05 Picerno 2008 Joint Kinematcs Estimate Using Wearable Inertial and Magnetic Sensing Modules

7/26/2019 05 Picerno 2008 Joint Kinematcs Estimate Using Wearable Inertial and Magnetic Sensing Modules

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Joint kinematics estimate using wearable inertial and magneticsensing modules

Pietro Picerno a,*, Andrea Cereatti a,b, Aurelio Cappozzo a

aDepartment of Human Movement and Sport Sciences Istituto Universitario di Scienze Motorie, Piazza Lauro de Bosis 6, 00194 Roma, Italy

bCentro di Cura e Riabilitazione Santa Maria Bambina, Oristano, Italy

Received 15 October 2007; received in revised form 10 March 2008; accepted 7 April 2008

Abstract

Background and aims: In many applications, it is essential that the evaluation of a given motor task is not affected by the restrictions of the

laboratory environment. To accomplish this requirement, miniature triaxial inertial and magnetic sensors can be used. This paper describes an

anatomical calibration technique for wearable inertial and magnetic sensing modules based on the direct measure of the direction of

anatomical axes using palpable anatomical landmarks. An anatomical frame definition for the estimate of joint angular kinematics of the

lower limb is also proposed.

Methods: The performance of the methodology was evaluated in an upright posture and a walking trial of a single able-bodied subject. The

repeatability was assessed with six examiners performing the anatomical calibration, while its consistency was evaluated by comparing the

results with those obtained using stereophotogrammetry.

Results: Results relative to the up-right posture trial revealed an intra- and inter-examiner variability which is minimal in correspondence to

the flex-extension angles (0.22.98) and maximal to the internalexternal rotation (1.67.38). For the level walking, the root mean squared

error between the kinematics estimated with the two measurement techniques varied from 2.5% to 4.8% of the range of motion for the flex-

extension, whereas it ranged from 13.1% to 41.8% in correspondence of the internalexternal rotation.

Conclusion: The proposed methodology allowed for the estimate of lower limb joint angular kinematics in a repeatable and consistent

manner, enabling inertial and magnetic sensing based systems to be used especially for outdoor human movement analysis applications.

# 2008 Elsevier B.V. All rights reserved.

Keywords: Movement analysis; Anatomical calibration; Anatomical frame definition; Joint angular kinematics; Wearable devices; Inertial and magnetic

sensing

1. Introduction

The description of joint kinematics during the execution

of a physical exercise may be accomplished by tracking the

trajectory of active or passive point markers located on theskin of the subject using optoelectronic stereophotogram-

metry (SP). The reconstructed coordinates of these markers

in an arbitrarily defined global frame (GF) allow for the

determination of local frames (technical frame: TF)

associated with each bone of interest and, therefore, the

description of the relevant instantaneous pose (position and

orientation). The use of this instrumentation is constrained

by the fact that the measurement volume is limited and that

the relevant equipment is financially demanding. A possible

alternative to this approach is the use of motion analysis

systems based on electromagnetic [1,2] or ultrasound [3]technologies. But again, the ultrasound or electromagnetic

fields generated by these systems are limited in volume.

More recently, the availability of miniature solid-state

inertial and magnetic sensors has opened up a new

perspective [4,5]. A three-dimensional (3D) linear accel-

erometer, a 3D angular rate sensor and a 3D magnetometer

have been assembled to produce a sensor module, referred to

as magnetic field angular rate and gravity sensor (MARG

[6]). The use of specific sensor fusion algorithms can

www.elsevier.com/locate/gaitpost

Available online at www.sciencedirect.com

Gait & Posture 28 (2008) 588595

* Corresponding author. Tel.: +39 06 36733506; fax: +39 06 36733517.

E-mail address: [email protected](P. Picerno).

