Relevant Elderly Gait Features for Functional Fitness Level Grouping

Marta S. Santos

, Vera Moniz-Pereira

, Andr

e Lourenco

1,3

, Ana Fred

and Ant

onio P. Veloso

Instituto de Telecomunicac¸

oes,1049-001, Lisboa, Portugal.

Univ Tecn Lisboa, Fac Motricidade Humana,CIPER, LBMF, P-1499-002 Lisboa, Portugal

Instituto Superior de Engenharia de Lisboa, Lisboa, Portugal

Keywords:

Functional Fitness Level, Elderly Population, Clustering, Kinematic and Kinetic Parameters, Feature Selec-

tion.

Abstract:

Locomotor tasks characterization plays an important role in trying to improve the quality of life of a growing

elderly population. This paper focuses on this matter by trying to characterize the locomotion of two popu-

lation groups with different functional ﬁtness levels (high or low) while executing three different tasks - gait,

stair ascent and stair descent. Features were extracted from gait data, and feature selection methods were

used in order to get the set of features that allow differentiation between functional ﬁtness level. Unsuper-

vised learning was used to validate the sets obtained and, ultimately, indicated that it is possible to distinguish

the two population groups. The sets of best discriminate features for each task are identiﬁed and thoroughly

analysed.

1 INTRODUCTION

Fall-related morbidity and mortality rates are referred

to as one of the most common and serious prob-

lems faced by the elderly, affecting around 30% of

the population above 65 years (Todd and Skelton,

2004). Several risk factors have been associated with

falls, of which lower limb muscle weakness and gait

and balance deﬁcit seem to have a preponderant role

(Rubenstein, 2006). Accordingly, we have found, in

a cohort of 647 Portuguese older adults, that falls

might not be an inevitable consequence of ageing

and that health, functional ﬁtness and physical activ-

ity parameters were the most determinant factors for

both episodic and recurrent falls (Moniz-Pereira et al.,

2012). Further, we also veriﬁed that the majority of

the falls occurred in an outdoor setting, and mainly

while walking or climbing stairs. Thus, the biome-

chanical characterization of locomotor tasks in older

people with different levels of functional ﬁtness may

have an important contribution for the prevention of

falls and the improvement of quality of life in this

population.

The particular case of locomotion data analysis

presents several inherent difﬁculties (Chau, 2001a),

such a: high-dimensionality (several kinetic and kine-

matic variables acquired through a period of time);

temporal dependence (there’s a quasi-periodic tempo-

ral dependence, being difﬁcult to model); high vari-

ability (intrasubject and intersubject); data is typically

composed by curves which are hard to correlate, and

the relationships between variables are nonlinear.

Usually, gait data analysis is done through statisti-

cal studies (Horv

ath et al., 2001), (Prince et al., 1997)

leading to a series of means and standard deviations

of the parameters measured for pre-determined pop-

ulation groups, which can be hard to analyse and do

not reﬂect the relative importance of the measures in

the problem studied.

Pattern recognition systems have been explored as

an alternative way of looking into gait data. Through

the analysis of gait patterns it has been possible to

detect gait pathologies (Kohle et al., Jun; Hausdorff

et al., 1997), fatigue (Janssen et al., 2011), to eval-

uate the effects of medical procedures on gait (Ishii

et al., 1996), or to detect subject’s features (age group,

ﬁtness level) (Reid et al., 2010). These systems usu-

ally require the following sequence of steps: (1) sens-

ing, (2) segmentation and data cleaning, (3) feature

extraction, and (4) learning. Learning can be super-

vised (where training is required and performed using

labelled samples) or unsupervised (where the system

ﬁnds natural groups in data).

One of the steps required in pattern recognition

systems is feature extraction. Most of the times, fea-

tures are empirically deﬁned by visualization of the

153

Santos M., Moniz-Pereira V., Lourenço A., Fred A. and P. Veloso A..

Relevant Elderly Gait Features for Functional Fitness Level Grouping.

