1. An evaluation of inertial motion capture technology for use in the optimization of road cycling kinematics
Welcome to my presentation. My name is John Cockcroft, from Stellenbosch University in the Western Cape. This morning I will be presenting a study entitled:
John Cockcroft
University of Stellenbosch

2. Overview of Presentation
Background to inertial motion capture and road cycling
Research motication objectives
Experimental work
Analysis of magnetic interference
Analysis of cycling kinematics
Conclusions
I will start by giving quite a lot of background on the working principles of inertial motion capture systems
And then briefly cover road cycling kinematics
Before presenting the project goals
I will also explain a little about the measurements we took
And then move on to the important part of the work, which consists of an analysis of magnetic interference in the tests
And some results from the cycling kinematics measurements
We will then finish off with conclusions

3. Inertial motion capture technology
The MVN BIOMECH system
Produced by Dutch company Xsens B.V
One of the fastest growing European technology companies
Track motion with accelerometers and gyroscopes attached to the body with a body fitting Lycra suit
The inertial motion capture system used in this study is called the MVN BIOMECH. It is produced in the Netherlands.
Although motion capture technology has been around for a couple of decades, inertial motion capture systems such as the MVN have only become available in the last few years.
Although the MVN is an adolescent technology, it is emerging as a serious contender to the optical systems. Validations studies have actually shown that the MVN is on par with “golden standard” camera systems such as the Vicon.
The MVN is called an inertial system because instead of using external cameras to track human motion it uses inertial sensor units attached to the body.

4. Portable storage Kinematic Analysis Digital model Body-fit Lycra suit
The main advantage of the MVN suit is its portability. Unlike the traditional lab based optical systems, the MVN can fit into a suitcase and can be used outdoors for field testing.
The test subject wears a body fitting Lycra suit which houses 17 inertial measurement units, called MTxs, each one attached to a strategic body segment.
Motion capture data is transmitted wirelessly from the suit to a nearby computer in real time at 120 Hz.
These sensor signals are then processed in the MVN software to produce a live digital rendering of the test subject’s motion with a biomechanical model (avatar).
Finally, kinematic variables such as joint angles can then be extracted for analysis in other programs such as Matlab
Wireless
data transmission
Sensor to segment
calculations

5. FULL BODY KINEMATICS: BIOMECHANICAL MODEL
Sternum
Head
Forearm
Hand
Upper leg
Lower leg
Upper arm L3 L5
Foot T8
T12
Neck
Shoulder
Toe
This is the biomechanical model which moves on the computer screen just like the test subject in the suit. The body is modelled with 23 segments connected by 22 joints.
Before testing the model needs to be calibrated with various poses to take into account the way the suit fits onto the subjects body, what we would call sensor-to-segment orientation, and scaled according to their body dimensions so that the segments are the right lengths.
Each MTx represents one of these segments, and the MTx kinematics are converted into the corresponding body segment kinematics at each time step using the sensor to segment orientation
23 Segments, 22 joints
Anthropometrical scaling

6. INERTIAL MEASUREMENT UNITS
Inertial navigation system
Gyroscope provide angular data
Accelerometer provide linear data
Magnetometer provide heading in the global frame
Errors in sensor kinematics
Integration error in gyroscope and accelerometer data
Magnetic disturbances
Each inertial measurement unit, or MTx, contains three 3D sensors: accelerometers, gyroscopes and magnetometers. These measurements are input into a inertial navigation system, like those used in aircraft or naval vessels, in order to calculate the motion of the MTx as it moves with the subjects body.
The gyroscope signal is integrated and differentiated to get the angular position and acceleration of the MTx and the accelerometer signal is integrated and double integrated for the linear position and velocity of the MTx. Since the coordinate system for these measurements is local for the MTx, the heading data from magnetometer allows for the measurements to be translated to the global frame.
There are some problems with the sensor data however – there are errors in the gyroscope and accelerometer data which accumulate over time due to integration, and errors in global heading when the magnetic field around the MTX is disturbed.