0966-6362/$ see front matter # 2008 Elsevier B.V. All rights reserved.

doi:10.1016/j.gaitpost.2008.04.003
mailto:[email protected]://dx.doi.org/10.1016/j.gaitpost.2008.04.003http://dx.doi.org/10.1016/j.gaitpost.2008.04.003mailto:[email protected]


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provide an estimate of the orientation of a TF embedded in

the module housing relative to a global Earth-fixed GF

defined by the direction of gravity and the local magnetic

North vector[7,8]. This can be accomplished with no spatial

limitations and at a relatively low cost. The estimate of

the global position of the TF is not yet reliable since the

double integration of the linear acceleration, which yieldsdisplacement data, can be accurately calculated only for

types of motion where the velocity of the MARG sensor

returns cyclically to zero[9]. In addition, a further limitation

may be encountered. The presence of metallic objects in the

vicinity of a MARG sensor can change the direction of the

local magnetic North vector and therefore the orientation of

the GF thereby introducing an artefact. The latter

disturbances that may affect the sensors involved may be

compensated by the above-mentioned sensor fusion algo-

rithms for limited intervals of time [7]. These problems

may, in turn, limit the maximal duration of the movement

tracking.

For an effective and repeatable representation of angular

kinematics, an anatomical frame (AF) must be associated

with each bone involved in the analysis [10,11].

Since this frame is normally different from the relevant

TF, a transformation matrix from the latter frame, provided

by the motion-tracking equipment, to the AF must be

determined (movement-morphology registration). Relevant

information is collected through a procedure referred to as

anatomical calibration.

In regards to the above stated purpose, superficial

anatomical landmarks (ALs) are identified by manual

palpation and their location relative to the TF are determined

using an ad hoc experiment [12]. The location of internalALs may be determined either as a function of the location

of superficial ALs or, when applicable, as the mean centre of

rotation of a selected movement between two adjacent bony

segments[13]. A functional approach may also be used for

the identification of anatomical axes (AAs). A bone may be

made to rotate, relative to its adjacent counterpart, in an

anatomical plane. The relevant mean axis of rotation is taken

as the AA orthogonal to that plane [14]. The utilization of the

functional approach to determine both ALs or AAs may be

open to dispute since, for its reliability, the mechanical

behaviour of the joint involved must be very close to that of a

spherical or a cylindrical hinge, respectively, which is rarely

the case. In addition, the joint involved must be capable of

sufficiently wide angular excursions[15].

Different geometric rules have been proposed that define

AFs using AL positions and AA orientations [12,14,16,17]

determined as illustrated above. Although it has been

demonstrated that joint kinematics numerical representation

depends highly on AFs definition [18] and, consequently, the

movement analysis community considers it a priority, a final

consensus in this respect has not yet been reached.

Conversely, given the AFs of two adjacent bones, it is

now common practice to estimate joint kinematics using the

so-called Cardan convention[19].

Differently from motion analysis system based on stereo-

photogrammetry, electromagnetic or ultrasound technolo-

gies, using MARG sensors, the position of ALs cannot be

determined, while the orientation of axes is provided. The

axes of the AF can be assumed coinciding with the axes of

the TF by manually aligning the sensor module with the

anatomical axes. This approach is evidently unreliablesince it only approximates anatomical planes and is not

repeatable. A second solution, suggested by several authors

[20,21], is based on a functional approach: one of the axes of

the AF can be assumed as coinciding with the direction of

the 3D angular velocity vector measured by the MARG

sensor attached to the body segment while the segment is

rotated about a joints functional axis. A second axis of the

AF can be defined using the direction of gravity measured by

the MARG sensor module during resting posture. This

approach has the advantage of being quick to perform (so it

is ideal for virtual reality and entertainment applications),

but undergoes some limitations: movements planes and

postures are subjective and, thus, the determination of the

AFs may be not repeatable. Moreover, joint impairments

may produce axes that are not consistently related to bone

anatomy.

The purpose of this study is to present a novel anatomical

calibration procedure to be used in association with a

MARG based motion-tracking system. This procedure is

based on the identification of superficial ALs. The proposed

methodology can be used to compute 3D joint angular

kinematics by means of MARG sensors in a reliable manner.

