Team Status Report 4/25

The two remaining tasks before demo are wireless integration and to record more trials so the accuracy improves from 92% to 95%. Kat has been focusing on wrapping up the wireless integration since the demo, but we know the glove functions well with the cable connection. It’s possible the wireless implementation will add latency to the data classification, but the delay would be minimal due to the small amount of data we’re sending.  More data collection will only improve the model, so there are few risks with continuing.

Software unit testing: Tested the UI/UX with mock results for the entire character set and added buttons (shuffle set, reset, show signs) to improve the user experience.

Hardware unit testing (Teadora): confirmed connectivity/voltage from flex sensors into ADS with a multimeter. Confirmed I2C connectivity (with Kat) by receiving data on the console through cabled connection. Tested multiple conductive pad placements and attachment methods, and confirmed functionality using the suggested libraries for the breakout board and “touched” function.

Overall system test: Tested the full pipeline by collecting data from the pico when the frontend sends a request, sending the data to the model for evaluation, and posting the model’s evaluation to the frontend. We are finding that the accuracy drops significantly when testing the entire system which is what we are currently working to improve.

Teadora’s Status Report 4/25

This past week I worked on the final presentation and started the final poster. The remaining tasks before demo are to complete the wireless integration and record more data so the accuracy improves from 92% to 95%. We plan to meet Monday to review the poster, record more data trials, and hopefully record the final video. I will also start the final report soon.

Team’s Status Report 4/18

Currently the biggest challenges are with tuning the ML classification model. During the past week we’ve recorded trials to have training data for the model, but we aren’t currently meeting our live accuracy classification requirements. While we expect this will improve with more trials, it also seems likely that there’s bugs in the model-user interface integration.

The hardware integration is finished: the final board is soldered, the wiring is neat and correct, and the battery connection was attached. We’re still using the cable to power the system for right now, since we’re prioritizing getting the classification accuracy higher before integrating the wireless mode.

We adjusted the features the ML model is being trained on to more accurately distinguish the signs, which means we need to rerecord some of our data. Kat and Nia observed that the IMU rotation (pitch, roll, and heading) were helpful in distinguishing some signs. We also removed the translation mode as it would require exceptional model evaluation which we don’t have yet.

Teadora’s Status Report 4/18

This week I soldered the lightweight board we’re using for the final demo, then adjusted it after there were some errors. I recorded 33 trials for all 36 characters (1188 trials total) to add data to the ML model, contributing my part to the goal of 100 trials/character. I also wrote part of the final presentation slides and did the design work + copy-writing to communicate my progress since design review.

Weekly extra questions: As you’ve designed, implemented and debugged your project, what new tools or new knowledge did you find it necessary to learn to be able to accomplish these tasks? What learning strategies did you use to acquire this new knowledge?

A lot of my work this semester involved selecting hardware components to meet the design requirements, then learning how to use previously unfamiliar components. I gained a lot of experience reading and understanding datasheets. I worked a bit with Kat on the Pico integration, so I used online tutorials to brush up on I2C protocols and to understand the ML model Kat and Nia were implementing. One learning strategy I realized I needed to use more was repeatedly reading the datasheets, since the way some of the information was presented was unfamiliar to me and I didn’t always understand it the first time around.

 

Teadora’s Status Report 4/4

This week I added the capacitive touch sensors to the glove and presented at the interim demo. I was using conductive copper tape as the contacts for the glove, but I think it’s not a great long term solution since the adhesive can wear off and the copper gets crinkled. My solution is to embroider the glove with conductive thread to create a durable and flexible contact. My verification process involves confirming that the components work and can integrate effectively with the larger subsystems. Over the past few weeks I verified that the following components worked: flex sensors, IMU, ADS, capacitive touch sensor. First I confirmed that each component could be operated according to the manufacturer’s instructions by connecting it to a breadboard and measuring relevant information (ex: resistance, voltage with a multimeter). Then I connected it to the Pico and worked with Kat to make sure the signals were being received. Finally I attached the components to the glove and made sure signals were still accurate. For example, I first confirmed that the flex sensors worked as a voltage divider on a breadboard, then added it to a breadboard with the Pico. When I attached the flex sensors to the glove, the range of motion changed but data was still available, so I helped Kat calibrate the sensors to detect % finger bending.  I believe the IMU data will need more calibration since the Z axis is very sensitive. We will also verify the gloves functionality after seeing the ML training data output.