7. ELIMINATING GYROSCOPE INTEGRATION ERROR
Sensor drift
Gyroscope offset errors are cumulative over time
Orientation error can become very large in just a few seconds
Sensor fusion
Accelerometer is used as an inclinometer to stabilize gyroscope data in the vertical plane
Magnetometer data is used to stabilize gyroscope data in the horizontal plane
The gyroscope integration error is solved with a Kalman filter using sensor fusion.
The principle behind sensor fusion is that the errors in one sensor measurement can be reduced by using data from other sensors.
The Kalman filter fuses the accelerometer and magnetometer data with the gyroscope data to reduce drift in orientation.
To correct gyroscope drift in the vertical plane, the accelerometer is used as an incli-nometer to find the true vertical plane by separating the gravitational acceleration vector from the acceleration data.
Similarly, to stabilize the gyroscope drift in the horizontal plane, the Kalman filter uses the magnetometer like a compass to reduce drift to correct drift.

8. REDUCING ACCELEROMETER DRIFT ERROR
Joint Updates
Joint constraints reduce error in joint centre estimation
Estimation of contact points
Decreased drift of biomechanical model in global frame
Furthermore, as can be seen in the left diagram, the segments can drift apart positionally due to integration error in the accelerometer, which then leads to uncertainty about the location of the joint centre.
The Kalman filter equations eliminate this drift error by applying physical constraints on the segments and joints in biomechanical model
For instance, the MVN software uses joint-specific statistical models which specify probabilities of joint location based on known joint characteristics, such as possible range of motion in all three planes, to choose the most realistic joint centre locations.
To further reduce positional drift, especially of the collective body model in the global frame, the MVN software also detects contact points between the body and the physical environment, such as the floor or stairs, so that the total positional error in the global frame is less that 2% of the travelled distance.

9. REDUCING MAGNETIC INTERFERENCE
Permanent constant interference
E.g. prostheses, orthotics
Characterized as a priori by Kalman filter and removed
Temporary constant/varying interference
E.g. walking past a speaker or metal structure
Rejected by advanced Kalman filtering
Permanent varying interference (>30s)
E.g. metal beams in the floor
Kinetic Coupling algorithm for lower leg joint flexion
The third and final sensor signal which has to be corrected is magnetic interference in the magnetometer data.
There are different types of interference. Permanently present and constant distortions, such as a steel prosthesis, can be mapped by the Kalman filter and compensated for. Temporary distortions, whether constant or varying, such as walking past a speaker or past a metal structure, can be handled using Kalman filtering methods for disturbance rejection for a short time, usually less than thirty seconds.
In this way, the MVN system offers rigid immunity to the majority of magnetic field disturbances. However, permanent and varying magnetic interference cannot be remedied and present a significant obstacle to readings.

10. Road Cycling Goals of technique enhancement
Minimize power demand by increasing aerodynamic efficiency
Maximize power production by increasing biomechanical efficiency
Decreased risk of overuse injuries by improving technique
i.e. Optimize bike fit
And now quickly a little bit about road cycling.
There are many performance factors in road cycling, of which technique is the most relevant to motion capture.
Enhancing cycling technique is basically about maximizing the aerodynamic efficiency of the body position to decrease power demand and the biomechanical efficiency to increase power output during the pedal stroke. This, along with efforts to reduce injuries and improve comfort, is determined by the bike fit.

11. Saddle-height adjustment
Fore-aft position
Handlebar adjustment
Saddle-height adjustment
Down tube
Top tube
Seat tube
Seat tube angle
So how does bicycle fit work? In some ways, due to the large amount of physiological diversity between riders, bicycle fit methods have not yet been standardized and there is still a lot of subjectivity in current approaches.
There are two types of bike fit: traditional static bike fits, which are done with the cyclist off the bike and rely mostly on formulas and ratios based on anthropometry, and dynamic fits, which are done with the cyclist on the bike to fine-tune and personalize the bike fit while pedalling.
However, in principle, the goal is to adjust the distance between the three interface points with the cyclist: the saddle, pedals and handlebars (red).
The reach, between the saddle and the handlebars, affects the upper body position, whereas the distance between the saddle and the pedal interface affects the joint angles in the lower limbs.
Interestingly, it has been shown that dynamic rider kinematics are different to static measurements with the cyclist on the bike , suggesting that dynamic bike fits are superior and more realistic.
Therefore, because motion capture systems like the MVN can accurately measure full body kinematics simultaneously, they are especially applicable for conducting dynamic bike fit.

