Towards Situated Knee Trajectory Visualization forSelf Analysis in Cycling
Oral Kaplan*

Goshiro Yamamoto†Nara Institute of Science and Technology

Kyoto University Hospital

Nara Institute of Science and Technology

Yasuhide Yoshitake§Alexander Plopski¶

National Institute of Sports and Fitness in Kanoya

Nara Institute of Science and Technology

Christian Sandor||Hirokazu Kato**

Nara Institute of Science and Technology

Nara Institute of Science and Technology

A BSTRACTInflammation, stiffness, and swelling are frequently reported symptoms of patellar tendinitis among cyclists; making knee pain a consistently observed overuse injury in cycling. In this paper, we investigate the applicability of a knee trajectory visualization to selfanalysis for increasing awareness of movement patterns leading toinjuries. We briefly explain overuse injuries and patellar instability,
describe the experiments we did with cyclists for gathering requirements, and finally illustrate an augmented reality concept. We also
show two different types of visualizations with participant opinions;
one being conventional and other being a video-based one and discuss how situated visualizations can be utilized for improving self
awareness to injury causes.
Index Terms: Human-centered computing—Mixed / augmentedreality; Human-centered computing—Information visualization—
Visualization design and evaluation methods
1Takafumi Taketomi‡

I NTRODUCTION

Cycling is a low-impact non-weight-bearing form of exercise due
to body weight being carried by bicycle [3]. However due to its
exceedingly repetitive nature, overuse injuries in lower-extremities
are consistently observed [2]. This turns patellar tendinitis, injury
to the tendon connecting your kneecap (patella) to your shinbone,
a common problem in cycling with symptoms such as swelling,inflammation, and stiffness [2]. Furthermore, pain and discomfort
around knee joint is a frequently reported problem by cyclists; making it the second most common overuse injury type following backpain in cycling [2–4].
Although lateral deviation of patella during movement is known
to be a leading cause, current approaches to self analysis and assessment methods on knee movement consistently suffer from primitive problems such as high hardware requirement costs or counterintuitiveness in graph visualizations [5, 6]. Our ultimate goal withthis work is to minimize patellar tendinitis occurrences in cycling byproviding cost-effective and intuitive mixed/augmented reality feedback on one’s knee movement in the form of situated visualizations.
Towards this approach, we investigate the applicability of 3D knee
trajectory visualization. In this study, we aim to examine the validity* e-mail: oral.kaplan.nv4@is.naist.jp† e-mail: goshiro@is.naist.jp‡ e-mail: takafumi-t@is.naist.jp§ e-mail: y-yoshi@nifs-k.ac.jp¶ e-mail: plopski@is.naist.jp|| e-mail: sandor@is.naist.jp** e-mail: kato@is.naist.jp

of frequently reported problems as well as try to better understandthe requirements for developing these visualizations. To achieve
that, we captured knee movement data of amateur and professional
cyclists and orally interviewed each participant to identify problems
they face while utilizing two consistently used visualization methods. In this paper, we report the most frequently indicated problems
by participants and discuss how situated visualizations can play a
critical role in reaching a solution [7].
2 C APTURING K NEE M OVEMENT DATA
During our study, we have measured the data of seventeen amateurand nine professional cyclists. We collected informed consent anddata on physical measurements before experiments. We followed
orthodox bike fitting techniques to minimize fit related arguments [1].
We gathered knee movement data using a cycle ergometer (Wattbike)and six optical motion capture cameras (Optitrack Flex:V100R2).
We placed six reflective markers on subject’s lower body; two onknees, two on heels, and two on toes. We additionally placed onemarker to each pedal for extracting a reference plane to calculate
angular deviation in knee trajectories. Each experiment consisted offour phases; subject preparation, training, analysis and discussion.
Participants followed the same training regime after completing
necessary preparations such as warming up. We adjusted the training
difficulty based on each participant’s cycling experience and bodyweight.
Finally, we post-processed motion data to fill possible gaps andreject undesired reflections. We used this data to calculate lateralangular deviation in knee movement through the following process:1. Accumulate marker data for one-second which contains at least
one full knee movement in horizontal direction.
2. Calculate movement planes of pedals.
3. Use PCA on accumulated knee marker data and select theeigenvector with biggest eigenvalue.
4. Calculate the angular difference between pedal plane and selected eigenvector.
5. Repeat above steps until all data has been processed.
3 V ISUALIZATION R ESULTS
Most commonly used visualizations represent the angular deviationin the form of a line chart. Positive values in chart represent anoutwards movement trajectory compared to bicycle’s frame andnegative values mean knees are moving towards inside. In videobased visualizations, the trajectory of each knee is visualized in 3D;
mostly in a virtual reality environment (Fig. 2).
Despite their broad application spectrum, counter-intuitive nature of graph visualizations in realizing movement patters werefrequently indicated by both amateur and professional participantsFigure 3: Situated visualization concept of knee trajectories.

