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Joint Angles And Segment Length Estimation Using Inertial Sensors

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Daniel Roetenberg at ZHAW Zurich University of Applied Sciences
Daniel Roetenberg
Linda Schipper
Linda Schipper
Linda Schipper
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Pietro Garofalo at TuringSense
Pietro Garofalo
  • TuringSense
Andrea G. Cutti at INAIL Istituto Nazionale per l'Assicurazione contro gli Infortuni sul Lavoro
Andrea G. Cutti
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This abstract describes a novel and accurate method for 3D joint angles estimation using inertial sensors. It does not use magnetometers or the local magnetic field to stabilize heading. Exact joint location are accurately estimated in-use, allowing for patient specific scaling and tracking of movement.
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Joint Angles And Segment Length Estimation
Using Inertial Sensors
Daniel Roetenberg 1, Linda Schipper 1, Pietro Garofalo 1,2, Andrea Cutti 3, Henk Luinge 1
1 Xsens Technologies, B.V. , Enschede, The Netherlands
2 DEIS, Department of Electronics, Computer Science and Systems, University of Bologna (BO), Italy
3 I.N.A.I.L. Prostheses Centre, Vigorso di Budrio (BO), Italy
daniel.roetenberg@xsens.com
Abstract – This abstract describes a novel and accurate method for 3D joint angles estimation using inertial
sensors. It does not use magnetometers or the local magnetic field to stabilize heading. Exact joint location are
accurately estimated in-use, allowing for patient specific scaling and tracking of movement.
Keywords - inertial sensors, joint kinematics, segment length estimation
1. INTRODUCTION
The output of motion tracking is often only presented in joint angles. For this purpose, miniature gyroscopes and
accelerometers are an excellent tool. Although inclination can be measured with high accuracy using gyroscopes and
accelerometers, heading tracking can be challenging for some applications. A common way to estimate absolute
heading is by adding complementary sensors, usually magnetometers. Locations in which motion analysis is
performed do not always have a homogenous magnetic field which in turn leads to incorrect heading estimates [1].
Because only relative orientation between segments is required to compute the joint angle, there is no need for an
absolute heading reference. Favre et al. [2] described a method for 3D joint angle analysis using gyroscopes and
accelerometers including a calibration technique. Joint angles are expressed using the joint coordinate system
recommended by the ISB. Although this method is not affected by ferromagnetic materials since no magnetometers
are used, it gives only accurate results for short periods due to integration drift. Cooper et al [3] described a method
based on inertial sensors only by using the constraint that a knee is a hinge joint. Results show a high accuracy for
knee flexion/extension angles. However, ab/adduction and internal/external rotation are difficult to assess due to the
hinge constraint. Moreover, the hinge assumption cannot be used for other joints such as the hip or ankle.
Anatomical landmarks cannot be measured directly using inertial sensors which means that anthropometric
parameters are not available as they are with optical motion tracking. For many applications such as force
calculations, animation or VR, segment lengths should be known as well. For inertial motion tracking, a commonly
used method to estimate segment dimensions is by measuring distances between anatomical landmarks with a caliper
combined with regression equations based on anthropometric models [4]. A limitation is that anatomical landmarks
do not represent the functional center of rotation of the joint. Moreover, palpation of anatomical landmarks can
introduce errors up to a few cm [5].
This paper describes a novel method for stable and accurate tracking of 3D joint angles that does not reference to the
local magnetic field. Moreover, during motion the vector from the sensor to the joint center can be estimated. When
sensors are placed on three adjacent segments, the length from joint center to joint center of the intermediate segment
can be estimated.
2. METHODS
The algorithm, named Kinematic Coupling (KiC), estimates the orientation of three adjacent segments with the joints
modeled as ball-and-socket joints containing some laxity. The algorithm is constructed in a statistical framework in
the form of a Kalman filter and consists of two parts, a prediction step and a correction step. In the prediction step, the
angular velocities measured with the gyroscopes are used to predict the change in angle of each segment and the
acceleration is used to predict the velocity and position of each segment. Since these estimates will suffer from
integration drift, a correction step is necessary. In the correction step, the gravity vector derived from the
accelerometers is used to stabilize the inclination of each segment. Also a joint constraint is applied.