DOI: 10.5220/0004726001530160

In Proceedings of the International Conference on Physiological Computing Systems (PhyCS-2014), pages 153-160

ISBN: 978-989-758-006-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

signal, which can lead to a big amount of extracted

features. Due to the ”curse of dimensionality” prob-

lem (Raudys and Jain, 1991), classiﬁcation error in-

creases with the increase of the number of features

for datasets with few observations. Feature selection

is an optional step performed before (or during) learn-

ing, that eliminates irrelevant features and overcomes

this problem, leading to improvements in the perfor-

mance. As an example, (Begg and Kamruzzaman,

2005) used feature selection in gait data causing an

increase on it’s SVM classiﬁer’s accuracy; and (Chan

et al., 2002) performed this as a pre-step of several

classiﬁers, resulting in an increase of the classiﬁca-

tion rate.

In this work, we will use several kinetic and kine-

matic variables acquired from a group of elderly, to

verify the possibility to distinguish between high and

low functional ﬁtness (FF) levels groups (Rikli and

Jones, 1999; Rose et al., 2006) and which locomo-

tion features are more relevant for the distinction of

these two groups. Due to the small sample available

and since we meant to approach the data in an explor-

ing perspective, unsupervised learning techniques are

used.

The rest of the paper is organized as follows: sec-

tion 2 gives a quick overview of related work; section

3 thoroughly explains the general methodology used

in this work, from data collection, passing by feature

extraction and selection and ﬁnally clustering and val-

idation methods used; section 4 shows the results of

applying the proposed methodology to our dataset; on

section 5 the biomechanical meaning of the selected

features is discussed; and section 6 draws the ﬁnal

conclusions.

2 RELATED WORK

Even though most of the gait pattern recognition in-

vestigation has been focused on supervised learning

(Chau, 2001a) and (Chau, 2001b), some papers have

reported the use of unsupervised learning techniques

to investigate several gait characteristics. In (Xu et al.,

2006), the authors tried to ﬁnd underlying gait pat-

terns among pathological and healthy gaits by apply-

ing k-means and hierarchical clustering algorithms

(Jain and Dubes, 1988) to a series of features previ-

ously extracted. Cluster evaluation was done in terms

of silhouette and mean square error (Halkidi et al.,

2002).

In (Vaughan and O’Malley, 2005) fuzzy cluster-

ing is used to identity different walking strategies in

children and young adults with cerebral palsy. In

(Toro et al., 2007) hierarchical cluster analysis is used

on sagittal kinematic gait data derived from children

with and without cerebral palsy. Different walking

strategies were distinguished by (Su et al., 2001) in

patients with ankle arthrodesis using a fuzzy cluster-

ing technique. Non-hierarchical cluster analysis was

used by (Mulroy et al., 2003) to classify the gait pat-

terns of patients recovering from a stroke based on the

temporal-spatial and kinematic parameters of walk-

ing. In (Jiang et al., 2010), afﬁnity propagation clus-

tering is used to better grouping of gait data based on

the person’s characteristics, and help to explain its re-

lationship with human gait.

As shown there are several different clustering al-

gorithms used for gait pattern recognition. In this

study we apply the classical hierarchical clustering al-

gorithms due to its simplicity and interpretability.

3 METHODOLOGY

Having as goal the separation of two populations

(with high or low functional ﬁtness level), the main

focus of this work was to determine which features,

from the acquired data, would be more relevant.

Several kinematic and kinetic variables were ac-

quired from 3 different locomotor tasks, further de-

scribed. The analysis is performed separately for each

of the tasks, to systematically analyse the features in-

volved, and because the tasks induce a different mor-

phology in some variables.

The features were empirically determined by in-

spection of the signals, and selected using feature se-

lection techniques. For the latter, we used a Wrapper

method (Alelyani et al., 2013) combined with clus-

tering. Finally, the obtained subsets of features were

evaluated against the true label in order to verify the

relevance of the features selected to our problem.

The methodology followed in this paper is system-

atized in ﬁgure 1.

3.1 Experimental Sets and Data

Acquisition

A convenience sample of 27 participants over 65

years was selected from (Moniz-Pereira et al., 2012).