12. Objectives Objectives Motivation
Perform measurements of outdoor cycling kinematics
Investigate level of magnetic interference from road bikes
Compare indoor and outdoor results
Investigate the link between rider kinematics and optimal bike fit
Motivation
Sports science research has been slow to adopt motion capture
There is very little sports related work being done with the MVN
Outdoor measurements of cycling kinematics not yet been conducted
This leads to the project goals.
So, basically, there were four project objectives.
The first one was to find out whether we could get accurate measurements of outdoor road cycling.
Secondly, we wanted to find out if there was significant magnetic interference during testing.
The second objective was to establish how we could use the MVN data for improving road cycling technique. This meant conducting a analysis of sub-elite cycling technique and identifying key focus areas for future research using the MVN.

13. Test Protocol General suit setup Two tests on separate days
10 male sub-elite cyclists using own bicycles
Two tests on separate days
Indoor on stationary trainer
Outdoor followed by pursuit vehicle
Three sessions per test
Low, medium and high intensity (2, 3.5 and 5.5 W.kg-1)
1min long steady-state recordings using the MVN suit
So we tested a group of ten male competition level cyclists wearing the MVN suit
The basic test protocol was to conduct two tests on separate days, an indoor test on a stationary trainer and an outdoor test on a stretch of road
Each test consisted of three sessions where a motion capture recording was taken for one minute during steady state at low, medium and high power.

14. Test Protocol
This is the setup of the tests in the lab on the trainer

15. Test Protocol This is a photo of one of the outdoor tests we did
The cyclist is riding with the suit on and we were following with the laptop in the pursuit vehicle within wireless range

16. Test Protocol This is just an example of the MVN software interface
You can view the avatar from any angle, and the video feed comes from a camera synchronized with the MVN.

17. Magnetic Measurement Results
Test environments
The indoor results show significant disturbances
Outdoor tests were undisturbed
Road bicycles
Hand sensors experienced worst interference
Chains, sprockets and pedals disturbed foot sensors
Sensor fusion settings
Kinematic Coupling algorithm is immune to interference
Can only be used for lower body when moving
So in terms of magnetic results, the laboratory environment was quite disturbed, probably because of the steel building supports. However, the outdoor environment is conducive to testing, which is promising.
However, although most of the road bikes were made of carbon fibre and aluminium, the ferromagnetic materials in the handlebars as well as in the chain and sprockets, caused unacceptable magnetic interference to the hand and foot sensors. This means that the MVN system cannot take standard measurements on normal road bikes accurately.

18. For example, this is a graph of the magnetic field readings taken in one of the tests for the left upper arm. The x axis is the 10 cyclists that were tested, and the y axis is the magnetic intensity readings of the magnetometer.
The green line, which represents the indoor intensity, is offset from the undisturbed value of 50. The outdoor intensity, on the other hand, the blue line, is almost totally undisturbed.
Because these lines are flat, the magnetic field is the same for all the cyclists and so we can see that there is very little bicycle related interference for the upper arm.

19. However, as we move down the arm to the forearm sensor, closer to the bike, we see that the earths magnetic field starts to distort .