Figure 1: Three inertial sensor modules are places on the upper leg, lower leg and foot. The vectors rA and rB1 point from sensor
to the knee joint. The vectors rB2 and rC point to the ankle joint.
Joint Constraint
The joint constraint consists of the assumption that the movements measured with the sensor on the upper leg and the
sensor on the lower leg are equal in the knee joint. The same assumption is used for the ankle joint with the lower leg
and foot sensor or any other joint for which the algorithm can be used. The relative heading of both segments
becomes observable when the joint is subject to acceleration. For this, the vectors rA and rB1 from sensor A and sensor
B to the joint center of the knee and the vectors rB2 and rC to the joint center of the ankle should be known:
GS
knee sensor A A A
GS
s
ensor B B B1
=+⋅
=+⋅
pp Rr
pRr
(1)
GS
ankle sensor B B B2
GS
s
ensor C C C
=+⋅
=+⋅
pp Rr
pRr
(2)
Where p is position of the joint or sensor and GSR the rotation matrix describing the orientation from the sensor in
the global frame.
Segment scaling
By using relations (1) and (2) and the correlations between the movements of segments, the vectors from sensor to
joint are estimated in the Kalman filter framework. When the vectors rB1 and rB2 are estimated, the length of the lower
leg from knee joint center to ankle joint center is known.
Experiments
The lower limb kinematics of the unimpaired limb of a transfemoral amputee during walking were measured with
Xsens inertial sensors and with an optical system Vicon. Reflective markers were rigidly attached on the inertial
sensors modules. With this set-up, the soft tissue artefacts are equal for both systems. The Cast protocol [6] was
applied as calibration method to align sensors to segments.
As a reference for the segment length estimation, the lower leg length was based on the length between the midpoint
of the lateral and medial epicondyles and the lateral and medial malleoli measured with the optical system in a static
trial.
3. RESULTS
The knee and ankle joint angles are shown in Figure 2. The mean RMS difference between KiC and the optical
system over all joint angles for 10 strides is 2.0 degrees.
20 60 100
-10
5
50
70
KNEE
F(+)E(-) (deg)
20 60 100
-10
0
15
25 Ab(+)Ad(-) (deg)
20 60 100
-25
-15
-5
5 I(+)E(-) (deg)
20 60 100
-40
-20
0
15
Stride (%)
ANKLE
20 60 100
-20
-15
-5
5
Stride (%) 20 60 100
5
10
20
35
Stride (%)
Figure 2: 3D knee and ankle joint angles for one typical stride during walking, in black the KiC algorithm and in gray (dashed)
the optical system. The mean RMS difference between KiC and the optical system over all joint angles for 10 strides is 2.0
degrees which is in the order of the accuracy of the optical system.
Figure 3 shows the segment length estimation for the lower leg. The solid line is the length estimated with the KiC
algorithm. The vectors rB1 and rB2 were initialized at length 0. When the subject starts moving, the estimate converges
quickly to the length as measured with the optical system. The gray dashed line is the length measured in the static
trial and the dotted line is the distance between the lateral epicondyle and lateral mallelus during walking of the
optical system. The difference at the end of the trial is 2.4 cm.
00.5 11.5 22.5 33.5 4
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
Time (s)
Length (m)
Lower leg length
Figure 3: Lower leg segment length estimation during walking. The solid line is the length estimated with the KiC algorithm. It
starts converging as soon as the subject starts walking. The dashed line is the length between the knee and ankle joint centers as
determined with the optical system in a static trial. The dotted line shows the distance between the RLE and RLM markers during
walking. The difference between KiC and the optical reference is 2.4 cm at the end of the trial.
4. DISCUSSION
From figure 2, it can be concluded that the KiC algorithm is an accurate method for ambulatory joint angle tracking.
Based on the used measurement set-up, the observed differences are in the order of the accuracy of the optical system.