None of them had any neurologic or orthopedic condi-

tion that would affect their gait pattern. Immediately

prior to data collection, all participants were informed

Figure 1: Methodology followed in this work.

PhyCS2014-InternationalConferenceonPhysiologicalComputingSystems

154

about the study, accepted to participate and signed an

informed consent. The Ethics Committee of Faculty

of Human Kinetics approved the study protocol.

Functional ﬁtness level was established according

to a total score (TFFs) of 6 functional ﬁtness tests (the

8 foot up and go, and the 30 second Chair Stand, from

Senior Fitness Test battery (Rikli and Jones, 1999),

and items 4 [step up and over] , 5 [tandem walk], 6

[stand on one leg] and 7 [stand on foam eyes closed]

from the Fullerton Advanced Balance Scale (Rose

et al., 2006)).

Three locomotor tasks were performed by each

subject: gait (G), stair ascent (SA) and stair de-

scent (SD). Several kinetic and kinematic variables

were acquired relative to one gait cycle while per-

forming each task. When performing the locomotor

tasks, participants were barefoot and wore tight black

shorts and t-shirts. Anthropometric measures (sub-

jects body mass, stature and trochanteric height) were

taken and the marker set used was based on the cal-

ibrated anatomical system technique (CAST) (Cap-

pozzo et al., 1995), using a digitizing pointer for the

ASIS markers 2(a).

Kinematic and kinetic data was collected with

a Qualisys Track Manager system (Qualisys AB,

Gothenburg, Sweden) with 8 infrared, high speed

cameras (Qualisys Oqus 300, Qualisys AB, Gothen-

burg, Sweden) working at a frequency of 200 Hz and

synchronized with two Kistler force plates (9281B

e 9283U014 Kistler Instruments Ltd, Winterthur,

Switzerland). For the stairs trials, a wooden staircase

with three steps was built. Each step had 15 cm of

height and 27 cm of depth. The last step was ex-

tended (80 cm depth) in order to avoid deceleration

during stair climbing.

Two force platforms were used. The ﬁrst was em-

bedded on the ﬂoor in front of the staircase, while the

second was covering and securely ﬁxed on the ﬁrst

step. This step was built ensuring an extreme rigidity

of the structure. Each force platform was independent

of the surrounding wooden pieces to ensure adequate

measures.

Participants were asked to walk at their comfort-

able pace. Prior to data collection, training trials were

allowed so that the subjects would become comfort-

able with each task. Three trials from each task were

collected, and the order of the tasks (walking and

stairs) was randomized.

A seven segments (feet, shanks, thighs and pelvis)

model was built for each subject 2(b) and optimized

through inverse kinematics (Lu and O’Connor, 1999)

to minimize the effect of soft tissue artefact. The

joints were modelled as spherical joints, i.e. rota-

tional motion was allowed in the 3 axis, but transla-

(a) Instrumented

suject.

(b) Subject based 7 segment

3D model.

Figure 2: Aquisition set.

tions were restricted.

A fourth order Butterworth low pass ﬁlter at 10Hz

was used for both kinematic and kinetic data. Gait

variables included: (1) foot and pelvis absolute an-

gles, (2) lower limb joint angles (using a XYZ Car-

dan sequence), (3) ground reaction forces, (4) lower

limb joint moments and powers (determined through

inverse dynamics). Kinetic data was normalized to

subjects body mass. As all variables were computed

for the 3 planes of motion (X sagital plane, Y frontal

plane and Z transverse plane), a total of 34 variables

were analysed

All the aforementioned data processing was per-

formed through a continuous pipeline developed

under Visual 3D software (Professional Version

v4.80.00, C-Motion, Inc, Rockville, USA).

3.2 Feature Extraction

Each acquisition comprises a total of 34 kinetic and

kinematic variables acquired during one gait cycle

performing a certain task. The data set contained 3

acquisitions of the same task per individual (from a

total of 27 individuals). The individuals were divided

in two groups according to their total functional ﬁt-

ness score (TFFs) - High FF level (HFFl) and Low

FF level (LFFl). The median of the TFFs was 21 and

the subjects were classiﬁed as having a Low FF score

(TFFs range from 17 to 21 in a total of 14 subjects)

and High FF score (TFFs range from 22 to 24 in a

total of 13 subjects).