The repeatability of the method and its consistency with

joint kinematics obtained using SP are assessed with

reference to the lower limb during posture and level walking.

2. Materials and methods

The anatomical calibration is carried out while the subject

assumes a suitable stationary posture with the body segments of

interest equipped with a movement tracking MARG sensor and

using a calibration device. The latter is equipped with two mobile

pointers that can be made to point two-selected ALs. The body of

the device carries a MARG sensor, an active axis of which is

aligned with the line joining the two pointers and, thus, two ALs. In

this way the orientation of this line relative to the GF can be

measured. For each bony segment, the orientation of a minimum of

two non-parallel lines must be determined in order to construct the

orthogonal axes of the AF through a given geometric rule. Cali-

bration devices of different geometry may be required in order to fit

bones with different shapes (Fig. 1).

During the anatomical calibration procedure, the MARG sensor

module attached to the body segment provides the orientation

matrix (gRt(0)) of the TF while the MARG sensor module placed

on the calibration device measures the orientation of at least two

unit vectors (guk;k= 1, 2) of lines passing through couples of ALs,

all relative to the GF. The latter vectors are, thereafter, represented

in the TF through the rigid transformation

tukg RT

t0 g uk (1)

P. Picerno et al. / Gait & Posture 28 (2008) 588595 589


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and used to calculate the orientation matrix of the AF in the TF

(tRa(0)) through a geometric rule.

During the execution of a physical exercise, in each i-th sampled

instant of time, the motion-tracking MARG sensor provides the

matrix gRt(i), i= 1, . . ., N and the global orientation of the AF,

necessary to estimate joint kinematics. It is calculated as

gRai g Rti

tRa0; i 1;. . . ;N (2)

The MARG sensor based anatomical calibration procedure

described above can only rely on external palpable ALs. Thus,

the most commonly adopted AF definitions for femur, tibia and foot

cannot be used [12,14,16,17]. The ALs and the geometric rules

used in this study that define the AFs for pelvis and lower limb bony

segments are illustrated in Fig. 2. It should be emphasized that other

definitions are difficult to realize.

During the experiments, both the MARG sensor outputs and the

stereophotogrammetric data (9 VICON Mx cameras, Oxford

Metrics, UK), were sampled at 120 samples per second. To mini-

mize problems related to ferro-magnetic disturbances, experiments

were conducted in a controlled magnetic field environment. A

single able-bodied adult subject was involved in the experiment.

Four MARG sensors (MTx, Xsens Motion Technologies,

Enschede, The Netherlands) were firmly attached to the volunteers

sacrum, the latero-distal thigh, the medial facet of the tibia and,

laterally, to the tarsal bones. Each module was equipped with a

three retro-reflective point marker cluster. The two markers on the

rod were 10 cm apart from each other and approximately 7 cm from

the marker placed on the case (Fig. 1c and d).

The AL locations, listed inFig. 2, were identified by an expert

through manual palpation and marked with a felt pen. Six exam-

iners performed the MARG sensors anatomical calibration proce-

dure using the above-described calibration devices. One of the

examiners performed the anatomical calibration procedure six

times. Each calibration session took approximately 5 min.

Thereafter, retro-reflective markers were placed on the ALs

using the relevant markings.

Since the AFs definitions adopted in this study differed from

those commonly adopted in gait analysis, the kinematics estimate

was compared with that obtained using a selected commonly

adopted lower limb AFs definition [12]. For this purpose, extra

markers were located on the left posterior iliac spine, the tibial

tuberosity and the second metatarsal head. The position of these

markers in the TFs constructed using the relevant point marker

clusters were determined through SP. After this calibration was

carried out, the markers that indicated the ALs were removed. The

P. Picerno et al. / Gait & Posture 28 (2008) 588595590

Fig. 1. Calibration devices adopted to identify the lines connecting ALs (a and b). Calibration of the directions identified by GT and LE (c), and of the direction

identified by ME and LE with respect to the thigh TFMARG (d).