The assumptions that were used in the method are rather general. Specifically, the joint constraint does not specify
any limitation on the degrees of freedom of rotation in the joint which means that it can be applied to any ball-and
socket like joint. No magnetometers were used in this set-up. For absolute heading reference or stability when
standing still for long times, they could be added. Given the typical performance of current miniature gyroscopes, we
expect a small acceleration once every ten seconds to be sufficient to keep the heading error within 1-2 degrees.
Not only the joint angles, but also the joint positions can be estimated as part of the estimation method. Knowing the
joint position is vital for making patient specific measurements as well as to determine the orientation of the inertial
sensors with respect to the joints. For the walking trial under investigation, the convergence was sufficiently fast for
use in clinical applications. Moreover, the resulting estimated arm approached the arm estimated by palpation to
within an accuracy that can be expected by palpation. During walking, we can see a periodic pattern in segment
length estimation. This pattern is also present in the optical system. The biggest contribution in this variation is most
likely caused by soft tissue movements. The sensor mounted on the legs will move along with the muscles and skin
which will change the actual distance from sensor to joint. Another effect, though probably small, is the variation of
the rotation point within the knee joint as a function of the joint angle.
By including a sensor on the pelvis in the algorithm, the length of the upper leg can be estimated. Also, this method
can easily be applied to other body segments such as arms. The segment lengths of end effectors (feet, hands and
head) cannot be assessed using this method.
5. REFERENCES
[1] W.H.K. de Vries, H.E.J. Veeger, C.T.M. Baten, F.C.T. van der Helm, Magnetic distortion in motion labs, implications for validating inertial
magnetic sensors, Gait & Posture (2009), 29(4): 535- 54.
[2] Favre, J., Jolles, B.M., Aissaoui, R. and Aminian, K., Ambulatory measurement of 3D knee joint angle. Journal of Biomechanics, 41
(2008), 1029-1035
[3] Glen Cooper, Ian Sheret, Louise McMillian, Konstantinos Siliverdis, Ning Sha, Diana Hodgins, Laurence Kenney, David Howard. Inertial
sensor-based knee flexion/extension angle estimation. Journal of Biomechanics, 42 (2009), 2678-2685
[4] E.B. Bachmann. Inertial and magnetic tracking of limb segment orientation for inserting humans into synthetic environments. PhD Thesis,
Naval Postgraduate School, 2000.
[5] Della Croce U., Leardini A., Chiari L., Cappozzo A. Human movement analysis using stereophotogrammetry Part 4: Assessment of
anatomical landmark misplacement and its effects on joint kinematics (2005) Gait and Posture, 21 (2), pp. 226-237.
[6] Alberto Ferrari, Andrea Giovanni Cutti, Pietro Garofalo, Michele Raggi, Monique Heijboer, Angelo Cappello and Angelo Davalli. First in
vivo assessment of “Outwalk”: a novel protocol for clinical gait analysis based on inertial and magnetic sensors. Medical & Biological
Engineering & Computing, 48 (1), 2010,. 1-15
... Simple kinematic models have been used to estimate the position and orientation of multiple segments measured by inertial sensors and avoid integration drift (Molet et al. 1999;Luinge et al. 2007;Roetenberg et al. 2010;Zhou and Hu 2010). They are also applied, using an extended Kalman filter, in (one of) the first commercial full-body inertial motion capturing systems (MVN, Xsens Technologies, Enschede, The Netherlands). ...
... Because this drift increases over time, it limits the length of the reconstructions in the musculoskeletal model. Pre-processing sensor fusion algorithms using multiple IMMUs and a kinematic model (Molet et al. 1999;Luinge et al. 2007;Roetenberg et al. 2010;Zhou and Hu 2010), as discussed in the introduction, could prevent this, but should be used with care. Resulting IMMU orientations can already be affected by STAs or other measurement errors, and special care is needed to guarantee agreement between the kinematic models used during pre-processing and the global optimisation described in this study. ...
... However, segment lengths do have an important influence on the reconstructed motion, so they need to be measured manually or determined from additional measurements with the same subject where position information is available. Alternatively, segment lengths (and IMMU locations) can be determined by the kinematic coupling algorithm suggested by Roetenberg et al. (2010). ...
