Look who's talking!
We restructured last week, and this week we started working virtually on different parts of our project. It’s nice that most of our parts shipped really quickly, so I think we already have everything that we will need for the rest semester (hopefully). This week …
This week we met on Monday (03/23) to discuss what to do about the microphones and we finally ordered four of the cheap adafruit microphones as only one of our devices will display automatic mode. I worked on downloading the gesture dataset. We met on …
This week we began implementing based on our restructured plan. Neeti is working on the gesture classifier, Shrutika is working on modelling the physical platform animation with Simulink and Gauri is working on the control loop. We also ordered and received the mics. So we have all the parts we need to complete our project. The main changes we are making are with regards to animating a physical platform using Simulink instead of building a real one and rotating the animation based on the inputs received and processed by the RPis. We overall seem to be on track to demo-ing at least a mostly full version of manual mode.
Updated Risk Analysis:
We have identified several risk factors for our project. The first risk factor is related to the gesture detection input, taken by the Raspberry Pi Camera Module to classify left and right gestures, and forward them to the paired COMOVO. The risk here is that the accuracy rate of identifying the gestures depends on the size and quality of our dataset. Since we don’t have the resources to make our own datasets anymore, we are choosing an already available dataset and have changed the gestures to thumbs-up and thumbs-down for right and left. We plan to narrow the scope for the purpose of the demo to recognizing a gesture with a specific background (against a plain black/white background).
The second risk factor is the accuracy of the sound localization. We are planning for a very high accuracy rate of the COMOVO rotating towards the loudest speaker, and we have identified several risks regarding ambient sound, sound from the phone speaker and the accuracy of directional microphones. We plan to use hand-made baffles around each mic to ensure that too much ambient noise is not picked up. For the demo video we will demo in a quieter room so that there isn’t much distracting ambient noise.
The third risk factor is the potential for higher latency than is anticipated. Since our use case is for long distance video calls, our short distance testing might not accurately represent the latency for our original use case.
Finally, the performance of our device is heavily dependent on parameter tuning – the classifiers will need to be tuned to accurately recognize heads and hands and the baffled omnidirectional microphones’ sensitivities need to be sufficient to offset the short distance between them.
Updated Gantt chart:
This week we met on Monday to research what to do about the microphones and finally ordered 4 cheaper omnidirectional ones. We decided that making baffles around these would be a better idea than buying 4 super expensive cardioid singing mics. On Wednesday we met …
This week was our first week of virtual meetings. Definitely an adjustment, but I think we have a solid plan going forward. We’re definitely rushed for time since we lost a few weeks with restructuring project virtually and waiting for our parts to ship, but …
This was our first Zoom quarantine week! I think we managed quite well taking everything into account. This week was mostly us rehashing our project plan. We spent 10:30-12:30 on Monday and Wednesday during what would be our normal class time discussing what changes we …
This week we met on Monday (03/16) to float around ideas for how to transition our project to accommodate the new remote setup such as using an animation instead of a physical platform and motor as the output of our control loop. We then briefly met with Jens to get checked off and gain some clarity on the new expectations for our project and we learned that as long as we are able to prove that our software components are able to control the hardware components as promised, we do not need to actually get a hold of some of the hardware parts.
On Wednesday (03/18), we further dived into how we would transition the project and split up the work. We also came up with a list of questions to ask professor Sullivan in our meeting, about the general direction of the course, the expectations for the SoW document, as well as the availability of parts and TechSpark.
I spent some time researching guides, models, and datasets for our hand gesture classifier on Thursday (03/19).
On Friday, we met to discuss the next steps for the classifier and decided we will need to change the gestures to rotate the device in manual mode to fit existing hand gesture datasets. We also did some research on mics to use and after consulting professor Sullivan, we decided to go with cheaper omnidirectional microphones with baffles. Finally, we worked on creating our SoW document and will update it with a new Gantt chart and block diagram before we submit it this weekend.
It has been interesting transitioning to zoom meetings and I am hopeful that we can still create something cool that we are proud of! 🙂
We had our first zoom meeting on Monday (03/16)! We used the 10:30-12:30 lab time to begin to think about how we would transition our project to accommodate the remote nature of the class. We discussed which parts of the project we could keep the …