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hip joint centre in the pelvis point marker cluster was also deter-

mined using the functional approach described in [13].

An experimental trial was carried out while the volunteer

assumed an upright posture. A second experimental trial was

performed during level walking at a self-selected speed. During

both trials the global orientation of the MARG sensor and the

global position of the point marker clusters were tracked simulta-

neously.

The joint angular kinematics of hip, knee and ankle, during

upright posture and during level walking, were estimated using

both SP and MARG sensor data. In both cases the Cardan angular

convention and the AF definitions reported inFig. 2were used.

Data from SP were also used to estimate the joint angular

kinematics according to commonly adopted AF definitions[12].

2.1. Data analysis

The intra- and inter-examiner repeatability of the MARG sensor

anatomical calibration procedure was evaluated in terms of root

mean square deviation (RMSD) from the mean of the joint angles

estimated during the up-right posture and generated by the six

relevant calibrations.

The consistency between the two experimental methods was

assessed by comparing, for each joint, the angles obtained from the

data provided by the MARG sensors, using the anatomical calibra-

tions performed by the six examiners, and the angle provided by the

SP system. To this purpose, the absolute value of the differences

between the former six angles and the latter angle was calculated

and the relevant descriptive statistics (mean and standard deviation)

determined. This was done using the data collected during the

upright posture.

With reference to the level walking trial, consistency was

evaluated both in terms of offset and waveform dissimilarity among

the joint angles time histories. The offset was calculated as the

mean absolute difference (MAE) between the arithmetic mean of

each of the curves obtained with the MARG sensors and that

obtained with SP. To analyze the waveform dissimilarity, curves

obtained with the MARG sensors and SP were aligned with respect

to their relative mean value. From the aligned curves, the root mean

square error (RMSE), averaging over one gait cycle, and the RMSE

expressed as a percentage of the nominal joint rotational amplitude

(RMSE%) were derived.

The comparison between the AF definitions adopted in this

study and that proposed by[12]was carried out by calculating the


Fig. 2. Right lower limb anatomical frame definitions. ukrepresents the oriented axes identified by pointing two ALs by means of the calibration devices. The

origins of the AFs are arbitrarily placed.


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correlation coefficient (R) and difference in the range of motion

(DRoM) between the joint angular kinematics estimated using the

two different AF definitions.

3. Results

Precision and consistency in estimating joint angles

during the up-right posture trial using the MARG sensors are

reported inTables 1 and 2. On average, the intra-examiner

repeatability was higher than inter-examiner repeatability. In

particular, the internalexternal rotation (In/Ex rot) varia-

bility was the highest across all joints. The knee In/Ex rot

exhibited the highest intra- and inter-examiners RMSD, 4.98

and 7.38, respectively.

For all joints, the flexion-extension angle (Fl/Ex)

presented the smallest AE, with the mean values ranging

from 1.38to 1.88and standard deviation values below 0.98.

The largest AE was observed in correspondence with the In/

Ex rot and, in particular, the highest mean value of AE, equal

to 8.38, was found for the ankle, whereas the largest S.D.

value of AE, equal to 6.18, was observed in correspondence

with the hip.

For all joints, the MAE affecting the angular kinematics

estimated by the MARG sensors during one gait cycle was

minimal for the Fl/Ex angle and maximal for the In/Ex rot.

For Fl/Ex, it ranged from 1.28 at the ankle to 38at the hip.

For the Ab/Ad, it ranged from 3.68at the hip to 5.58at the

ankle and finally for the In/Ex rot, it varied from 4.58at the

hip to 21.78at the ankle. The RMSE values varied from 0.88

to 3.68and were minimal for the Fl/Ex and maximal for the

In/Ex rot, across all joints and angles. The Fl/Ex curves

estimated with the MARG sensors were very similar to those

estimated with SP (Fig. 3) and the RMSE were always lower

than 4.8% of the nominal RoM (Table 3). The largest

RMSE% value was the knee In/ex rot and was equal to

41.8% of the nominal RoM.