Driving a musculoskeletal model with inertial and magnetic measurement units
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We developed and evaluated a new kinematic driver for musculoskeletal models using ambulatory inertial and magnetic measurement units (IMMUs). The new driver uses the orientation estimates based on sensor fusion of each individual IMMU and benefits from two important properties of musculoskeletal models. First, these models contain more complex, anatomical, kinematic models than those currently used for sensor fusion of multiple IMMUs and are continuously improved. Second, they allow movement between segment and measured sensor. For three different tasks, the new IMMU driver, (optical) marker drivers and a combination of both were used to reconstruct the motion. Maximal root mean square (RMS) joint angle differences with respect to the silver standard (combined IMMU/marker drivers) were found for the hip joint; 4°, 2° and 5° during squat, gait and slideboard tasks for IMMU-driven reconstructions, compared with 6°, 5° and 5° for marker-driven reconstructions, respectively. The measured angular velocities corresponded best to the IMMU-driven reconstructions, with a maximal RMS difference of 66°/s, compared with 108°/s and 91°/s for marker-driven reconstructions and silver standard. However, large oscillations in global accelerations occurred during IMMU-driven reconstructions resulting in a maximal RMS difference with respect to measured acceleration of 23 m/s(2), compared with 9 m/s(2) for reconstructions that included marker drivers. The new driver facilitates direct implementation of IMMU-based orientation estimates in currently available biomechanical models. As such, it can help in the rapid expansion of biomechanical analysis based on outdoor measurements.
View
... After separately estimating the pitch and roll of two sensors [17], a second Kalman filter exploited information on the link between segments to improve the relative orientation between sensors. Roetenberg et al. [18] incorporated the position of the joint center in the sensor dynamics to correct for drift in the joint angle. Kok et al. [19] incorporated position and velocity in the dynamic model, combined with a biomechanical constraint to keep adjacent segments connected, at all times. ...
... Also, experimental validation has been carried out on rigid mechanical setups with joints that match ideal assumptions [16] or is restricted to segment inclination in a dominant sagittal movement plane [15]. Including position and velocity [19] makes the problem more computationally heavy and extensive validation is necessary for a good understanding of the working principles [18]. ...
Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements
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The ability to capture joint kinematics in outside-laboratory environments is clinically relevant. In order to estimate kinematics, inertial measurement units can be attached to body segments and their absolute orientations can be estimated. However, the heading part of such orientation estimates is known to drift over time, resulting in drifting joint kinematics. This study proposes a novel joint kinematic estimation method that tightly incorporates the connection between adjacent segments within a sensor fusion algorithm, to obtain drift-free joint kinematics. Drift in the joint kinematics is eliminated solely by utilizing common information in the accelerometer and gyroscope measurements of sensors placed on connecting segments. Both an optimization-based smoothing and a filtering approach were implemented. Validity was assessed on a robotic manipulator under varying measurement durations and movement excitations. Standard deviations of the estimated relative sensor orientations were below 0.89° in an optimization-based smoothing implementation for all robot trials. The filtering implementation yielded similar results after convergence. The method is proven to be applicable in biomechanics, with a prolonged gait trial of 7 minutes on 11 healthy subjects. Three-dimensional knee joint angles were estimated, with mean RMS errors of 2.14°, 1.85°, 3.66° in an optimization-based smoothing implementation and mean RMS errors of 3.08°, 2.42°, 4.47° in a filtering implementation, with respect to a golden standard optical motion capture reference system.
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... Motion analysis protocols based on stereophotogrammetry, providing both kinematics and kinetics outcomes, can support the quantitative evaluation of the aspects previously described, allowing the comparison of different prosthetic devices (either based on different working principle or provided by different suppliers). Moreover, instrumental analysis becomes useful when compensatory mechanisms in the unimpaired limb occur during walking, as reported in [37]. An example of a motion analysis protocol based on stereophotogrammetry and CAST technique [16], specifically designed for the evaluation of prosthetic knees is presented in Chapter 3. ...
