average result was in the evaluation of the change between the fifth (pre-swing) and the sixth

(initial swing) phases in the knee (x

= 88.24%), and the best absolute result (100% correct) was achieved in

the 2

and 3

measurement point, between the second (loading response) and third (mid stance) phase in the

hip (average result of all timepoints for that change x

= 82.45%). The worst absolute result (10%) occurred:

1) in the change between the first (initial contact) and second (loading response) phases in the hip, and 2) in

the change between the third (mid stance) and fourth (terminal stance) phases in the ankle, both in the 2

measurement point. Students generally did not accurately assess the human gait (from the initial 43.96%,

through 61.95%, to the final 62.45% distribution of correct answers), in the observational way that

experienced experts do in their clinical practice, due to observational obstacles – perceptive and cognitive.

Technology-free approaches are commonly used in clinical practice due to their simplicity and affordability.

However, these are subjective methods, and the gap should be bridged with an objective assessment approach,

e.g., video-based, or computerized 2D/3D motion analysis.

1 INTRODUCTION

Observation, along with palpation, is the main tool in

the clinical work of a physiotherapist. In observation,

the precision and experience of the examiner is very

important. This is why such a skill should be taught

from the very beginning of formal education at the

university level. In the curriculum, in the Clinical

Kinesiology course, as well as in the Biomechanics

and Applied Biomechanics courses, human gait

analysis is fundamental. Although there are many

options for gait analysis (kinematic, kinetic,

electromyographic), in a clinical sense, kinematic

observational movement analysis, i.e., combined

https://orcid.org/0000-0003-2659-5949

https://orcid.org/0000-0001-6680-8940

complex open and closed kinematic chain movements

such as gait, is usually performed visually. Therefore,

a well-experienced "eye" is needed. When a patient

enters a physiotherapist's office, the presentation of

gait is often the first step in discovering the causes

and consequences of pathology, imbalances, irregular

movement patterns, etc., which are masked in

conventional static postural analysis or diagnostic

approaches that are also static (e.g., MR, X-ray, or

CT). The question is whether the observation is

accurate enough in clinical practice or whether we

have to use instrumented biomechanical analysis

(e.g., methods and systems of optoelectronic gait

analysis) for a proper assessment.

Kiseljak, D. and Grui

c, I.

Observation as a Tool for Gait Assessment: Eye, Camera, Vision and Viewing.

DOI: 10.5220/0012265200003587

In Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2023), pages 249-256

ISBN: 978-989-758-673-6; ISSN: 2184-3201

249

Various automatic imaging measurement

techniques using special purpose hardware have been

developed (Mihradi et al., 2013). Biomechanical

analysis of human gait using an optical tracking

system has been widely applied to assess and

diagnose various pathologies, monitor rehabilitation

methods, and analyse sports techniques (Flores-

Morales et al., 2016). Optical motion capture is an

established tool for assessing biomechanics. Using

standard laboratory equipment ensures the simplicity

of the procedure and its wide applicability

(Eichelberger et al., 2016). Despite this, such systems

have been used in controlled environments, limited

by the research area, with limitations on the mass of

the equipment and the costs of its implementation

(Flores-Morales et al., 2016).

Toister (2020) differentiates between two

different types of experience: vision and viewing. He

explains that vision can be defined as a situation

where similar modes of perception are enabled for

most events in the real world. Viewing, on the other

hand, he explains as a situation that allows the time

required for the cognitive processes of vision to be

separated from the time span of events in the real

world.

Famous painting Horse-racing at Epsom, 1821,

oil on canvas, 92 x 122.5 cm (Figure 1) exhibited in

the Louvre, Paris, was made by the great nineteenth-

century French painter Théodore Gericault. Although

he was initially a neoclassicist (i.e., inclined to

resemble and even magnify real anatomy) (Davies et

al., 2010), he showed horses with outstretched legs in

full flight through the air (Gombrich, 2004). It is so

common sense, dos Santos (2009) points out, that

horse legs position is obviously not true, adding that

the real position is non-intuitive and intangible, made

conceivable only through an instrument.

Figure 1: Horse-racing at Epsom, by Théodore Gericault

(1821).