Due to limitation of the acquisition setup, in gait

and stair descent tasks, a gait cycle (GC) is consid-

RelevantElderlyGaitFeaturesforFunctionalFitnessLevelGrouping

155

Table 1: Total functional ﬁtness score of the population of

this study. Low TFFs range: 17-21; High TFFs range: 22-

24.

TFFs 17 18 19 20 21 22 23 24

Freq. 1 1 1 4 7 2 3 8

ered from toe off to toe off, and in stair ascent from

heel strike to hell strike. Also, the signal morphology

varied considerably for some variables from task to

task. So, it is not possible to simply compare the vari-

ables when acquired during different tasks, and, there-

fore, the acquisitions are further separated by task per-

formed.

The features extracted included the signals’ mean,

standard deviations, maxima, minima, area under the

curve and skewness. Through visual analysis of each

variable, a set of characteristics was extracted result-

ing in a total of 33, 31 and 37 features extracted for

the G, SA and SD tasks, respectively. The features

were then normalized in amplitude per task.

3.3 Feature Selection

One of the main problems in machine learning is the

selection of relevant features from a set of extracted

features. The feature selection can be divided in two

main tasks: subset selection and subset evaluation.

In this work we used three techniques for subset

selection (Molina et al., 2002): forward, backward

and ﬂoating forward feature selection.

Forward feature selection (FS) is a bottom up

method, i. e., it begins with an empty set and the best

features are added at each step. The best features are

the ones that, together with the rest of the subset of

features already selected, will result in a better score

according to some evaluation criteria.

Backward feature selection (BS) is similar to FS

only it uses a top-down perspective, i. e., it begins

with a full set and deletes the less relevant features.

The less relevant features are the ones which exclu-

sion will lead to a set of features with the highest

score, according to some evaluation criteria.

The main disadvantage of the forward and back-

ward feature selection methods is that they converge

to local maxima of the evaluation function. To avoid

this, and since we have a small number of features and

samples, we have evaluated all the of possible car-

dinalities of the feature subset. This means that we

have studied/evaluated the subsets resulting from set-

ting all the possible values of Min. no. of features as a

stopping criterion. This will return the full behaviour

of the evaluation function allowing us to choose its

global maximum.

Sequential ﬂoating forward feature selection

(FFS) (Pudil et al., 1994) starts with an empty subset

of features as in FS. However, the number of features

does not increase monotonously. The algorithm in-

volves both adding and deleting features. In this way

nesting of feature sets is avoided.

In this study the application of the feature se-

lection step is evaluated a clustering validity index

over the clusters obtained using the subset of fea-

tures under evaluation. We used the Ward’s hierar-

chical method in combination with two clustering va-

lidity indexes: Adjusted Mutual Information score

(AMI) (Vinh et al., 2010);Consistency Index (CI)

(Fred, 2001).

3.4 Clustering

Unsupervised learning refers to the problem of ﬁnd-

ing hidden structure on the data. In this study Ward

clustering (Murtagh and Legendre, 2011) (Jain and

Dubes, 1988) is used and is, therefore, described in

the next subsection. The last subsection, explains the

validation methods used.

Other clustering methodologies, such as k-means,

where used. However their results were worse than

the ones obtained with Ward clustering therefore, and

due to space constrains, these results are not presented

nor this methodology is detailed.

3.4.1 Ward Method

Ward minimum variance method is an hierarchical

clustering method that aims to minimize the sum of

squared differences within the clusters (Murtagh and

Legendre, 2011). It starts by considering each sample

as a single cluster (singleton). Then, it will ﬁnd the

two clusters that, after merging, will lead to the mini-

mum increase in the total within cluster variance. At

each step, the clusters obeying this condition will be

merged until a pre-deﬁned total number of clusters is

reached.