... This confirmed the need to consider the two phases separately in the analysis of the kinematics of a subject, as well as the need to always incorporate in an upper-extremity protocol a segmentation algorithm. The differences between the average patterns in the upward and downward phases 112 are consistent with previous findings for the scapula-humeral and claviclehumeral rhythm [36,37,6]. ...
View
... IMMS data were processed in MATLAB software, based on the MT Software Development Kit (Xsens Technologies B.V.) [7]. The orientations of the SUs on the foot, shank and thigh were calculated with the Kinematic Coupling algorithm (KiC [26]), to avoid any influence of a non-homogenous earth magnetic field in the gait laboratory, that could be caused by ferromagnetic materials in the surroundings [9]. The anatomical CSs were defined according to the Outwalk protocol. ...
... The KiC algorithm, used for the estimation of the ankle and knee kinematics, does not rely on the use of the IMMS magnetometers [26]. Therefore, these joint kinematics were not affected by a non-homogenous earth magnetic field which may be a main concern in gait laboratories [9]. ...
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3D kinematic measurements in children with cerebral palsy (CP) to assess gait deviations can only be performed in gait laboratories using optoelectronic systems. Alternatively, an inertial and magnetic measurement system (IMMS) can be applied for ambulatory motion-tracking. A protocol named Outwalk has recently been developed to measure the 3D kinematics during gait with IMMS. This study preliminary validated the application of IMMS, based on the Outwalk protocol, in gait analysis of six children with CP and one typically developing child. Reference joint kinematics were simultaneously obtained from a laboratory-based system and protocol. On average, the root mean square error (RMSE) of Outwalk/IMMS, compared to the reference, was less than 17° in the transversal plane, and less than 10° in the sagittal and frontal planes. The greatest differences were found in offsets in the knee and ankle rotation, and in the hip flexion. These offset differences were mainly caused by a different anatomical calibration in the protocols. When removing the offsets, RMSE was always less than 4°. Therefore, IMMS is suitable for gait analysis of major joint angles in a laboratory-free setting. Further studies should focus on improvement of anatomical calibrations of IMMS that can be performed in children with CP.
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... Motion analysis protocols based on stereophotogrammetry, providing both kinematics and kinetics outcomes, can support the quantitative evaluation of the aspects previously described, allowing the comparison of different prosthetic devices (either based on different working principle or provided by different suppliers). Moreover, instrumental analysis becomes useful when compensatory mechanisms in the unimpaired limb occur during walking, as reported in [37]. An example of a motion analysis protocol based on stereophotogrammetry and CAST technique [16], specifically designed for the evaluation of prosthetic knees is presented in Chapter 3. ...
... This confirmed the need to consider the two phases separately in the analysis of the kinematics of a subject, as well as the need to always incorporate in an upper-extremity protocol a segmentation algorithm. The differences between the average patterns in the upward and downward phases 110 are consistent with previous findings for the scapula-humeral and claviclehumeral rhythm [36,37,6]. ...
First in vivo assessment of Outwalk: A novel protocol for clinical gait analysis based on inertial and magnetic sensors
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A protocol named "Outwalk" was recently proposed to measure the thorax-pelvis and lower-limb kinematics during gait in free-living conditions, by means of an inertial and magnetic measurement system (IMMS). The aim of this study was to validate Outwalk on four healthy subjects when it is used in combination with a specific IMMS (Xsens Technologies, NL), against a reference protocol (CAST) and measurement system (optoelectronic system; Vicon, Oxford Metrics Group, UK). For this purpose, we developed an original approach based on three tests, which allowed to separately investigate: (1) the consequences on joint kinematics of the differences between protocols (Outwalk vs. CAST), (2) the accuracy of the hardware (Xsens vs. Vicon), and (3) the summation of protocols' differences and hardware accuracy (Outwalk + Xsens vs. CAST + Vicon). In order to assess joint-angles similarity, the coefficient of multiple correlation (CMC) was used. For test 3, the CMC showed that Outwalk + Xsens and CAST + Vicon kinematics can be interchanged, offset included, for hip, knee and ankle flexion-extension, and hip ab-adduction (CMC > 0.88). The other joint-angles can be interchanged offset excluded (CMC > 0.85). Tests 1 and 2 also showed that differences in offset between joint-angles were predominantly induced by differences in the protocols; differences in correlation by both hardware and protocols; differences in range of motion by the Xsens accuracy. Results thus support the commencement of a clinical trial of Outwalk on transtibial amputees.
