Evaluation of KINECT and SHIMMER Sensors for Detection of Gait

Parameters

Katja Orlowski and Harald Loose

Department of Computer Science and Media, Brandenburg University of Applied Sciences,

Magdeburger Str. 50, 14770 Brandenburg, Germany

Keywords:

Motion and Gait Analysis, KINECT and SHIMMER Sensors, Low-cost Sensors, Health Applications.

Abstract:

Detecting gait parameters is possible using various sensors based on different physical principles. In our

investigation a visual system, the Microsoft KINECT, and an inertial sensor system with SHIMMER 9DoF-

sensors, are used for capturing the gait of various persons. Both systems have a small form factor and are

affordable regarding cost. Hence these are well-suited for mobile applications in the health care environment.

Using these low-cost sensor systems, motion capture and analysis can be done in hospitals, physiotherapy units

or nursing homes. This paper focusses on the comparison of detected gait parameters by analyzing statistical

parameters. The examination of accuracy of both systems is carried out in two steps; ﬁrst by initially measuring

the gait of a small group of volunteers and second of a larger group. The noise is also examined which has to

be ﬁltered out in the preprocessing procedure. The choice of ﬁlter impacts the detection of gait parameters.

As a result the noise is characterized rather nonspeciﬁcally in both systems. As expected, the gait parameters

determined by the systems are not identical, but similar. The deviations vary in the speciﬁc gait parameters;

some are less error-prone than other.

1 INTRODUCTION

Nowadays smart, mobile sensors are applicable in

health care. In previous papers their applicability was

discussed and ﬁrst results were shown (see (Orlowski

et al., 2012)). In this paper the algorithms used for

the automatic detection of gait parameters are intro-

duced based on the analysis of the normed gait cycle

by (Perry, 2010). Perry calculated the mean joint an-

gles of the foot, knee, thigh and hip from the gait of

55 subjects. Derived values of those data are used to

check the correctness of the algorithms. The given

normed gait cycle consists of a stance and a swing

phase. The stance starts with putting the heel on the

ground (initial contact, IC) and ends with the detach-

ment of the toe from the ﬂoor (terminal contact, TC).

The TC is the beginning of the following swing phase

which ends with IC. While one foot is in the swing

phase, the other has full contact to the ﬂoor. Perry

et al. subdivided the stance in ﬁve, and the swing in

three phases. In our investigation only three of eight

phases are considered. In addition to stance and swing

the midswing, the middle third of the swing phase, is

investigated. The midswing begins when the swing-

ing foot crosses the standing one and ends when the

Figure 1: Gait cycle - left (black) and right (white) leg with

the swing and stance phase marked by colored stripes and

IC, TC, midswing and -stance (Murray and Kory, 1964).

”swinging limb is forward and the tibia is vertical”

(Perry, 2010). It is the interval from about 75-87 % of

the gait cycle.

Figure 1 shows a whole stride for each leg (left

- black, right - white) and the percentage of walking

cycle. An overlap of the stance phases of both legs is

identiﬁable. During normal walking, the swing phase

is always shorter than the stance, and the swing phase

of one leg is within the stance phase of the other. A

40 to 60 percent relationship between swing to stance

phase is generally assumed (Perry, 2010).

This paper includes a comparison of the gait pa-

rameters of the Kinect and SHIMMER sensors based

on the data of a step sequence of 26 volunteers. Fur-

thermore, the sensor noise is investigated through sep-

arate experiments as the choice of the ﬁlter inﬂuences

157

Orlowski K. and Loose H..

Evaluation of KINECT and SHIMMER Sensors for Detection of Gait Parameters.

DOI: 10.5220/0004227901570162

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2013), pages 157-162

ISBN: 978-989-8565-36-5

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

the quality of the results. The accuracy of the sensors

is evaluated using statistical values.

