Team’s Status Report 3/14

This week, Kat did the processing for the Raspberry Pi Pico to receive analog information and send it to the machine learning model. Nia generated a synthetic dataset that she ran the neural network with. Teadora tested the flex sensors and will be continuing to evaluate them and the IMU with the Raspberry Pi Pico this upcoming week.

This week we will make sure we can receive the analog signals properly from the IMU and flex sensors. We will also continue working on the machine learning model and optimize it for the types of data we will be using. We also will complete our work on the data organization such as how the data goes from when it first arrives to on the computer to the machine learning model.

Katherine’s Status Report 3/14

This week I finished the wired connection between the Raspberry Pi Pico so we can read values from the analog signals. Once the ADC adapter comes in we will be able to connect all of the analog signals at once. I plan on finishing the bluetooth processing tomorrow so we can focus on the machine learning model for the rest of the week and time.

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.

Nia’s Status Report 3/14

This week, I fully implemented the Practice page within the existing React/Vite localhost framework. Currently, the practice mode cycles through all 26 ASL letter signs (A–Z), evaluating each response as correct or incorrect. For testing purposes, a 1-in-4 probability of an incorrect answer is simulated. Upon completing all 26 characters, the user is presented with a results summary screen displaying their overall performance.

For the machine learning subsystem, I built and validated the data generation and training pipeline  The pipeline currently generates a synthetic dataset and feeds it through a neural network classifier. I initially had an issue where the output layer was hardcoded to 5 classes while the dataset contained 36, causing the model to perform at random chance  with ~5% accuracy. After fixing the output layer to dynamically match the number of classes in the dataset, the model achieved 100% accuracy on the synthetic data. This is expected as the synthetic data is generated from a distinct random base vector with minimal noise. This result confirms that the pipeline is functioning correctly and is ready to be evaluated against real sensor data.

Next week, I plan to design and implement the transcription mode. I will also begin forming the actual training dataset from collected data points. Once an initial set of real samples is gathered, I will apply data augmentation techniques to artificially expand the training set. This is a standard approach for sensor datasets where collection time is limited and will allow us to increase dataset size significantly without requiring more physical recording sessions. If time permits, I will also set up a REST API endpoint that exposes the model’s predictions to the frontend.

Team’s Status Report 3/7

This week we submitted the design report, which required making more decisions about the power supply part of the hardware subsystem. There seems to be some discussion of which battery we will use. Teadora’s proposal is a standard 9V battery with a step down converter to power the RPico and IMU at 5V. Katherine’s proposal involves a 3.7V lithium ion battery to power the RPi Pico and IMU. 

This upcoming week we need to test and potentially make some revisions to our design now that our parts have arrived. We will test the parts with what we have so far and determine if we need to make adjustments or not. We also need to order a part to add ADC pins to our RPi Pico, but Katherine has already found the part/s that we can use for that. 

Additional questions:

Section A was written by Katherine

Global factors to be considered for our project are for people without access to computers or technology. This is our main global consideration as our product is geared towards people who want to learn, however it would be hard for someone who does not have a computer since our product would work with a computer in pairing. As of right now, our design is dependent on a local machine to run our algorithm and website. As our product is a prototype we are expecting that it would be significantly more accessible with our algorithm and website uploaded to AWS Cloud. Therefore it would be as simple as having the URL, a computer and the glove. However, as our project specifically interfaces with a computer to display results it would change our product greatly to make it accessible to anyone who does not have one.

 Section B was written by Teadora 

The cultural factors we are considering are beliefs around disability and interpersonal connection. Historically, people with disabilities have been excluded from public life, whether intentionally or through a lack of accessible options. When we were researching existing ASL sensing gloves, a lot of the options focused on recognizing signs and reading them out loud to reduce the need for an interpreter. These design decisions reinforce a cultural belief that speaking out loud is a better form of communication. However, they don’t respond to the reality that sign languages are their own complete languages, separate from spoken language. As a note from the Virginian community college Germana states, “The sentence “I see a big orange cat” would be signed as follows:CAT, ORANGE, BIG, I SEE [1]. In situations where quick and accurate communication with someone who uses ASL is needed, the existing gloves won’t suffice, and in some ways continue to exclude the deaf community from public life. Our design is based in the belief that learning ASL is a better solution because it connects with an existing language and culture. Our project emphasizes specific users: people who are interested in learning ASL, perhaps to communicate with friends or because they’re losing some hearing themselves. Another cultural factor is that ASL has started to increase in cultural capital. It’s seen as impressive for someone to know ASL, and this is emphasized by the inclusion of ASL interpreters in popular entertainment, like concerts and TV shows. Our design recognizes that communicating in ASL is increasingly necessary and desirable, not just to the deaf community but to the broader American public. 