View
... An advanced articulated body model constraints the movements of segments with respect to each other and eliminates any integration drift. Multi-segment models are typically used in the field of bio-mechanical engineering Roetenberg et al (2010) describe experimental results for a filter using these models. Piezoresistive textile sensors have been used to detect the movements of human body in a wide range of configurations, for the monitoring of the hand, the wrist, the elbow and the knee articulation . ...
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Textile sensors have attempted to measure heart rate, respiratory rate, as well as moving performance. The characteristics of conductivity and elasticity of textile are used to measure angle and performance. In this chapter, the principles of motion analysis using conventional optical method, inertial sensors, and textile sensors are briefly presented. In particular, overview of textile sensors in terms of conductivity and elasticity is explained. Finally, the current topics of sensor application are introduced.
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... However, use of IMMS for segment orientation may introduce new sources of error in joint position and KAdM estimation, due to anatomical calibration of sensors and nonhomogenous magnetic fields that may influence the orientation estimation (de Vries et al., 2009;O'Donovan et al., 2007). Options for accurate IMMS measurement of segment and joint orientations and positions could include kinematic coupling algorithms estimating position vectors from IMMS to joint centres ( Roetenberg et al., 2010), body-mounted systems using magnetic actuation that can track the positions of IMMS with reasonable accuracy ( Schepers et al., 2010), or palpation of anatomical bony landmarks with IMMSbased calibration devices ( Picerno et al., 2008). Since the KAdM is sensitive to the accurate estimation of the joint position, future research should focus on determining whether these methods can actually improve the estimation of joint positions. ...
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The external knee adduction moment (KAdM) during gait is an important parameter in patients with knee osteoarthritis (OA). KAdM measurement is currently restricted to instruments only available in gait laboratories. However, ambulatory movement analysis technology, including instrumented force shoes (IFS) and inertial and magnetic measurement systems (IMMS), can measure kinetics and kinematics of human gait free of laboratory restrictions. The objective of this study was a quantitative validation of the accuracy of the KAdM in patients with knee OA, when estimated with an ambulatory-based method (AmbBM) versus a laboratory-based method (LabBM). AmbBM is employing the IFS and a linked-segment model, while LabBM is based on a force plate and optoelectronic marker system. Effects of ground reaction force (GRF), centre of pressure (CoP), and knee joint position measurement are evaluated separately. Twenty patients with knee OA were measured. The GRFs showed differences up to 0.22 N/kg, the CoPs showed differences up to 4 mm, and the medio-lateral and vertical knee position showed differences to 9 mm, between AmbBM and LabBM. The GRF caused an under-estimation in KAdM in early stance. However, this effect was counteracted by differences in CoP and joint position, resulting in a net 5% over-estimation. In midstance and late stance the accuracy of the KAdM was mainly limited by use of the linked-segment model for joint position estimation, resulting in an under-estimation (midstance 6% and late stance 22%). Further improvements are needed in the estimation of joint position from segment orientation.
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This contribution is concerned with joint angle calculation based on inertial measurement data in the context of human motion analysis. Unlike most robotic devices, the human body lacks even surfaces and right angles. Therefore, we focus on methods that avoid assuming certain orientations in which the sensors are mounted with respect to the body segments. After a review of available methods that may cope with this challenge, we present a set of new methods for: (1) joint axis and position identification; and (2) flexion/extension joint angle measurement. In particular, we propose methods that use only gyroscopes and accelerometers and, therefore, do not rely on a homogeneous magnetic field. We provide results from gait trials of a transfemoral amputee in which we compare the inertial measurement unit (IMU)-based methods to an optical 3D motion capture system. Unlike most authors, we place the optical markers on anatomical landmarks instead of attaching them to the IMUs. Root mean square errors of the knee flexion/extension angles are found to be less than 1° on the prosthesis and about 3° on the human leg. For the plantar/dorsiflexion of the ankle, both deviations are about 1°.
