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Innovative Uses

Accelerations in Snowboarding

I’ve often wondered about the magnitudes of accelerations as I ride my snowboard at Mt. Hood in Oregon. Last summer I did some experiments, and here is one data set from those trials. (And yes, I did say summer- we have snow year- round on the glaciers of Mt. Hood.) While this discussion is about snowboarding, it also illustrates how you can take data in the field for any experiment.

When making deep, carved turns on skis or snowboard there is a significant feeling of compression or weight at the sharpest point of the turn. In contrast, there is a sense of release or floating between turns, as the snowboard is moving from one edge to the other. It is that motion I decided to study. Riders speak of “pulling g’s” during the turns. How big is the g-factor? Let’s see.

To make these measurements I carried a Vernier 3-Axis Accelerometer, a Vernier LabPro with batteries, and a Texas Instruments TI-84 Plus calculator.

Although I ultimately wanted to work with the data on a computer, I used a calculator to control data collection and to display data after each run. Using the calculator gave me immediate feedback on the quality of data. The standard DataMate program is all that is needed to control data collection. The 3-Axis Accelerometer was just loose in the backpack, since I planned to use only the vector sum of the three axes to measure the net acceleration. I chose a 10 sample per second data rate. Next time I take snowboarding data, I’ll use 20 samples per second for more detail. The run extended for 60 seconds, which was enough time to start data collection, drop the gear into my backpack, and make some turns.

After viewing graphs of each run, I used the Save Experiment option in DataMate to safely store the data to the LabPro memory. This allowed me to store named, multiple runs to the flash memory on the LabPro, and then later transfer the data to Logger Pro 3 running on a computer. This little-known data storage function is useful in many situations, such as water quality field work or amusement park physics.

Upon returning to Portland I downloaded the runs to my computer. In Logger Pro I created a calculated column that is the square root of the sum of the squares of the individual acceleration values, giving me the net or scalar acceleration values. Scalar acceleration with an accelerometer corresponds to the perceived g-factor.

Any use of an accelerometer requires a short interpretation of the measurements. Because the accelerometer responds to both kinematic acceleration and the Earth’s gravitational field, the scalar “acceleration” is 9.8 m/s2 when the device is at rest. The measurement is really the Normal Force per Unit Mass, which we’ll call the g-factor for short. Kinesthetically, the g-factor corresponds to the compression one feels in the legs during snowboarding. You feel a g-factor of 9.8 m/s2 (or 1 g) when standing still.

Here is one run I collected, with narration of the time sequence of events:
0 – 15 s noise from putting on backpack, then standing still.
~16 s three bounces to mark start of run
20 – 30 s irregular turns through rough snow
30 – 60 s even deep turns. Each peak is the midpoint of a deep, carved turn.

These deep turns were very even and smooth, and the snow was freshly groomed and nearly untracked. Succeeding peaks are turns to the left, then right (or for you riders out there, toeside, then heelside…). At the peak times my body is low, legs bent, and the feeling is one of high compression or weight. Each minimum corresponds to the transition between turns. At this time my body was raised, legs somewhat extended, and a sense of lighter weight is felt. From these data, it looks like the turns generated sometimes more than 2.5 g, but at the time of edge transition small values show that there was very little force of the snow on my board. Sometimes my board leaves the snow between turns. That didn’t happen during this run, but I would expect the reading to go to zero during those moments, since I’d be in freefall. Maybe next time I’ll capture one of those events.

One thing I learned in taking these data is that the least bit of thrashing, rough snow, or unintended movements will result in large peaks that are as big as the peak accelerations due to smooth, even turns. Only the smoothest rides can easily be used for analysis.

The next step for another day in this study is to take a video of a rider making turns while carrying the accelerometer. Logger Pro allows the synchronized display of a video with sensor data, which helps students see exactly what movement is correlated with a particular acceleration. The only additional equipment needed is a basic digital video camera. Why not have your students try this?

John Gastineau, Staff Scientist, Vernier Software & Technology

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