3.4.2 Subset Evaluation and Clustering

Validation

After obtaining the natural clustering partitions of

the data, we need to check if the partitions revealed

are correlated with the parameter we want to investi-

gate,the functional ﬁtness level. This is done by com-

paring the partitions obtained with the data’s true la-

bel using a validation method. The validation method

will return a score that is a measure of the similarity

between the partitions obtained and the true label.

We used two external criteria: Adjusted Mutual

Information score (AMI)(Vinh et al., 2010) and Con-

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156

sistency Index (CI) (Fred, 2001) to compare the ob-

tained results with the ground truth information.

As a Mutual Information function, AMI mea-

sures the agreement of the two assignments, ignoring

permutations. Furthermore, it is normalized against

chance. It is bounded between 0 and 1. Values close

to 0 indicate random or largely independent labels,

while values close to one indicate signiﬁcant agree-

ment. Also, it is invariant to cluster shape so it can be

used with any clustering algorithm.

Let U and V be two clusters, H(U) (eq. 1) and

H(V) (analogous to eq. 1) the entropy of the clus-

ters, I(U, V ) the mutual information between the two

clusters (eq. 2), and E[I(U, V )] the expected mutual

information between the two clusters. The AMI score

is given by equation 3.

H(U) =

|U|

∑

i=1

P(i)log(P(i)) (1)

I(U, V ) =

|U|

∑

i=1

|V |

∑

j=1

P(i, j)log(

P(i, j)

P(i)P( j)

) (2)

AMI(U, V ) =

I(U, V ) − E[I(U, V )]

maxH(U), H(V ) − E[I(U, V )]

(3)

The consistency index (CI) reﬂects the fraction of

shared samples in matching clusters in two data par-

titions, over the total number of samples. It is an iter-

ative procedure that, in each step, determines the pair

of clusters having the highest matching score, given

by the fraction of shared samples. As AMI, it ignores

permutations, is bounded between 0 and 1 (0 means

no matching at all, 1 means perfect match). CI can be

generally expressed by:

CI =

min{nc

,nc

}

∑

i=1

n shared

(4)

where nc

the number of clusters in partition i and

n shared

is the number of samples shared for the i

clusters. One can say that the CI score is the cluster-

ing equivalent to an accuracy measure since it reﬂects

the fraction of well classiﬁed samples.

4 RESULTS

As a baseline approach, we applied the clustering al-

gorithm directly to the extracted features, without per-

forming feature selection. A total of 33, 31 and 37

features were used for clustering in the gait (G), stair

ascent (SA) and stair descent (SD) tasks, respectively.

As a result, we obtained a CI score of 0.667 for the

Table 2: CI score and number of features of the subsets

obtained with the different feature selection conﬁgurations.

The results were obtained with the classical feature selec-

tion algorithms, column ”Typical”, and our adapted version

to ﬁnd the global maximum of the subset evaluation func-

tion, column ”Global max”. Best results are highlighted.

AMI CI

Typical

Global

max

Typical

Global

max

0.827

(13)

0.827

(13)

0.741

(22)

0.741

(22)

0.827

(23)

0.79

(4)

0.852

(14)

0.859

(11)

0.778

(21)

0.815

(16)

0.704

(15)

0.778

(3)

0.679

(1)

0.802

(5)

0.802

(5)

0.802

(5)

0.79

(2)

0.889

(17)

0.889

(4)

0.889

(4)

0.802

(6)

0.815

(16)

0.802

(3)

0.815

(10)

FFS

0.679

(1)

0.802

(5)

0.78

(2)

0.889

(6)

0.852

(7)

0.802

(3)

G and SD tasks, and 0.556 for the SA task, indicating

that the features selected, as a group, did not allow

a good differentiation between the locomotion of the

subjects belonging to the two functional ﬁtness levels.

In order to investigate which features would be

relevant for this purpose, we experimented several

feature selection conﬁgurations. As referred in the

previous sections, three subset search methods where

used (forward, backward and ﬂoating forward feature

selection), combined with two subset evaluation mea-

sures (AMI and CI scores), resulting in 6 different fea-

ture selection conﬁgurations. Also, we tried the typ-

ical BS and FS approach in which the only stopping

criteria is ”no improvement in the evaluation criteria”

versus a search for the global maximum of the evalu-

ation function. We present these results in table 2.