The introduction of technologies that improved

observation (e.g., the camera obscura, the telescope,

the zoopraxiscope), made it possible to create images

that were dissociated from the tangible and began to

define the real state. A paradigmatic example is

Eadweard Muybridge’s late nineteenth-century

pioneer study of horse locomotion (Figure 2). It

showed that horses never fully extended their limbs

forward and backward, while their hooves were

leaving the ground, as previous illustrators such as

Gericault had been deceived into interpreting (dos

Santos, 2009). Mastandrea and Kennedy (2018)

tested images of horse gaits, including analysis of

Gericault's image. The authors concluded that it is

unrealistic, supporting their conclusion with the

description of fake-gallop horse motion: In a jump,

the front limbs are flexed at the beginning, and as the

horse clears the fence. Then they stretch out, reach

towards the ground, and the back extremities bend

forward and under the body. The authors state that

these findings are consistent with those by Eadweard

Muybridge in 1878. Due to the fast movement, it is

not possible to make an accurate analysis of the

movement by direct perception of galloping horses.

Mastandrea and Kennedy believe that this perceptual

problem can be solved with the help of stopped-

motion photography. On the other hand, at the time

Muybridge's photographs were created, they received

a cool response from artists, other photographers and

certainly the public. The photos were claimed to be

"unnatural" and even "unrealistic" (Toister, 2020).

Figure 2: The horse in motion, by Eadweard Muybridge

(1878).

The aim of this research was to check observation

skills on a sample of absolute beginners, through the

assessment of human gait, and to find out how

effective the kinematic viewing analysis is, or

whether there is a need for an instrumented

biomechanical approach.

The hypothesis was that the observation of human

gait, in the way experienced practitioners do, would

K-BioS 2023 - Special Session on Kinesiology in Sport and Medicine: from Biomechanics to Sociodynamics

250

not provide sufficient results among novice students.

2 METHODS

2.1 Design

The study was conducted retrospectively using the

data collected during Clinical Kinesiology course, in

the first semester of the academic year 2020/2021 via

on-line seminars (i.e., it was the time of Covid-19

pandemic restrictions), where Moodle e-learning

platform and Microsoft Teams software were used.

2.2 Participants

A total of 190 first-year bachelor level physiotherapy

students (120 female and 70 male) participated in the

study. There were 90 full-time students, and 100 part-

time students.

2.3 Procedure

The seminars included three complementary tasks,

through which three measurement points were

completed.

2.3.1 First Measurement Point

The first seminar began with an introductory online

30-minute lecture on gait analysis via Microsoft

Teams. After the theoretical part on the kinesiology

analysis of gait (i.e., defining the gait cycle and the

eight phases of gait), followed the guidelines for the

preparation of homework. Each student had to choose

any person (exclusion criteria were choosing

themselves or any of their colleagues, the presence of

any pathology, such as neuromuscular disorders), and

make a video recording of a normal walk (i.e., using

normal walking speed without speeding up or slowing

down), in the sagittal plane, according to the left-to-

right convention. A blind experiment was ensured by

the suggestion of using a specific shot that shows only

the pelvic area and the lower extremities. The

PowerPoint template for creating the homework was

uploaded to the Moodle e-learning platform. For each

of the eight phases of the walking cycle, in the

mentioned PowerPoint template, there was a place for

a photo that the students had to extract from the video

material, so that each of the eight photos represents a

specific phase of the walking cycle. The next task was

to quickly change the PowerPoint slides to create an

animation (i.e., motion) of the walking cycle, and

visually detect 7 changes (I – VII) between 8 postures

(e.g., the change between the first (initial contact) and

the second (loading response) phase is assigned as

change number I, the change between the second

(loading response) and third (mid stance) phase is

assigned as change number II, etc.), and fill in the

table (Table 1), using the symbols F (for flexion), E

(for extension) or X (in case there is no change in

angle between phases).

Table 1: Seven changes between the eight phases of the gait

cycle in the lower limb joints.

I II III IV V VI VII

Hip

Knee

Ankle

The learning material (a PowerPoint document

with a recorded audio guide and detailed information

about the homework) was uploaded to the Moodle e-

learning platform, and five days were provided for the

task. Students had to enter the observed changes in

the table. The tasks had to be posted on the Moodle

forum (i.e., a PowerPoint document as an attachment

to the discussion on the forum), which was set in the

form of questions and answers, which means that the

insight into the answers of other participants was only

possible after the task was submitted. It was possible

to submit the material only once.

2.3.2 Second Measurement Point

We started the second task a week after the first one,

live online, via the Microsoft Teams interface. The

new task was for everyone to observe the same test

subject (via the photos extracted from the

supplementary materials of the book by Oatis (2009),

the recommended literature for the Clinical

Kinesiology course). The lecturer was changing the

slides at a moderate speed to create an animation.