2 SYSTEMS AND EXPERIMENTS

2.1 Systems

For motion capture and analysis different sensor types

are used, which can generally be distinguished due

to different recording principles and ﬁelds of ap-

plication. The Microsoft KINECT sensor system

(KINECT) and the Shimmer

sensors (SHIMMER)

are predestined for mobile health applications be-

cause of their small size (see Fig. 2) and the compar-

atively low cost. While KINECT is a visual system

based on an absolute reference system, the SHIM-

MER (9-DoF-sensor) is an inertial system working

with a relative reference system. That system cal-

culates incrementally the position of the sensor by

knowing the initial position.

The depth image of the KINECT is produced by

measuring the distortion of the reﬂected dots from the

pseudo-random beam pattern sprayed out from the

IR laser (Taylor, 2011). Further information on the

used systems can be found in earlier papers (Orlowski

et al., 2012; Orlowski and Loose, 2012)

2.2 Experiments

Preliminary investigations were done to determine the

noise of the sensor systems. For this experiment a

dummy (see ﬁgure 2) was used. In contrast to a hu-

man test person moving slightly during standing, a

skeleton always rests. The KINECT is able to detect

correctly the skeleton and to capture the stands. For

measuring the SHIMMER sensor noise, the calibrated

sensor is placed on a table. The results of these anal-

yses are describe in the following subsection.

To assess the determination of gait parameters us-

ing KINECT and SHIMMER sensors, two steps have

been carried out: ﬁrst the gait of a small group of vol-

unteers is measured for an initial assessment of the

setup and algorithms, thereafter the data of a larger

group of healthy subjects is evaluated.

Within the small group the step sequence of six

healthy subjects (3 M, 3 F, mean age: 29.8) is si-

multaneously captured with both systems. Due to the

restricted capturing area of the KINECT the step se-

quence consists of one to three steps each leg, vary-

ing in step length. The KINECT is placed in front

of the walking line at a deﬁned height. The person

walks toward the KINECT until a distance of 80 cm is

reached. During the walking experiment the proband

Figure 2: The Microsoft KINECT and two SHIMMER sen-

sors (left). The unmoved dummy used to determine the

KINECT sensor noise (right). Below: Experimental setup

for noise evaluation with marked dummy positions.

wears two SHIMMER 9-DoF-sensors which are ﬁxed

above the ankle on each leg. A start and stop synchro-

nization is not implemented; the systems are started

and stopped manually. After the initial assessment

the gait of 26 healthy students (20 M, 6 F, mean age:

24.1)) was captured. There were no special in- or ex-

clusion criteria for the choice of the volunteers.

2.3 Noise Evaluation

2.3.1 KINECT

In contrast to the publication of (Khoshelham and El-

berink, 2012), the sensor noise of the KINECT is

reviewed by measuring the skeletal data of the non-

moving dummy. To verify the hypothesis that the

noise is dependent on the distance, the skeleton was

recorded at ﬁve positions (see ﬁgure 2). To elimi-

nate random noise of a single KINECT, the setup was

repeated with two other KINECT systems. Three 30-

second-datasets at each given distance with a frame

rate of 30 Hz were recorded.

The noise signal of selected joints and their fre-

quency spectrums were examined. Most of the fre-

quencies of the noise signal are below 5 Hz with a

more or less normal distribution. The reason for the

slightly higher proportion of low frequencies might

be a drift. The mean, standard deviation (std), mini-

mum and maximum are determined as statistical val-

ues. The mean of the standard deviation of the hip is

±1.3 mm, of the shoulder ±2.7 mm and of both ankles

±3.4 mm.

Figure 3 shows the box plots of the nine record-

ings. The samples of the selected joints of the nine

recordings are each summed up to one vector consist-

ing of about 8100 samples. The median value is close

to zero because the data is shifted by its mean. Obvi-

ously, the deviations of the left and right side are sim-

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Figure 3: Box plots: noise of both ankle, hip and shoulder

summarizing the measurements of 3 KINECT (dist 3 m).

Table 1: Mean of the standard deviation of the noise and its

relative value depending on the distance.