Results improved with feature selection. Also, as

expected, results were generally better with the global

max method; there are few situations where the ﬁrst

maximum coincided with the global maximum of the

evaluation function.

The best CI scores obtained were of 0.827, 0.889

and 0.852 for the G, SA and SD tasks. These results

indicate that the the features identiﬁed by the feature

selection algorithms allow to distinguish the subjects

of each group with a reasonable degree of conﬁdence

and it is worth to analyse the subsets in detail, which

will be done in the next section.

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157

5 SELECTED FEATURES

DISCUSSION

For the results presented in the table 2 we deﬁned best

result as a higher CI score or a lower number of se-

lected features. However, in a biomechanical context,

fewer variables can mean results that are very difﬁcult

to interpret. Indeed, other conﬁgurations presented

subsets with the same score but with a higher num-

ber of features. For the G and SA tasks 3 and 14

conﬁgurations, respectively, presented a score equal

to the one selected as best. For the SA task, the best

subset only contained 4 features, which is not enough

for the biomechanical analysis, so we were forced to

look into other frequently selected features present in

the subsets with the same score as the best one. The

maximum score for the SD task corresponded to a se-

lection of features with small locomotor relevance, so

we investigated the features frequently chosen by sub-

sets with the second higher score for this task - 0.815.

In the next subsections we describe and discuss

the features that are both frequently chosen by high

score subsets and relevant to the locomotor task.

5.1 Gait Task

The group of elderly subjects with lower functional

ﬁtness level (LFFl) walked with the hip more ﬂexed

throughout the stance (ﬁgure 3(a)). (DeVita and

Hortobagyi, 2000) have detected the same difference

when comparing young with elderly subjects. In their

work, the authors suggested that the increased hip

ﬂexion in elderly gait pattern was probably a postu-

ral adjustment in order to be able to produce larger

extensor hip joint moment during stance and to com-

pensate for the lower plantarﬂexor joint moment ex-

erted. Although in this study we have not found dif-

ferences in the hip extensor joint moment, the ankle

plantaﬂexor joint moment peak showed to be lower

in the LFFl group, meaning that these subjects have

a signiﬁcant less vigorous push off. Other authors

(Prince et al., 1997); (Winter, 1991) have also re-

ported a reduction in peak plantarﬂexor moment when

comparing elderly with young subjects. These differ-

ences are also in accordance with the lower ground

reaction force vertical peak showed by the LFFl peak

during the push-off phase.

In contrast with the previously referred studies,

however, we have found that subjects with a LFFl had

a higher knee extensor joint moment peak at the be-

ginning of the stance, during the weight acceptance

phase. As the LFFl subjects also presented a higher

degree of knee ﬂexion (ﬁgure 3(b)) during this phase,

a larger knee extensor moment may be necessary to

control knee ﬂexion and thus to properly support the

body.

Data concerning the other planes of motion is

scarce in the literature for this population. Neverthe-

less, the higher external rotation of the hip, ankle ad-

duction joint moment (ﬁgure 3(c)) and knee abduc-

tor angular impulse seem to suggest a higher effort to

control medio-lateral body stability in the LFFl group.

5.2 Stair Ascent Task

When compared to the HFFl group, the LFFl group

also showed to adopt a different strategy to deal with

the SA task. The higher hip and pelvis ﬂexion angles

(ﬁgures 3(d) and 3(e)) and a higher abduction hip an-

gle may be a strategy of the subjects with low func-

tional ﬁtness level in order to guarantee a safe clear-

ance of the swing leg through the intermediate step.