The joint kinematics obtained using the AF definitions

proposed in this study were highly correlated with those

estimated using the AF definitions suggested by [12].

The correlation coefficient for the Fl/Ex was equal to 1 for

all joints whereas the DRoM was less than 0.58. The

lowest R was the knee In/Ex rot, and it was equal to 0.942


Table 1

Intra- and inter-examiners repeatability (RMSD) of joint angles estimated

with MARG sensors during an upright posture

Joint Fl/Ex (8) Ab/Ad (8) In/Ex rot (8)

Intra Inter Intra Inter Intra Inter

Hip 2.9 1.8 1.2 2.4 3.5 6.6

Knee 0.2 2 1 1.1 4.9 7.3

Ankle 0.4 1.5 1 1.5 1.6 1.6

Table 2

Mean(standarddeviation) of the differences in absolutevalue (AE) between

joint angles estimated with SP and the MARG sensors during an upright

posture

Joint Fl/Ex (8) Ab/Ad (8) In/Ex rot (8)

Hip 1.8 (0.7) 3 (2.2) 6.7 (6.1)

Knee 1.9 (0.7) 4.6 (1.1) 6.3 (3.9)

Ankle 1.3 (0.9) 5.7 (1.5) 8.3 (1.6)

Fig. 3. Ensemble plots of hip, knee and ankle joint angle waveforms. The solid black lines represent the joint kinematics obtained with SP and the dashed grey

lines representthe joint kinematics obtained with theMARGsensorsas generatedby theanatomical calibrations performedby thesix examiners. The curves are

temporally normalized from initial right foot contact (0%) until subsequent foot contact (100%).


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whereas the largest DRoM, equal to 28, was the knee Ab/

Ad (Fig. 4).

4. Discussion and conclusion

The proposed methodology for the anatomical calibration

using MARG sensors led to a consistent and repeatable

estimation of the lower limb AFs. The joint angles computed

during an upright posture were used to evaluate the

differences in the orientation of the AFs determined using

the MARG and SP and the repeatability of the calibration

procedure performed with the MARG sensors. The estimate

of the Flex/Ex angles was the most reliable for all joints

both in terms of repeatability (RMSD < 2.98) and in

terms of consistency with the SP data (AE < 1.98 (0.78)).

The estimates of the Ab/Ad angles were repeatable

(RMSD< 2.48) but were associated to differences with

respect to the SP data up to 5.78 (1.58). The less reliable

angles estimate was for the In/Ex rot which exhibited boththe lowest repeatability (RMSD< 7.38) and consistency.

To exclude errors related to the uncertainties associated

with ALs identifications, their positions were identified by

an expert and then marked with a felt pen. Thanks to this,

two objectives were accomplished: all operators performed

the anatomical calibration exercise by pointing at the same

previously marked ALs; the AFs, determined using the

MARG sensors and SP, were defined using the same points,

thus allowing comparison between the results. Unfortu-

nately, SP reconstructs the position of the geometrical centre

of the marker placed on the AL, which is different from the

palpable position of the AL on the skin. Hence, the direction

of the line connecting two ALs measured with a MARG

sensors (hosted in its specific calibration device) and with SP

is unavoidably different. This difference increases when the

distance between two ALs decreases (i.e. for epicondyles

and malleoli), resulting in an AF determined differently

from one anothers measurement system even if identical

ALs and axis definitions are used. This circumstance may

explain the greater errors found in correspondence with the

Ab/Ad and In/Ex rot.

It is important to note that the cluster of markers was

mounted on the MARG sensor case, and therefore, during the

walking trial, TFs obtained with MARG sensors and SP were

affected by the same soft tissue artefact and were related by a


Table 3

Comparison of joint angular kinematics estimate relative to one gait cycle

obtained with the MARG sensors and SP

Joint Angle MAE (8) RMSE (8) RMSE %

Hip Fl/Ex 3 0.8 2.5

Ab/Ad 3.6 1.5 13.3

In/Ex rot 4.5 1.8 13.1

Knee Fl/Ex 2.4 1.9 3

Ab/Ad 4.8 2.8 21.1

In/Ex rot 9.4 3.6 41.8

Ankle Fl/Ex 1.2 1.2 4.8

Ab/Ad 5.5 2.2 11.7

In/Ex rot 21.7 3.5 32.5

The offset was evaluated in terms of mean absolute error (MAE). The

waveform distortion was evaluated in terms of root mean square error

(RMSE) between joint angular kinematics curves aligned with respect to

their relative mean values, estimated with SP and the MARG sensors.