 Section C was written by Nia

The environmental factors for our device mainly relate to the materials used in the hardware and the energy consumption of the device. Because our design uses electronic components such as the Raspberry Pi Pico, an IMU sensor, wiring, and a battery power supply, it contributes to the broader issue of electronic waste. Electronic devices often contain metals, plastics, and batteries that can be harmful to the environment if they are not disposed of properly. When selecting components for our prototype, we considered using available and reusable parts so that they can be repurposed in other projects rather than immediately discarded. 

Another environmental consideration involves the type of battery used to power the system. The team discussed using either a standard 9V battery with or a 3.3V lithium-ion battery. While 9V batteries are easy to obtain, they are often disposable and may contribute more waste if replaced frequently. Rechargeable lithium-ion batteries, on the other hand, can be reused many times and generally produce less battery waste over the lifetime of the device. However, lithium-ion batteries must also be handled and recycled properly because they contain materials that can be hazardous if they end up in landfills [2].

[1] “Provided by ASL Grammar Guide The Academic Center for Excellence 1 ASL Grammar Guide,” 2023. Available: https://germanna.edu/sites/default/files/2023-07/ASL%20Grammar%20Guide%20%28edit%207-24-23%29.pdf

[2] Vermont Department of Environmental Conservation. Lithium-Based Battery Management Fact Sheet. 2020. Available: https://dec.vermont.gov/sites/dec/files/wmp/SolidWaste/Documents/lithium-basedBatteryManagementFactSheet.pdf

 

Katherine’s Status Report 3/7

This week I worked on the design document and researching the machine learning model. I have begun with the serial processing code however I need the microcontroller to test it. As the microcontroller arrived this week I will be able to work on that and see how it works. I made some revisions to our design as we do not have enough pins on our microcontroller for our analog signals. Therefore I am ordering an ADS that will give us extra channels for our analog signals, I will discuss with my team on Monday before placing the order. I found one that adds 4-channels and that was original plan, however as I do research I discovered that a lot of people end up using touch sensors so I am wondering if implementing an 8 extra ADC pins would be needed.

Nia’s Status Report 3/7

This week, I worked on our design report. I worked on the introduction, design requirements, system implementation for the processing/machine learning subsystem and the software/user interface subsystem, testing/verification, and some portions of project management. As of now, our main concern is begin able to collect data for the machine learning training. We won’t be able to do so until we have built a MVP of our hardware system. I also created a design of our intended UI for teach mode using Figma Make

I plan to implement the frontend during the next couple of weeks. Once we have a working hardware system, I will assist with data collection and processing.

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.

Team Status Report for 2/21

The biggest risk we are facing right now is our parts not arriving on time. We are behind schedule if they do not come this week as our plan was to assemble the glove and begin testing. However, without hardware we are still making progress working on the communication between Raspberry Pi and software as well as working on the user-interface. This week we will finish the Design document and assemble the glove if our parts arrive. We will also continue implementing the software portions that we do not need hardware for.

 

This week we also formalized parts of the design for the design report, like picking which protocols we’ll use for MVP and final. The glove is based on Raspberry Pi and will use C for the controls. The IMU will use I2C to communicate with the Raspberry Pi. The flex resistors will communicate with the Raspberry Pi through its GPIO, possibly with capacitors to filter out noise. For MVP, the glove will communicate with the computer via USB, but we’d like to do wireless communication for final. We’re considering adjusting the weight requirements based on the batteries we’ve been able to find: if we can’t find a light enough battery to make the glove easy to use, we’ll need to switch to powering the glove with a USB cable or similar, but make the cable long so it’s still ergonomic.

Nia’s Status Report 2/21

This week, I worked on our design presentation slides and began creating the file structure for our code. We will be adding code for the frontend and machine learning portions as we wait for our parts to arrive next week.

I will also begin designing the UI on figma. We plan to have a test mode to begin where the user is shown a letter/number and they are to sign the letter and receive feedback (correct/incorrect).