Teadora’s Status Report for 3/28

This week I assembled the glove by attaching the flex sensors and imu along with some cables. I also helped Kat with testing and calibration by wearing the glove. As I mentioned last week, we’re looking into using touch sensors to help identify some signs better. When I was deciding what parts to order, I chose the capacitive touch sensor breakout instead of pressure touch sensors like (https://www.adafruit.com/product/166).  The touch sensor breakout is built as an i2c board, which means it can integrate quickly with our existing setup. The pressure sensors would have needed  an ADC board and similar setup to the flex sensors, since both are variable resistors. We have enough funds, pins, and physical space to use an ADS, but I still chose the capacitive touch sensor because it seemed easier to directly set it up as binary touch/no touch readings, which is what I understood the design required instead of variable pressure. I also found that the size and shape of the easily available pressure sensors didn’t seem like it would work well with the glove — the circular pads were larger than a finger tip and seemed to get in the way, and the square ones were too small to work on the palm. With the capacitive touch sensors, I should be able to  tape out conductive regions with copper foil tape and detect when the finger makes contact with those regions. The foil tape arrived yesterday (Friday 3/27) so I’ll use that in testing this week. I feel like we’re on track with the project, assuming we can start to record data soon.

Team Status Report 3/28

The current risks are related to the time it takes to train the ML model. Touch sensors won’t affect the model since it will just be added as an additional feature so they will likely improve it and the ml model shouldn’t take more than a week. Training should take max 5 mins once we have data, so the main holdup really is getting data. We can improve the model post MVP by tuning training parameters

This past week we assembled a glove to make sure the flex sensor readings worked on the hand.

We added capacitive touch sensors to the system so we could better detect when fingers are touching each other or the palm, which is necessary to distinguish signs like U/V, M/N, etc. Teadora selected capacitive touch sensors instead of force sensing resistors and details the reasons for the decision in this week’s status report. There wasn’t a significant parts cost ($15 at most for conductive tape and a breakout board), but we will need to test the touch sensors and make sure the readings can transfer easily to the ML model. We will display our in progress glove with flex sensors and IMU at the interim demo.

Teadora’s Status Report 3/21

This week I attended the ethics lecture and confirmed backup parts orders. Last week I was concerned about how the voltage divider on the flex sensors would interact with the input impedance to the ADC, so I tested a TL072 op amp as buffer. It turned out this wasn’t necessary after I worked with Katherine to get the flex sensor calibration working on the build in Pico ADC pins. We also connected IMU to Pico with I2C and were able to read out data. Confirmed that the connections worked for battery power and microusb power. My part in this was measuring voltage, connecting the test setup, and referencing suggested designs in spec sheets. Tomorrow (Sunday 3/22) we’re going to assemble the glove and try to get preliminary data readings.

https://photos.app.goo.gl/PmM3M6UYRMjM3gnv5

https://photos.app.goo.gl/sXXgsgqtNRei6V7HA

Teadora’s Status Report 3/14

This week I worked on testing the flex sensors and IMU. Based on the resistance on the ADC pins on the RPico, I concluded we’ll need to use buffers between the voltage divider on the flex sensor and the ADC input pin. I made some orders for backup parts that will allow us to parallelize the work flow better. I think we’re on track. This coming week I’ll attach the IMU to the microcontroller and work with Kat to make sure we’re receiving data correctly, and hopefully we can start recording sign data at the end of next week.

Teadora’s Status Report for 3/8/2026

During the week before Spring Break, I worked on the design report. This required making some more decisions about the hardware subsystem, specifically the power supply. Unfortunately I had a very busy week leading up to the design report submission and was traveling that Friday, so we needed to ask for an extension to complete the report. I also worked with Quinn to resolve a miscommunication on parts ordering, which should be resolved now. I need to meet with Katherine this week and discuss the power system implementation since it seems we have different ideas about how it will work. Realistically I think we are behind schedule and I will need to do a lot more work between now and the interim demo to catch up. The tasks I need to complete this week are connecting the IMU and flex sensors to the RPico, and powering the RPico to ensure we can get signals out of it.