20. And when we get to the hand sensor we see that there are significant and varying bike-related disturbances for some cyclists.
This shows that the different bicycles contain different amounts of ferromagnetic materials in the handlebars. A similar pattern is seen for the upper leg, lower leg and foot.
Fortunately, the MVN software can be used to calculate flexion angles in the sagittal plane without the magnetic data. This at least allowed for some analysis of the hip, knee and ankle flexion angles

21. Hip, knee and ankle flexion definitions

22. This is an example of the left leg measurements taken in an outdoor test for a single cyclist. The blue is knee flexion, the red hip flexion and the green is ankle flexion. The x axis is the angle of the pedal crank, taken as 0 from the top pedal position and 180 at the bottom position of the pedal.
As expected, the angles are sinusoidal and fairly uniform, due to the repetitive nature of pedalling. The hip is at maximum extension at exactly the bottom of the pedal stroke, whereas the knee is about 15 degrees earlier, when the leg is parallel to the slightly angled seat post. Again the knee is at maximum flexion about 345 degrees, whereas the hip is at maximum flexion only slight after the top of the pedal stroke. The ankle is in the heel down position for the first half of the downstroke, before going into maximum plantarflexion around the same time as the knee is maximally extended, and then recovering during the upstroke back to the heel down position. All of these findings are almost identical to the data published in other studies. Although of course, the maximum and minimum values differ with different saddle heights and bicycle fit.

23. Overview of outdoor kinematics
Flexion measurements are valid
Joint excursions correlate well with past studies
Significant variability in flexion between cyclists
Maximum
Minimum
Range
Hip
75.5 ± 9.7
(~90**)
23.5 ± 8.5
(~45**)
52 ± 4.6
(54 ± 4*) (~45**)
Knee
117 ± 7.7
(~110**)
31.5 ± 7.7
(30-60**)
85.5 ± 6.5
(69 ± 4*) (~75**)
Ankle
12.1 ± 8.9
(plantarflexion)
-9.8 ± 8.8
(dorsiflexion)
21.9 ± 6.8
(19 ± 4*) (~20**)
Here we have a table of the overall maximum, minimum and range of flexion results, each with a mean and standard deviation. Measurements taken in other studies are shown in brackets for comparison.
The flexion values which were measured compare well with values from other published studies. The maximum and minimum hip flexion were a bit lower in general, perhaps due to a higher than average seat height, although the range of motion was very similar to other studies. On the other hand, the range of knee flexion was about 10 degrees higher with the MVN due to higher than usual maximum flexion and lower than usual minimum flexion values. This may be due to .... Due to the high variability of ankle plantarflexion and dorsiflexion, standard values are not available, although the range of motion reported is also very close to those in other studies.
Another important thing to note is the relatively high standard deviations for the measurements. This is a strong indication that competition level cyclists, who have all had professional bicycle fits and adapted this to suit there personal preferences, are cycling at quite different joint excursions. This brings into question the static methods of bicycle fit which use anthropometric measurements to choose seat height and other parameters, since this data shows that joint angles are not highly correlated between top level cyclists and that there other variables which affect performance and choice of optimal bicycle fit.
* (Bini RR, 2008)
** (R.J Gregor, 2000)

24. Comparison of indoor/outdoor tests
Ecological validity of indoor testing
Is laboratory testing on a trainer realistic?
No wind resistance, rigid wheel fixtures etc.
Comparison of laboratory and road tests
Hip and knee values in outdoor tests higher on average
Ankle values in outdoor tests lower on average
However, no clear trend between indoor and outdoor tests
Some studies have recently been done to determine the effect of indoor testing on the validity of the data.
A comparison of the indoor and outdoor data from our study showed that there were minor differences between the average values for hip, knee and ankle flexion.
These may be accredited to small subconscious changes in technique due to the lack of wind resistance and the rigid wheel fixtures in the laboratory tests, although there is no real trend in the data indicating significant changes.
All in all, the indoor and outdoor data were the effectively the same.
Δ Maximum
Δ Minimum
Δ Range
Hip
-4.7°
-6.0°
1.3°
Knee
-1.8°
-3.9°
2.1°
Ankle
3.9°
4.0°
-0.1°

25. Here we see, for example, the maximum knee flexions measured in all six tests for each of the ten cyclists. The red, orange and yellow values are the indoor tests and the blue values are the outdoor tests.
Here we can see that although the cyclists had varying peak flexion values, the indoor and outdoor measurements for each specific cyclist are extremely close.
This is also a strong indication of the test-retest reliability of the MVN systems measurements, since the suit was worn on two separate occasions with separate calibrations.
Furthermore, this consistency in kinematic measurements taken for successive tests on the same day also serves to confirm the same day repeatability of the MVN system