RMSE % represented the RMSE expressed as a percentage of the RoM

estimated with SP.

Fig. 4. Ensemble plots of hip, knee and ankle joint angle waveforms. The solid lines represent the joint kinematics obtained using a commonly adopted AF

definition[12], while the dashed lines represent the joint kinematics obtained from the AF definition suggested in this study. The movement data have been

recorded using SP.


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time-invariant rigid transformation. The performance of the

methodology was not evaluated with respect to the actual

bone movement, which was unknown, but with respect to the

movement obtained by SP, considered as the gold standard.

The joint angular kinematics obtained with the MARG

sensors was comparable to that obtained using SP. The Fl/Ex

angles estimate showed the highest accuracy (RMSE < 28,

MAE< 38). Thehighesterrors were found in correspondence

with the In/Ex rot angles. In general, the kinematic patterns

were quite well reproduced (RMSE< 3.68) whereas the

offset from the curves obtained with SP were high and in

particular for the ankle joint (MAE = 21.78).

The AF definitions chosen in the present work differ from

those commonly adopted in gait analysis. In fact, the AF

definitions were conditioned by the fact that only superficial

ALs can be pointed using the calibration device, which

provides axis orientation rather than position of points.

However, the estimated joint kinematics was still consistent

(0.942< R < 1, 0.58 < DRoM< 28) with the conventional

description of the joint movement.

In general, gait analysis requires gathering information

on the spatio-temporal parameters and the joint kinematics

and kinetics. The aim of this study was limited to the

improvement of the quality of joint angular kinematics

estimate with MARG sensors. Different methods for the

determination of spatio-temporal parameters from accel-

erations and angular velocities have been described[22]. To

the authors knowledge, only one study faced the problem of

joint kinetics estimation [23], analyzing solely ankle

moment and power. Future research should be aimed at

the development of a methodology, based on the use of

MARG sensors, which allows for ambulatory analysis of thelower limb kinetics.

A limitation in the use of this type of instrumentation is

that the presence of ferromagnetic disturbances can affect

the orientation of the MARG sensors. In fact, in a non-

homogeneous magnetic environment each MARG sensor

computes its orientation with respect to a different GF. This

results in an erroneous estimate of the relative movement

between MARG sensors. It is, hence, crucial to operate in

controlled conditions during both the calibration procedure

and the acquisition trial.

In conclusion, the proposed methodology, based on the

direct identification of ALs, enables MARG sensor based

systems to be used as a valid alternative to SP for three

dimensional joint angular kinematics estimate, especially in

those applications where the evaluation of the motor task

should be carried out in a natural environment and for a

prolonged interval.

Acknowledgements

The authors would like to thank Andrea Mele for his

technical support. This study was funded by the Italian

Ministry of Health and the ISPESL.

Conflict of interest

None.

References

[1] Kobayashi K, Gransberg L, Knutsson E, Nolen P. A new system for

three-dimensional gait recording using electromagnetic tracking. Gait

Posture 1997;6:6375.

[2] Mills P, Morrison S, Lloyda DG, Barrett RS. Repeatability of 3D gait

kinematics obtained from an electromagnetic tracking system during

treadmill locomotion. J Biomech 2007;40:150411.

[3] Kiss RM, Kocsis L, Knoll Z. Joint kinematics and spatial-temporal

parameters of gait measured by an ultrasound-based system. Med Eng

Phys 2004;26:61120.