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Ambulatory measurement of the knee adduction moment in patients with osteoarthritis of the knee
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High knee joint-loading increases the risk and progression of knee osteoarthritis (OA). Mechanical loading on the knee is reflected in the external knee adduction moment (KAdM) that can be measured during gait with laboratory-based measurement systems. However, clinical application of these systems is limited. Ambulatory movement analysis systems, including instrumented force shoes (IFS) and an inertial and magnetic measurement system (IMMS), could potentially be used to determine the KAdM in a laboratory-free setting. Promising results have been reported concerning the use of the IFS in KAdM measurements; however its application in combination with IMMS has not been studied. The objective of this study was to compare the KAdM measured with an ambulatory movement analysis system with a laboratory-based system in patients with knee OA. Gait analyses of 14 knee OA patients were performed in a gait laboratory. The KAdM was concurrently determined with two the systems: (i) Ambulatory: IFS and IMMS in combination with a linked-segment model (to obtain joint positions); (ii) Laboratory: force plate and optoelectronic marker system. Mean differences in KAdM between the ambulatory and laboratory system were not significant (maximal difference 0.20%BW⁎H in late stance, i.e. 5.6% of KAdM range, P>0.05) and below clinical relevant and hypothesized differences, showing no systematic differences at group level. Absolute differences were on average 24% of KAdM range, i.e. 0.83%BW⁎H, particularly in early and late stance. To achieve greater accuracy for clinical use, estimation of joint position via a more advanced calibrated linked-segment model should be investigated.
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First in vivo assessment of Outwalk: A novel protocol for clinical gait analysis based on inertial and magnetic sensors
Article
Full-text available
  • Nov 2009
  • MED BIOL ENG COMPUT
A protocol named "Outwalk" was recently proposed to measure the thorax-pelvis and lower-limb kinematics during gait in free-living conditions, by means of an inertial and magnetic measurement system (IMMS). The aim of this study was to validate Outwalk on four healthy subjects when it is used in combination with a specific IMMS (Xsens Technologies, NL), against a reference protocol (CAST) and measurement system (optoelectronic system; Vicon, Oxford Metrics Group, UK). For this purpose, we developed an original approach based on three tests, which allowed to separately investigate: (1) the consequences on joint kinematics of the differences between protocols (Outwalk vs. CAST), (2) the accuracy of the hardware (Xsens vs. Vicon), and (3) the summation of protocols' differences and hardware accuracy (Outwalk + Xsens vs. CAST + Vicon). In order to assess joint-angles similarity, the coefficient of multiple correlation (CMC) was used. For test 3, the CMC showed that Outwalk + Xsens and CAST + Vicon kinematics can be interchanged, offset included, for hip, knee and ankle flexion-extension, and hip ab-adduction (CMC > 0.88). The other joint-angles can be interchanged offset excluded (CMC > 0.85). Tests 1 and 2 also showed that differences in offset between joint-angles were predominantly induced by differences in the protocols; differences in correlation by both hardware and protocols; differences in range of motion by the Xsens accuracy. Results thus support the commencement of a clinical trial of Outwalk on transtibial amputees.
View
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Current motion tracking technologies fail to provide accurate wide area tracking ofmultiple users without interference and occlusion problems. These limitations makedifficult the construction of a practical and intuitive interface, which allows humans to beinserted into networked virtual environments in a fully immersive manner. Advances in thefield of miniature sensors make possible inertial/magnetic tracking of human body limbsegment orientation without the limitations of current...
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A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth's magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 mile/h. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5 mph). The joint constraint used in the IMU analysis was derived from the Qualysis data. Limitations of the method, its clinical application and its possible extension are discussed.