26. Conclusions
Can the MVN system measure accurate outdoor cycling kinematics?
No. Road bikes cause unacceptable magnetic interference
Only lower body joint angles in the sagittal plane
Flexion values correlate well with other studies
What was learned from the kinematic data?
No clear difference between indoor and outdoor kinematics
High variability in hip, knee and ankle flexion suggest that bicycle fit should not be based on anthropometrical data
So in conclusion
First of all, the question was, are accurate measurements possible with the MVN?
However, in terms of full body kinematics, the answer is no. Only accurate hip, knee and ankle flexion measurements are currently possible.
These measurements correlate well with other studies done on lower limb kinematics.
We also saw that outdoor kinematics are not significantly different to indoor kinematics, which suggests that laboratory measurements are in fact ecologically valid.
And most importantly, we found that competition cyclists are not cycling at the same joint angles, even though they have close to optimal bicycle fit. This confirms that bicycle fit requires more data than just the kinematics which are being used for static bicycle fits.

27. THANK YOU

28. Motion Capture What is motion capture technology?
Converting analogue marker tracking to a digital model
Applications in entertainment (e.g. movies and games)
Used in movement science (e.g. medical research of gait) M
PHYSICAL SETUP
MARKER TRACKING
DIGITAL MODEL
So what is a motion capture system?
Basically, a motion capture system measures the full body kinematics of a test subject, including the angles of every joint in 3D. It does this by tracking the motion of specific points on a persons body with some form of motion sensor, often a camera, and reconstructs the full body kinematics on a computer with a digital body model.
Motion capture data has many valuable applications. It is very popular in the entertainment industry. For example, they are incorporating motion capture data to improve the realism in computer game graphics and movie animations.
Motion capture is also very useful as a research tool in the movement sciences, for example in biomechanical studies of gait or research efforts to improve ergonomics in factories etc.
However, there are some limitations with the technology at the moment. Most notably, the traditional optical systems require a controlled laboratory environment surrounded by cameras, which restricts the space available for motion capture and thus means that the types of motion which can be measured are limited, especially for sports, where motion capture data is extremely useful for analysis of technique etc.

29. OVERVIEW OF MOTION CAPTURE PROCESS: MVN SENSOR FUSION SCHEME
This is the MVN sensor fusion scheme, which converts the raw inertial data from the suit into a digital avatar.
The fusion scheme consists of two steps: a prediction and then correction of full body kinematics.
On the left the motion capture process begins with the raw data signals from the inertial sensors, which are input into an inertial navigation system.
The INS tracks the motion of each inertial sensor attached to the test subjects body in 3D space.
Then, the INS data is used to predict the kinematics of each segment of the subjects body corresponding to the MVN biomechanical model.
In the correction step, errors in the biomechanical model are eliminated with knowledge of joint characteristics, or joint updates, and contact points between the biomechanical model and the external world, such as feet touching the ground. Aiding sensors, such as a gps, can also be used if necessary, but are not covered in this study.

30. GYROSCOPE SENSOR FUSION: ERROR-STATE KALMAN FILTER
Orientation error θε
Gyroscope offset error bε
Magnetic disturbance error dε
QHM, Qd
QZM
QZG, QHG, Qb, Qθ
Accelerometer model
Gyroscope model
Magnetometer model
Kalman filter
Magnetometer signal
Gyroscope signal
Accelerometer signal + _ VA VG HG HM
This is the error-state Kalman filter used for sensor fusion.
This diagram shows how the MVN system compensated for integration error in the gyroscope data.
The principle behind sensor fusion is that the errors in one sensor measurement can be reduced by using data from other sensors.
Here, the gyroscope data contains inaccuracies, due to temperature effects etc., which are cumulative over time in the rotation data due to integration. In this way, the sensor readings can “drift” by several degrees after a few seconds!
However, to stabilize the rotation measurements in the vertical plane, the accelerometer data can be used as an inclinometer, by separating the gravitation vector from the acceleration data and thereby finding the vertical plane.
Furthermore, to stabilize the sensor drift in the horizontal plane, the Kalman filter incorporates the heading data from the magnetometers.
The Kalman filter estimates the probability of errors in each sensors data and then assigns priority to the sensor with the most trusted accuracy - covariances