Also, as mention before for the walking task, a more

ﬂexed hip during the stance may also be a postural

adjustment in order to produce a larger extensor mo-

ment of the hip during the stance (DeVita and Hor-

tobagyi, 2000). In fact, the subjects from the LFFl

group seem to compensate their lack of plantarﬂexor

joint moment during the stance, with a higher exten-

sor hip moment. This was also veriﬁed by (Novak and

Brouwer, 2011), when comparing young and older

subjects. Furthermore, subjects higher functionality

showed, not only to use more their plantarﬂexors, but

also to produce more knee extension power during the

weight acceptance phase.

On the contrary of what has been reported when

comparing young with older subjects (Novak and

Brouwer, 2011), the LFFl group showed a lower hip

abductor joint moment (ﬁgure 3(f)) when compared

to the HFFl group. It could be hypothesize that due

to the higher task demand, the subjects with a lower

functional ﬁtness level were not able to rely as much

as the HFFl subjects on the hip abductor muscles to

control the body lateral stability.

5.3 Stair Descent Task

Finally, for the SD task the more signiﬁcant features

obtained to distinguish the LFFl group from the HFFl

group were difﬁcult to interpret in a biomechanical

point of view. However, if we consider the features

belonging to the second highest score subsets, it is

interesting to verify that in accordance to what was

veriﬁed in the previous tasks, the LFFl group had a

more ﬂexed hip (ﬁgure 3(g)) during the SD task and

produce a higher hip extensor joint moment. Further,

similar to what we have found for the SA task, the

subjects with lower functionality produced a lower

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158

(a) Hip’s angle in X (G).

Feature: mean.

(b) Knee’s angle in X (G).

Feature: 2nd max

in Y (G). Feature: maxi-

mum (40% to 60% of GC),

minimum and mean on the

second third of the signal

(d) Hip’s angle in X (SA).

Features: mean (till 20% of

the GC)

(e) Pelvis’ angle in X

(SA). Feature: mean.

(f) Hip’s joint moment in Y

(SA). Features: mean.

(g) Hip’s angle in X

(SD). Features: mean.

(h) Knee’s momentum force

in Y (SD). Feature: mean.

Figure 3: Plot of some of the gait cycle variables from

which features where selected as most distinctive. Individu-

als with low functionality score are plotted in blue, and high

functionality scores in black.

hip abduction joint moment (ﬁgure 3(h)) during this

task showing therefore not to rely, as much as the

HFFl group, on hip abductors to control the medial

lateral stability of the body.

6 CONCLUSIONS

This paper summarizes the potential of different ki-

netic and kinematic features, acquired using an 7

segments model (feet, shanks, thighs and pelvis), to

distinguish different functional ﬁtness levels in an

sample of elderly population. Unsupervised learning

methodologies were used, and evidence was found

favouring the natural separation of elderly population

groups according to this parameter. Feature selection

has proven to be an effective tool in revealing interest-

ing variables increasing the discriminative capacity.

A set of best distinguishing features for each task

is presented along with an analysis of the features se-

lected and their meaning for the elderly locomotion.

The results showed that some of the differences ob-

served between groups are similar to the ones reported

in the literature when studying differences between

young and old subjects. In general, LFFl subjects

adopted a more ﬂexed hip posture during the anal-

ysed taskstasks. Additionally, they seem, not only to

redistribute joint moments and compensate their lack

of plantarﬂexor moment with a higher hip extensor

moment, but also not to rely on the hip abductors, as

much as the HFFL group, to control medio-lateral sta-

bility in more challenging tasks (SA and SD). These

changes may increase the predisposition to fall in the

LFFl group. Further, this could mean that changes in

gait pattern may not be only a consequence of age-

ing, but also be caused by losses in functionality. The

further investigation of these different gait patterns is

therefore important for the establishment of exercise

programs, aiming to improve functionality and there-

fore to prevent falls, for this population.

Future work includes trying different learning

methods and feature selection methods and an exten-

sive evaluation of the approach for larger data sets.

ACKNOWLEDGEMENTS

The authors are grateful to all the subjects who

volunteered to participate in this study. This

work was supported by the Portuguese Foun-

dation for Science and Technology (SFRH/BD

/36670/2007, SFRH/PROTEC/49512/2009 and

PTDC/DES/103178/2008 - InVivoMuscle).

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