Dist

[mm]

left

Ankle

(relErr)

right

Ankle

(relErr)

Hip

(relErr)

Shoulder

(relErr)

2300 1,5 mm

(0,07 %)

2,3 mm

(0,1 %)

0,9 mm

(0,04 %)

1,4 mm

(0,06 %)

3000 3,1 mm

(0,10 %)

3,4 mm

(0,11 %)

1,3 mm

(0,04 %)

2,7 mm

(0,09 %)

4000 6,1 mm

(0,15 %)

10,5 mm

(0,26 %)

3,4 mm

(0,09 %)

5,0 mm

(0,12 %)

ilar with the exception of more outliers on the right

side. The center of the shoulder has larger deviations

than the hip center. The deviations including the out-

liers of the hip and shoulder are much smaller than

those of both ankles.

Further evaluations compare the dependency of

the noise on different distances. Even when using the

smoothing parameter of the KINECT SDK for less

skeletal jitter (see Microsoft KINECT SDK API Ref-

erence or (Fernandez, 2011)), the noise grows with

the distance. Table 1 contains the calculated mean of

the standard deviation of the recordings at different

distances as well as the relative errors based on the

respective distances.

2.3.2 SHIMMER

The noise of four SHIMMER sensors was deter-

mined. After sensor calibration a 25-second record-

ing was performed. The signals of the gyrometer were

transformed to the frequency domain. Examining the

recorded noise of the gyrometer, the noise could be

classiﬁed rather nonspeciﬁc. It is more or less nor-

mal distributed and has a few distinct peaks. Similar

Figure 4: Box plots of the noise of the gyrometer.

to the KINECT noise evaluation, statistical values are

determined for all noise signals. Figure 4 represents

a summary of the statistical evaluation of the noise

signals. All angular velocity signals in x, y and z are

summed up to one vector each, and box plots were

created. The plot contains the box plots of the angu-

lar velocity. Obviously, there are no signiﬁcant differ-

ences between the three axes. The standard deviation

of the angular velocity is ±0.3

◦

/s .

3 METHODS

3.1 Normed Gait Cycle

Using the mean joint angles provided by Perry et

al., different data, such as angular velocity, horizon-

tal movement and velocity, are derived from the mean

joint angles of the left leg (see ﬁgure 5) using the limb

length of a subject (ankle-hip: 88 cm, ankle-knee: 41

cm, knee-hip: 47 cm, foot: 28 cm) . Since the speed

of a normal gait is deﬁned at 1.3-1.6 m/s (Oeberg and

Oeberg, 1993), we choose an uniform forward motion

of the hip at 1.4 m/s (see ﬁgure 6 (below)). The values

of derived velocity are negative because the distance

to the sensor is reduced during the measurement.

Figure 5: Mean joint angles of the normed gait cycle.

All diagrams in ﬁgure 6 as well as the walking cy-

cle in ﬁgure 1 show that the stance phase of the left

leg is approximately between 5 and 60 % of the gait

cylce. The swing phase starts directly after the stance

phase and is not yet completed at 100 %. The angu-

lar and the horizontal velocity are around zero dur-

ing stance since the distance to the sensor does not

change. The middle diagram, depicting the horizon-

tal movement of the foot, ankle, knee and hip, shows

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159

Figure 6: Calculated angular velocity (above) of the shank

and foot, horizontal velocity (middle) of foot, ankle and

knee and horizontal movement (below)

a nearly constant distance around 3 m from 5 to 50-60

% of the gait cycle. After analyzing the normed gait

and the derived values, conclusions for the determi-

nation of gait parameters can be drawn. It is possible

to convey characteristics of the gait which can be ex-

tracted automatically from the measured data of the

KINECT and SHIMMER gyroscope.

A gait cycle consists of a stance and a swing phase

and can be characterized by its length and duration.

From the angular velocity of the shank, the begin-

ning (IC) and end (TC) of the stance phase as well

as the middle of swing phase (midswing) can be de-

termined. In contrast to the deﬁnition of midswing

as a phase (Perry, 2010), midswing is used as a char-

acteristic point of the swing phase. At the beginning

and end of the stance phase local minima occur in the

angular velocity. The midswing is characterised by a

local maxima (see ﬁgure 7).