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Magnetic distortion in motion labs, implications for validating inertial magnetic sensors
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  • Feb 2009
Ambulatory 3D orientation estimation with Inertial Magnetic Sensor Units (IMU's) use the earth magnetic field. The magnitude of distortion in orientation in a standard equipped motion lab and its effect on the accuracy of the orientation estimation with IMU's is addressed. Orientations of the earth magnetic field vectors were expressed in the laboratory's reference frame. The effect of a distorted earth magnetic field on orientation estimation with IMU's (using both a quaternion and a Kalman fusing algorithm) was compared to orientations derived from an optical system. The magnetic field varied considerably, with the strongest effects at 5 cm above floor level with a standard deviation in heading of 29 degrees , decreasing to 3 degrees at levels higher than 100 cm. Orientation estimation was poor with the quaternion filter, for the Kalman filter results were acceptable, despite a systematic deterioration over time (after 20-30s). Distortion of the earth magnetic field is depending on construction materials used in the building, and should be taken into account for calibration, alignment to a reference system, and further measurements. Mapping the measurement volume to determine its ferromagnetic characteristics in advance of planned experiments can be the rescue of the data set. To obtain valid data, "mapping" of the laboratory is essential, although less critical with the Kalman filter and at larger distances (>100 cm) from suspect materials. Measurements should start in a "safe" area and continue no longer than 20-30s in a heavily distorted earth magnetic field.
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Human movement analysis using stereophotogrammetry Part 4: Assessment of anatomical landmark misplacement and its effects on joint kinematics
Article
  • Mar 2005
  • GAIT POSTURE
Estimating the effects of different sources of error on joint kinematics is crucial for assessing the reliability of human movement analysis. The goal of the present paper is to review the different approaches dealing with joint kinematics sensitivity to rotation axes and the precision of anatomical landmark determination. Consistent with the previous papers in this series, the review is limited to studies performed with video-based stereophotogrammetric systems. Initially, studies dealing with estimates of precision in determining the location of both palpable and internal anatomical landmarks are reviewed. Next, the effects of anatomical landmark position uncertainty on anatomical frames are shown. Then, methods reported in the literature for estimating error propagation from anatomical axes location to joint kinematics are described. Interestingly, studies carried out using different approaches reported a common conclusion: when joint rotations occur mainly in a single plane, minor rotations out of this plane are strongly affected by errors introduced at the anatomical landmark identification level and are prone to misinterpretation. Finally, attempts at reducing joint kinematics errors due to anatomical landmark position uncertainty are reported. Given the relevance of this source of errors in the determination of joint kinematics, it is the authors' opinion that further efforts should be made in improving the reliability of the joint axes determination.
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Ambulatory measurement of 3D knee joint angle
Article
  • Feb 2008
  • J BIOMECH
Three-dimensional measurement of joint motion is a promising tool for clinical evaluation and therapeutic treatment comparisons. Although many devices exist for joints kinematics assessment, there is a need for a system that could be used in routine practice. Such a system should be accurate, ambulatory, and easy to use. The combination of gyroscopes and accelerometers (i.e., inertial measurement unit) has proven to be suitable for unrestrained measurement of orientation during a short period of time (i.e., few minutes). However, due to their inability to detect horizontal reference, inertial-based systems generally fail to measure differential orientation, a prerequisite for computing the three-dimentional knee joint angle recommended by the Internal Society of Biomechanics (ISB). A simple method based on a leg movement is proposed here to align two inertial measurement units fixed on the thigh and shank segments. Based on the combination of the former alignment and a fusion algorithm, the three-dimensional knee joint angle is measured and compared with a magnetic motion capture system during walking. The proposed system is suitable to measure the absolute knee flexion/extension and abduction/adduction angles with mean (SD) offset errors of -1 degree (1 degree ) and 0 degrees (0.6 degrees ) and mean (SD) root mean square (RMS) errors of 1.5 degrees (0.4 degrees ) and 1.7 degrees (0.5 degrees ). The system is also suitable for the relative measurement of knee internal/external rotation (mean (SD) offset error of 3.4 degrees (2.7 degrees )) with a mean (SD) RMS error of 1.6 degrees (0.5 degrees ). The method described in this paper can be easily adapted in order to measure other joint angular displacements such as elbow or ankle.
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