[4] Kemp B, Janssen AJ, van derKampB. Body positioncan be monitored

in 3D using miniature accelerometers and earth-magnetic field sen-

sors. Electroencephalogr Clin Neurophysiol 1998;109:4848.

[5] Zhu R, Zhou Z. A real-time articulated human motion tracking using

tri-axis inertial/magnetic sensors package. IEEE Trans Biomed Eng2004;12:295302.

[6] Bachmann EB. Inertial and magnetic tracking of limb segment

orientation for inserting humans into synthetic environments. Ph.D.

Thesis. Naval Postgraduate School, Monterey, CA; 2000.

[7] Roetenberg D, Luinge HJ, Baten CT, Vltink PH. Compensation of

magnetic disturbances improves inertial and magnetic sensing of

human body segment orientation. IEEE Trans Neural Syst Rehabil

Eng 2005;13:395405.

[8] Sabatini AM. Quaternion-based strap-down integration method for

applications of inertial sensing to gait analysis. Med Biol Eng Comput

2005;43:94101.

[9] Veltink PH, Slycke P, Hemssems J, Buschman R, Bultstra G, Hermens

H. Threedimensional inertial sensing of footmovements for automatic

tuning of a two-channel implantable drop-foot stimulator. Med Eng

Phys 2003;25:218.[10] Cappozzo A. Gait analysis methodology. Hum Movement Sci

1984;3:2750.

[11] Lafortune MA, Cavanagh PR, Sommer HJ, Kalenak A. Three-dimen-

sional kinematics of the human knee during walking. J Biomech

1992;25:34757.

[12] Cappozzo A, Catani F, Della Croce U, Leardini A. Position and

orientation of bones during movement: anatomical frame definition

and determination. Clin Biomech 1995;10:1718.

[13] Camomilla V, Cereatti A, Vannozzi G, Cappozzo A. An optimized

protocol for hip joint centre determination using the functional

method. J Biomech 2006;39:1096106.

[14] Frigo C, Rabuffetti M, Kerrigan DC, Deming LC, Pedotti A. Func-

tionally oriented and clinically feasible quantitative gait analysis

method. Med Biol Eng Comput 1998;36:17985.

[15] Woltring HJ, Huiskes R, de Lange A, Veldpaus FE. Finite centroid andhelical axis estimation from noisy landmark measurements in the

study of human joint kinematics. J Biomech 1985;18:37989.

[16] Davis RBI, Ounpuu S, Tyburski D, Gage JR. A gait data collectionand

reduction technique. Hum Movement Sci 1991;10:57587.

[17] Wu G, Siegler S,AllardP, Kirtley C, LeardiniA, RosenbaumD, et al.ISB

recommendation on definitions of joint coordinate system of various

joints for the reporting of human joint motion. Part I. Ankle, hip, and

spine.International Society of Biomechanics. J Biomech 2002;35:5438.

[18] Della Croce U, Camomilla V, Leardini A, Cappozzo A. Femoral

anatomical frame: assessment of various definitions. Med Eng Phys

2003;25:42531.

[19] Grood ES, Suntay WJ. A joint coordinate system for the clinical

description of three-dimensional motions: application to the knee. J

Biomech Eng 1983;105:13644.



8/8

[20] Luinge HJ, Veltink PH, Baten CT. Ambulatory measurement of arm

orientation. J Biomech 2007;40:7885.

[21] ODonovan KJ, Kamnik R, OKeeffe DT, Lyons GM. An inertial and

magnetic sensor based technique for joint angle measurement. J

Biomech 2007;40:260411.

[22] Aminian K, Najafi B, Bula C, Leyvraz PF, Robert P. Spatio-temporal

parameters of gait measured by an ambulatory system using miniature

gyroscopes. J Biomech 2002;35:68999.

[23] Schepers HM, Koopman HF, Veltink PH. Ambulatory assessment of

ankle and foot dynamics. IEEE Trans Biomed Eng 2007;54:895902.


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05 Picerno 2008 Joint Kinematcs Estimate Using Wearable Inertial and Magnetic Sensing Modules