After the end of the last phase, he went through all the

gait cycle phases once more, but faster, so that the

students could check and finalize their answers. The

instructions to the students were to enter the results

they recorded while watching the presentation into a

word document “Table” (see Table 1) and post the

completed document as an answer to the discussion

on the new forum. It was possible to submit the

material within 30 minutes.

2.3.3 Third Measurement Point

The final task was carried out on the same day, 60

minutes after the second one. The final PowerPoint

document was uploaded to the Moodle e-learning

Observation as a Tool for Gait Assessment: Eye, Camera, Vision and Viewing

251

platform and was to be filled in by drawing lines

connecting the prominent anatomical points for each

of the 8 postures (8 phases of the gait cycle) while

visually determining the 7 angle changes (by

animation – rapid change of slides) for each joint. As

in Ross et al. (2015), joint angles were estimated by

selecting the following anatomical locations digitally:

iliac crest, greater trochanter, and lateral femoral

condyle formed the hip joint angle; and greater

trochanter, lateral femoral condyle, and lateral

malleolus formed the knee joint angle. Students were

instructed to assess the ankle joint angle by drawing

the line between the lateral tibial condyle and the

lateral malleolus, and then distally a line from the

lateral malleolus to the head of the fifth metatarsal

bone. The students should have entered the results in

the table on the last slide of the PowerPoint document

and published the material solved in this way as a

response to the discussion on the final forum.

Additional five days were provided for this final task.

Results based on Perry et al. (1996) are presented

in Table 2, and were shown to the students at the next

seminar, after a week. In the meantime, they could

compare their solutions to the third task with the

solutions of other colleagues via the forum.

Table 2: Correct answers (adapted from Perry et al. (1996)).

I II III IV V VI VII

Hip X

Knee F

Ankle F

E E

2.3.4 Data Reduction

The data were exported to Microsoft Excel, where a

comparison of the correct answers was made. The

main quantitative data analysis was performed using

a frequency analysis.

2.4 Statistical Analysis

All statistical analyses were performed using

STATISTICA v.14 (StatSoft, Inc., Tulsa, OK, USA).

3 RESULTS

Based on the frequency analysis of data on the

answers of 190 first-year students of physiotherapy,

in the Table 3 we have listed the percentages of their

correct answers according to gait phases and

measurement points.

Figure 3 presents main results on the proportion

of correct answers through three measurement points,

on average, and for each of the three observed joints.

On average, the differences between the 1

and

the other timepoints are obvious, in the direction of

better results (from the initial 43.96%, through

61.95%, to the final 62.45%); however, between 2

and 3

timepoint there was almost no change.

Trends in the hip and knee are ascend between the

initial and second measurement points (initial average

39.54% and 2

timepoint 63.61% for the hip, and

48.19% to 77.67% for the knee). There is a noticeable

difference between these points, while the difference

between the 2

and 3

measurement points is

insignificant for both joints (an increase of 2.79% for

the hip and a decrease of 0.61% for the knee). For the

assessment of the ankle joint, the results were quite

the same for all timepoints (44.13% in 1

, 44.58% in

, and 43.91% in 3

Table 3: Percentages of the students’ correct answers for the seven changes between the eight phases of the gait cycle in the

lower limb joints.

I II III IV V VI VII

HIP

First timepoint 15.26 47.36 50.52 21.05 44.73 69.47 28.42

Second timepoint 10.00 100.00 91.05 38.42 88.94 90.00 26.84

Third timepoint 15.78 100.00 92.10 37.36 92.63 90.52 36.31

KNE

First timepoint 31.05 44.73 42.63 57.36 78.94 30.52 52.10

Second timepoint 76.84 93.15 72.10 94.73 90.52 24.21 92.10

Third timepoint 78.42 91.05 58.94 94.73 95.26 25.26 95.78

ANK

First timepoint 58.42 29.47 51.57 48.94 47.89 30.00 42.63

Second timepoint 38.42 60.52 10.00 65.78 57.36 53.15 26.84

Third timepoint 33.68 55.78 12.63 64.73 62.10 54.21 24.21

Legend: I – the change between the first (initial contact) and the second (loading response) phases; II – the change between the second

(loading response) and the third (mid stance) phases; III – the change between the third (mid stance) and the fourth (terminal stance) phases;

IV – the change between the fourth (terminal stance) and the fifth (pre-swing) phases; V – the change between the fifth (pre-swing) and the

sixth (initial swing) phases; VI – the change between the sixth (initial swing) and the seventh (mid swing) phases; VII – the change between

the seventh (mid swing) and the eight (terminal swing) phases.

K-BioS 2023 - Special Session on Kinesiology in Sport and Medicine: from Biomechanics to Sociodynamics

252

Figure 3: Main results on the proportion of correct answers through three measurement points.

