As some of you may know from my last blogpost, I finally went to a day of testing. And when I say "a day of testing" I mean, quite literally, an entire day of data collection. The team originally planned to do both agility and metabolic testing this past Monday, but didn't even finish agility testing before the subject grew tired around 5pm. This is not to say we didn't have a successful venture, in fact, some graphs the team quickly developed indicated fantastic results. One of my favorite moments during testing was when I saw multiple members of the team really interact as a team--not just engineers, undergrads and biologists--but a group people giddy with results from hours of testing. In this post, I'm going to give you a rundown of how the five hours of testing played out, in detail.
Around 12:30 PM, Anthony (one of my advisors) and I, went to find a place to set up for the T-Test. In case you don't remember, a T-Test is set up as a T that has a 10 meter stick and 10 meter dash. We ended up in a hallway just around the Bio room we typically work from. Here, we also set up a laptop that receives data from the BiOM through a Wi-Fi signal. This is important for data collection and programming--because, remember, the BiOM works by reading a code, like a computer. While Anthony and I set up the testing area and cleared dozens of vials from the hall space (because, obviously, a Bio building can't go without being littered by vials), one of the engineering students on the project, Eric, helped the subject get fitted with the BiOM. This subject has vacuum pump attachment for the device to link up (this is the same subject I referenced in an earlier post). Anyway, we started testing with the WFH rather than the stock BiOM program, which we tested later. Initially, even after walking around on the BiOM, the subject wasn't performing at his best. After 10 solid tests, but 16 recorded (we sometimes can't use a test when there's a glitch in the run, this happened 6 times) we had multiple data points to consider. We had time, laptop counted tests (16), our counted tests (10) and the number of steps taken by the subject for each path. Path A was walking forwards to the intersection of the T's dash and stick, Path B was the sidestep 5 meters to the left, Path C was sidestepping 10 meters to the right, Path D was sidestepping 5 meters to the left back to the intersection of the dash and stick, and finally, Path E was the backward walk to the end of the stick. So, forward, side, side, side, backward. My job (at first) was counting and recording the steps taken in each path. Now, this may not seem so difficult, but it is an incredibly tricky maneuver, trust me. After we finished the trials for the WFH, we did trials for the stock BiOM. Trials for the stock BiOM were performed in the exact same way they were for the WFH. Except, this time, we had less glitches. So, laptop count was 12 trials and our count was 10 trials--(two problematic runs). After completing BiOM stock trials, we noticed that the times were significantly shorter than with the WFH. This was problematic because we didn't know if the time difference was due to more practice with the prosthesis by the time we had tested the stock BiOM or because the WFH was just less effective. Luckily, there was a simple yet time consuming solution: Re-test the WFH. After another hour of testing, we finished recording new data for the WFH. During this time, I switched roles to controlling the laptop. This was a much simpler job but it was also super cool to see the code provided to the BiOM. Also, please remember it takes about 30 minutes to switch from WFH to stock BiOM. From the new data, we discovered that times were much closer than before--good news! Typically, our times were about 30 seconds per T, but it's nearly impossible to compare those times to any previously results from other studies because the T-Test is usually used with athletes sprinting. But, from some simple calculations we can create a bubble number to compare the 30 seconds to. By knowing that the average walking speed of someone is 3.1 miles/hour, we can convert that to 1.39 meters/second (our units). We can then do a simple proportion to find how many seconds it would take the average human to walk 20 meters, which turns out to be around 15 seconds. But, because there is side stepping and backwards walking, we must add some time to make up for the differences in gait. So, we can safely say the average time of an able bodied human performing to the T-Test is roughly 15-20 seconds. This is great news because it means that while using a prostheses, our subject's time values are hardly off. After data collection of the WFH, the stock BiOM and the WFH again, the subject grew weary, which we could see through trends in the data. So, we were unable to test the passive device in fear of having inaccurate data. But, my advisor, Anthony, created a graph that showed five different steps having nearly the same torques with the WFH. This is fantastic because it shows consistency and proves that the WFH is effective. Additionally, the graph shows proper negative/positive torques when the foot is moving or is in plantar flexion/dorsiflexion. This is all so exciting.
Well, that was my first day of testing and I cannot wait for the many more to come!
Thank you for reading,
Pooja
Yay for the first day of testing! Whoop whoop!
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