Figure 1: Comparison of lateral deviation in knee movement ofa professional and an amateur level cyclist where positive angles
correspond to outward trajectories and vice versa.

Figure 2: Single frame taken from a video where trajectories ofmarkers attached to knees visualized in 3D.

(Fig. 1). We consistently observed significant difficulties in theircomprehension regardless of participant’s cycling experience level.
Although efficiency in representing long-term progress was referenced as a major benefit, the level of their intuitiveness in real-time
applications was largely disputable. Professional cyclists especiallymentioned the negative effects of fatigue on comprehension as a
crucial problem; particularly during high intensity interval training
sessions.
Almost all participants favored video-based visualization of knee
trajectories over graph based approach for realizing their featured
knee movement (Fig. 2) given the maximum training time wasless than a minute. Lengthy process of reaching a conclusion andmaking judgments was stated as a drawback by both professional
and amateur level cyclists. Although participants had differences
in ideas regarding the correctness of knee movement and how its
trajectory should be, most agreed on the intuitiveness of a videobased visualization for self assessment.
Furthermore, participants were surprised to see that the videobased visualization displayed a result which contradicts with theirperceived motion. We believe this could be a niche area where
further studies might be required where mixed/augmented reality
technologies can reveal additional unique characteristics of humanperception and proprioception.
4 C ONCEPT OF S ITUATED V ISUALIZATION IN C YCLING
Based on the initial results we have obtained, we plan to investigate
the usefulness of situated visualizations in self-evaluation of knee
movement in cycling. One possible application we consider is anaugmented reality environment where lateral trajectory of knees arevisualized on videos taken from front view of cyclists (Fig. 3). We
think that this method has the potential to induce an increase in task
performance similar to augmented reality based maintenance andassembly systems where users can see their own hands.
Additionally, there is strong possibility that a situated visualization approach could have significantly positive effects on lowerextremity muscle memory. Motor learning can be more efficiently
facilitated through an increased number of correct repetitions overa prolonged time; leading to a shorter learning process. This could
also contribute to decreasing attention given to pedaling; allowing
cyclists to place their focus on other aspects of their ride or training
which might affect their safety.
5 C ONCLUSION
We have investigated the applicability of a 3D knee trajectory visualization for increasing awareness to movement patterns and avoiding
overuse injuries. In this study, we have tracked knee movement
during indoor cycling and examined users’ subjective opinions onfrequently used graph visualizations. Based on our study, we may
fairly conclude that unique problems arise when graph visualizations are employed in real-time training scenarios; such as fatigueinduced decline of comprehension. On the other hand, lack of aproper overview in video-based visualizations reduces their usefulness during long training scenarios. As was mentioned, our aim is
to design a situated visualization to be used for self analyzing knee
movement during cycling training. Since augmented reality can beused to provide intuitive feedback on actual and measured motion
differences, we believe situated knee trajectory visualizations has apossibility to significantly improve one’s own movement cognition.
We believe this would be a crucial step for reducing the probabilityof knee overuse injuries in cycling.
ACKNOWLEDGMENTS
This work was supported by the MIC/SCOPE #162107006.
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