The worst mean assessment result for the hip was

the change between the first (initial contact) and the

second (loading response) phases (x

= 13.68%). For

the knee, on average, the assessment of the change

between the sixth (initial swing) and seventh (mid

swing) phases was the least accurate (x

= 26.66%).

The worst average result for the ankle was shown by

the evaluation of the change between the third (mid

stance) and fourth (terminal stance) phases (x

24.73%).

Figure 4: Observational analysis of the change between the

third (mid stance) and the fourth (terminal stance) phase in

ankle; the second measurement point.

Figure 5: Observational analysis of the change between the

second (loading response) and the third (mid stance) phases

in hip; the third measurement point.

Observation as a Tool for Gait Assessment: Eye, Camera, Vision and Viewing

253

The worst absolute result was 10% (which means

that only one in ten students saw that there was no

change in the angle between the proximal and distal

segments between the two phases), and this happened

in two cases: in the second measurement point of the

change between the first (initial contact) and second

(loading response) phases in the hip, and in the

second measurement point of the change between the

third (mid stance) and fourth (terminal stance) phases

in the ankle (Figure 4).

On the other hand, the best average result was in

the evaluation of the change between the fifth (pre-

swing) and the sixth (initial swing) phases in the knee

= 88.24%), and the best absolute result was 100%

correct, achieved in the 2

and 3

timepoint of

observation of the change between the second

(loading response) and third (mid stance) phase in the

hip (Figure 5), with the average result of all

timepoints for that change x

= 82.45%.

4 DISCUSSION

In this study, we examined the ability and skill of

first-year students in observational kinematic gait

analysis.

The hypothesis was confirmed: the students

generally did not accurately assess the human gait

(from the initial 43.96%, through 61.95%, to the final

62.45% distribution of correct answers), in the

observational way that experienced experts do in their

clinical practice.

To draw a parallel with the academic

environment, for this task the students received an

average grade of "Sufficient 2", the lowest passing

grade (i.e., to pass the exam, 60% correct answers are

required). However, this was only achieved at the

second and third timepoints; on average, they initially

failed. The lack of better results at the last two

timepoints, along with the lack of progress between

them, can be interpreted as insufficient knowledge

and skills of the students, but mainly in the context of

observational obstacles – perceptive and cognitive. In

a way, the students fell into the same trap that

Théodore Gericault had found himself in 200 years

earlier. Toister (2020) interprets such obstacles as the

temporal incompatibility of photographic technology,

where viewing is certainly a non-participatory

experience, unlike live viewing or observation in

which the observer is also a participant.

Technology-free approaches, such as

observational gait analysis, are commonly used in

clinical practice due to their simplicity and

affordability. However, these are highly subjective

methods, where the assessment results depend on the

interpretation skills and experience of the clinician

(Michelini et al., 2020). This gap should be bridged

with an objective assessment approach, e.g., video-

based two-dimensional (2D) motion analysis or

computerized three-dimensional (3D) motion

analysis. Some of the answers to the question of why

the students were so imprecise in their observational

assessment could be found in article by Toister

(2020), where he contextualizes that there are many

anatomical reasons for rejecting the comparison

between the eye and the camera (e.g., people have

two eyes and not one; human eyes are never fixed and

are always moving; neurological and cognitive

abilities are important for the perception of depth and

movement in the human vision, and the camera does

not offer anything similar to the above capabilities).

Toister (2020) concludes that human vision is more

similar to videography than photography, if at all.

The results of the assessment were the best for the

knee joint in all three levels of measurement. The

reason could be that the knee is the easiest to observe

due to its greatest range of motion. Ross et al. (2015)

also determined that the results of the testers' knee

joint assessment are closest to normal values, while

the results they obtained for the hip joint and

especially the ankle are not promising.

The students had the most difficulties in detecting

cases where there is no change in angle between

phases (e.g., the second measurement point of the

change between the first (initial contact) and second

(loading response) phases in the hip, and the second

measurement point of the change between the third

(mid stance) and fourth (terminal stance) phases in

the ankle. This means that because observers noticed

the femur shifts backwards, 70% of them (N = 136)

thought they were seeing extension, forgetting that at

the same time the pelvis and torso continued to move

forward (or went to the right, speaking in a two-

dimensional way; as Toister (2020) points out, our

field of vision is elliptical and not enclosed in a

rectangular frame), leaving hip angle unchanged

between the two phases. Similarly, visual detection of

heel-off (see Figure 4), led more than 50% of

assessors (N = 97) to be sure they saw (plantar)

flexion. However, the point is that they missed that

both segments moved from phase to phase and their

relationship to each other did not change (i.e., the

ankle angle remained the same).