31. SEGMENT KINEMATICS: CALIBRATIONS
N-pose
T-pose
Squat
Hand-touch
Calibrations are also necessary before motion capture can begin.
There are four calibration poses, taking less than a minute in total.
During calibration there is an initial estimate of joint centre positions.
Then, a stationary N and T pose are carried out to obtain the sensor-to-segment orientation
This is done by calculating the relative position of the sensors and the joint centers as a 3D vector offset.
Afterwards, there are also optional squat and hand touch movement calibrations which help to determine the functional axes of the legs and arms.

32. ESTIMATING SEGMENT KINEMATICS
The kinematics are estimated during every time step (at 120 Hz), by continually reestimating the joint centers and segments in the global frame.
As can be seen, the segment lengths are used along with the to

33. Effect of workload on kinematics
Relationship between cycling power and rider kinematics
Negligible change between low, medium and high power
Confirms the claims of current published literature
Δ Maximum
Δ Minimum
Δ Range
Hip
2.2
1.5
Knee
1.0
2.0
Ankle
2.9
4.1
3.6

34. Bilateral Asymmetry What is bilateral asymmetry? Results
Difference between left and right sides of the body
Can be a difference in kinetics or kinematics
Can be caused by limb dominance, differences in joint characteristics, anatomical differences, lateral pelvic tilt etc.
Results
Over 30% of the cyclists displayed significant asymmetry
Greatest asymmetry in hip and knee during downstroke
Greatest asymmetry for the ankles during the upstroke
Studies mostly focused on kinetic differences and not actual rider kinematics.

35. Bilateral asymmetry of the knee joint at minimum flexion
Right more flexed
This is an example graph of the outdoor bilateral asymmetry in each of the low, medium and high power sessions for each of the ten cyclists knee joints.
As we can see, most of the cyclists had minor asymmetries of less than 5 degrees.
Cyclist 1 had the greatest difference in knee flexion, with the right knee straightening about 13 degrees more at the same point than the left knee, which is quite severe.
Cyclists 7 and 8 were the same, at between 5 and 10 degrees. However, as can be seen, there is not trend between the low, medium and high power data.
It seems that those cyclists who had imbalances, produced similar asymmetrical flexion regardless of the workload.
Left more flexed

36. Knee overuse injuries Knee overuse injuries very common in cycling
Over 33% of knee injuries are to the patellofemoral joint (PFJ)
Second most common is iliotibial band friction syndrome (ITBFS)
Results from analysis
50% of the cyclists had knee flexion of over 120° and are at risk of PFJ pain
30% of the cyclists went under 25° flexion and are at high risk of ITBFS

37. Future Work Improvements to test protocol Avenues for future studies
Design of ferromagnetic-free road bicycle
Protocol for clinical anthropometrical measurements
Avenues for future studies
Integration of MVN data with measurements of kinetic, neuromuscular and metabolic variables
Analysis of biomechanical efficiency using 3D joint angles
Study of fatigue effects on upper body kinematics
Dynamic bicycle fit interventions for prevention of injury
One of the objectives was to identify key focus areas for future work.
At the moment the most important breakthrough will be to eliminate the magnetic interference, which would require the design of a custom road bike. That would allow for the measurement of 3D joint angles for biomechanical analysis and the upper body kinematics for fatigue analysis.
Once that is done, it would be feasible to begin integrating the MVN data with kinetic data such as pedal forces, power output and joint moments, neuromuscular activation patterns using surface EMG as well as physiological variables such as oxygen consumption and heart rate.
You could also develop better bike fit protocols for implementing interventions
Another area of growing interest in sports performance analysis is simulation software, which could benefit from the MVN data.