The beginning and end of the swing phase can be

detected in the velocity of the foot motion using a

threshold. During swing phase this threshold is ex-

ceeed. The beginning and end of the stance result in

the preceding swing phase parameters. In ﬁgure 7 the

lime, solid curve represents the velocity of the foot,

the dashed line the used threshold. The red circles

show where the threshold is exceeded.

Figure 7: Velocity and angular velocity with marked param-

eters and used threshold.

3.2 Determination of Gait Parameters

3.2.1 SHIMMER

The swing and stance phases are determined by iden-

tifying the characteristic points from the data of the

gyroscope. Figure 8 shows signals captured during

normal walking. The angular velocity captured with

SHIMMER is similar to those of the normed gait cy-

cle (compare ﬁgure 7 and ﬁgure 8).

The IC, TC and midswing points are determined

using an adapted algorithm analogical to that pro-

posed by Greene et al. (Greene, 2010). First dis-

turbances are minimized by a low-pass ﬁlter (5th

order Butterworth ﬁlter, corner frequency: 5 Hz).

Then the correctness of the polarity of the signal has

to be checked using the formula for the skewness

(mean(x) − median(x)). If the skew value is nega-

tive and the absolute value of the minimum is greater

than the absolute value of the maximum, the signal

is mirrored at the x axis. An additional criterion

(abs(min(x)) > abs(max(x))) is used. The combi-

nation of both criteria can change the polarity of the

signal. The local maxima and minima are searched

within the signal. Each local maximum stands for the

midswing of a swing phase. The preceding local min-

imum of the midswing point is the TC, the succeeding

local minimum represents the IC.

Figure 8: Angular velocity of the left/right lower leg with

marked features: IC-green, TC-yellow, midswing-black.

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3.2.2 KINECT

The start and end of a swing phase, which correspond

to the terms inital and terminal contact (IC/TC), are

calculated from the ankles‘ forward motion.

The captured data are ﬁltered by a mean ﬁlter of

length ﬁve which is applied through convolution. The

smoothed signal of the forward motion of the left

(blue-dotted) and right (red) ankle is displayed above

in ﬁgure 9. As mentioned before, to determine the

start and end points, the ﬁrst discrete derivative is cal-

culated. The result of the derivation is shown in the

lower plot in ﬁgure 9. At ﬁrst the local minima are

detected to determine the number of steps for each

leg. That information is necessary for the detection

of start and end points of each swing. We assume

that the local minima in the ﬁrst derivative represent

the midswing points. An experimentally determined

threshold is each time adapted to the minimum of the

two global minima of the derivative of the right and

left signal (th = −0.499 ∗ max) to ﬁnd all start and

end points of the swing phases.

Figure 9: Forward motion of the both ankles of a step se-

quences of one person: distance and velocity (below)

The upper plot of ﬁgure 9 contains the detected

start and end points of the swing phase. The constant

parts represent the stances, the small decreasing parts

the swing phases. The foot as well as the ankle move

only slightly during the stance phase while the move-

ment is clearly visible during the swing phases. The

step sequence begins with the right leg after an initial

recovery (about three seconds). Three right steps and

two left steps have be made during the short walking.

4 RESULTS

4.1 Introductory Remarks

As mentioned before the experiments were done in

two steps. First an initial assessment was performed

with a small group of volunteers to check the systems

and the used algorithms. Then, the gait of a larger

group was captured under the same conditions.

The captured data were visually surveyed to iden-

tify data with measuring failures such as storage er-

rors, early breaks of capturing and data with a dis-

turbed initial recovery. Recordings with such failures

were excluded from the assessment and evaluation.

After removing erroneous data, 55 datasets are in-

vestigated and the characteristic points (IC, TC and

midswing) can be detected.

4.2 Comparison and Statistical

Evaluation

The IC, TC and midswing points were detected us-

ing the described algorithms. The duration of the

swing and stance phase as well as the distance be-

tween midswing points were calculated. The devia-

tions between both systems were measured determin-

ing the absolute error in seconds. The existing devi-

ations are due to the preprocessing steps, algorithms

and the accuracy of the systems.