It is not that the students are not able to recognize

if there is no change between two segments (e.g.,

there were 72.1% (N = 137) correct answers in the

second measurement point of the change between the

third (mid stance) and fourth (terminal stance) phases

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254

in the knee), but it seems that they had perceptive

problem when both segments moved, and a much

smaller problem when one segment was dominantly

stable, and only one moved (e.g., there were more

than 90% (N = 172) correct answers in the third

measurement point of the change between the sixth

(initial swing) and seventh (mid swing) phases in the

hip, where an open kinetic chain movement included

relatively stable pelvis that supports anterior motion

of the femur).

The results of the first point of measurement (less

than 50% of correct answers (43.96%)) could have

been influenced by two factors: inaccuracy in the

selection of photos (which were extracted from the

video) that adequately represent a particular phase of

the gait cycle, and the possibility that the subjects did

not walk normally (i.e., they were speeding up or

slowing down, their vertical centre of body mass

displacement was too great, etc.).

For that reason, at the second measurement point,

one subject was selected, and eight positions (i.e.,

photographs) were selected by an experienced

practitioner, so that all participants observed identical

material.

However, in the third measurement point,

regardless of the additional PowerPoint support in the

observation, there was no improvement; the

participants seem to have reached their maximum. To

further improve the precision, optoelectronic

biomechanical analysis is needed, especially for the

ankle. Most of the problems with direct measuring

techniques (e.g., goniometry) can be overcome with

optical measuring methods (Mihradi et al., 2013).

However, as Eichelberger et al. (2016) emphasize,

careful optical motion capture system configuration

combined with thorough control of the measurement

process is required to produce high quality results.

Furthermore, passive marker tracking (even with

optokinematic such as Kinovea motion analysis

software or with an automatized optokinematic

analysis system (e.g., BTS, Vicon)) is more precise

than selecting prominent anatomical points on a 2D

photo or video. In their systematic review from 2020,

Michelini et al., referring to the findings of Ross et al.

(2015), point out that by using markers we can expect

higher intra-rater and inter-rater intraclass correlation

coefficient values. For two-dimensional motion

analysis, Fatone and Stine (2015) also suggest using

markers, since manual digitization where markers are

not present is time-consuming and potentially error

prone.

In a reliability study by Ross et al. (2015), the

testers were an experienced physiotherapist and two

final year doctoral students in physiotherapy. Intra-

rater reliability using the same video frame without

markers or practice for students was relatively poor to

moderate, while for an experienced physiotherapist it

was moderate to high.

It is certain that the lack of observation and

digitization practice is even more problematic for

first-year bachelor level physiotherapy students.

Please note that the educational goal of these

seminar tasks was not to check knowledge through

the accuracy of answers (after all, the participants

were students of the first semester of the first year of

bachelor study), but rather to encourage them to think

and make them aware of the importance of quality

observation for future specialists in the field of

physiotherapy.

4.1 Limitations

The study included only novice students. It would be

interesting to make a comparison with the results of

final-year students, and especially experienced

professionals.

Furthermore, when using the symbols F (for

flexion), E (for extension) or X (in case there is no

change in angle between phases), the “gold standard”

(e.g., Perry et al., 1996) seems to have reached its

clinical endpoint due to the mathematical

inconsistency of X symbol registration. It is obvious

that students’ error may have heterogeneity included

within higher “sampling” rate (in Hz) – i.e., even the

slightest F/E occurrence were registered (X means a

fixed value without “acceptable range”, and biology

rarely manifests likewise) – and it is specially in

concordance with e.g., high-tech industry

expectations. Revealing this limitation also serves

future establishment of “new gold standard” with

2D/3D automated video analyses included as a

support to outreach of observational subjectivity

limitations.

5 CONCLUSIONS

In Gericault's painting of a galloping horse, the

outstretched front and rear limbs successfully depict

fast movement. However, that expansions are too

extreme to be realistic, which is consistent with the

expansion of Gericault's artistic expression from

neoclassicism to romanticism. However, in

biomedical science, biomechanics and clinical

practice, there is no room for romanticism, but an

exact and precise approach to assessment is required.

From that perspective, in the context of ideals,

Muybridge is the ultimate winner over Gericault. In a

Observation as a Tool for Gait Assessment: Eye, Camera, Vision and Viewing

255

practical sense, on the example of gait analysis, the

same may apply to optokinematics in relation to

observational methods.

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