Figure 10: Box plots of the absolute errors between gait

parameters detected from SHIMMER and KINECT data.

Left: 1-Stance left, 2-Stance right, 3-Swing left, 4-Swing

right, Right: 1-Midswing left, 2-Midswing right

Figure 10 provides information about the devia-

tions of both systems/algorithms during measurement

and shows the absolute error measured for the param-

eters: stance left (1) and right (2), swing left (3) and

right (4) as well as midswing left (1) and right (2).

The absolute error constitutes the difference between

the parameters determined from the data of both sys-

tems. The absolute error of the swings is greater

than those of the stances. The large deviations in the

swing phase result from the fact that the pull of the

foot to the end position of the recording is most of

the time not correctly covered by the KINECT (see

ﬁgure 9). Contrary to the SHIMMER, the KINECT

algorithm does not recognize the last swing due to

the used threshold. A reduction of the threshold to

35% of the global minimum leads to the recognition

of the swing, but the duration of that swing differs sig-

niﬁcantly from that determined by SHIMMER. That

could be reasoned by the more difﬁcult recognition

EvaluationofKINECTandSHIMMERSensorsforDetectionofGaitParameters

161

of the last swing. The algorithm has to be optimized

regarding that problem.

The mean absolute error at stance phases is 0.084

s (±0.07 s) (left) and 0.078 s (±0.07 s) (right) as well

as at swing phases are -0.053 s (±0.13 s) (left) and

-0.053 s (±0.12 s) (right). The distance between the

midswing points was also considered. As expected,

this gait parameter is less prone. This is reﬂected in

the right plot. The mean absolute error for the dis-

tance of midswing points is -0.038 s (±0.04 s) (left)

and -0.042 s (±0.06 s) (right).

5 DISCUSSION

The evaluation of noise provides no clear results that

the signals captured with KINECT and SHIMMER

include a speciﬁc type of noise. The noise of the

KINECT depends on the subjects distance to the

recording unit. The greater the distance, the stronger

the inﬂuence of noise. Regarding other inﬂuences,

the environment, in which the measurements are con-

ducted, has to be investigated in further experiments.

The evaluation of derived values of the mean joint

angles have shown that the signals measured with

KINECT and SHIMMER are usable for the detec-

tion of gait parameters. In general the detected points

are correctly determined. The detected IC, TC and

midswing points represent characteristic points of the

swing and stance. They can be used to characterize

the gait and calculate the length and duration of each

swing or stance. The quality of the detection depends

on the signals’ frame rate and the noise.

Statistical values are used to assess the derived

gait parameters. The absolute errors were determined

by calculating the absolute difference of the gait pa-

rameters determined from the KINECT and SHIM-

MER data. The duration of the swing and stance

phase is not identical but similar with small devia-

tions. The deviations of the swing phase is greater

than those of the stance phases due to the detection

problem of the last swing. The differences between

the distance of the detected midswings are much

smaller than those of the swing and stance phase. The

different frame rates (KINECT: 30 Hz, SHIMMER

51.2 Hz) can be a reason for the deviations because

the temporal resolution is different and the accuracy

is not the same.

6 CONCLUSIONS

This paper presents the inﬂuence of noise of both sen-

sor systems. A further component of the paper is

the evalution of the normed gait cycle given by Perry

(Perry, 2010) and Murray (Murray and Kory, 1964).

Based on the evaluation, the correctness of the devel-

oped algorithm was veriﬁed. They have been used to

determine the gait parameters duration of swing and

stance phases and distance between midswings. The

investigation was carried out in two steps. At ﬁrst

the gait of a small group of volunteers was measured

and investigated to check for necessary adaptions on

the algorithms. Consequently, the algorithms were

adapted and additional functions were developed to

ensure a semi-automatic evaluation of amount of data.

Then, in a second step, the gait of 26 healthy students

was recorded and analyzed. The results of the com-

parison are shown.

In further experiments, the comparison with a gold

standard as well as with an established mobile sensor

systems, such as xsens

, has to be applied to assess

the quality and correctness of the data. Moreover, the

detection problem of the last swing has to be solved.

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