Meeting with Your TA

Description

By the Thursday of the third week, you must have a project approved, and should be ready to get working! At this time, you'll need to log into PACE and submit your schedule for the semester. Please be sure to make this as accurate as possible because once it's submitted, it can only be changed manually. Making a block of your schedule red means that you are unavailable during that time.

Once each person on your team has submitted their schedule, your TA will be able to easily check for available times to schedule a weekly meeting. Your TA should contact you, usually by the fourth week, via email, to set up a weekly meeting schedule at mutual convenience. During the first weekly meeting, your TA will assign your team a locker and a lab kit.

Weekly meetings with your TA are required and will be held throughout the entire semester until demonstrations are completed. Your TA is your project manager. The "homework" of the course consists of preparing for the weekly meetings. Your TA will evaluate your lab notebook each week, provide feedback, and recommend improvements. At each meeting you will be expected to present your progress since your last meeting, plans for the coming week, and any technical or administrative questions you need to discuss with your TA. You are expected to arrive on time and prepared to make good use of your time with your TA. Your TA may require that each team member to fill out the Progress Report Template and submit it to them prior to each weekly meeting.

Requirements and Grading

Attendance and participation in weekly meetings is required and will affect Teamwork and Lab Notebook scores. If you can't make it to a particular weekly meeting, it is your responsibility to inform your TA prior to the meeting time and set up an alternate time.

Submission and Deadlines

Your schedule must be submitted by the end of the third week of class and you will receive an email from your TA shortly after. Your first meeting with your TA should be during the fourth week of the semester.

An Intelligent Assistant Using Sign Language

Qianzhong Chen, Howie Liu, Haina Lou, Yike Zhou

Featured Project

# TEAM MEMBERS

Qianzhong Chen (qc19)

Hanwen Liu (hanwenl4)

Haina Lou (hainal2)

Yike Zhou (yikez3)

# TITLE OF THE PROJECT

An Intelligent Assistant Using Sign Language

# PROBLEM & SOLUTION OVERVIEW

Recently, smart home accessories are more and more common in people's home. A center, which is usually a speaker with voice user interface, is needed to control private smart home accessories. But a interactive speaker may not be the most ideal for people who are hard to speak or hear. Therefore, we aim to develop a intelligent assistant using sign language, which can understand sign languages, interact with people, and act as a real assistant.

# SOLUTION COMPONENTS

## Subsystem1: 12-Degree-of-Freedom Bionic Hand System

- Two moveable joints every finger driven by 5-V servo motors

- The main parts of the hand manufactured with 3D printing

- The bionic hand is fixed on a 2-DOF electrical platform

- All of the servo motors controlled by PWM signals transmitted by STM32 micro controller

## Subsystem2: The Control System

- The controlling system consists of embedded system modules including the microcontroller, high performance edge computing platform which will be used to run dynamic gesture recognition model and more than 20 motors which can control the delicate movement of our bionic hand. It also requires a high-precision camera to capture the hand gesture of users.

## Subsystem3: Dynamic Gesture Recognition System

- A external camera capturing the shape, appearance, and motion of objective hands

- A pre-trained model to help other subsystems to figure out the meaning behind the sign language. To be more specific, at the step of objects detection, we intended to adopt YOLO algorithm as well as Mediapipe, a machine learning framework developed by Google to recognize different sign language efficiently. Considering the characteristic of dynamic gesture, we also hope to adopt 3D-CNN and RNN to build our models to better fit in the spatio-temporal features.

# CRITERION OF SUCCESS

- The bionic hand can move free and fluently as designed, all of the 12 DOFs fulfilled. The movement of single joint of the finger does not interrupt or be interrupted by other movements. The durability and reliability of the bionic hand is achieved.

- The controlling system needs to be reliable and outputs stable PWM signals to motors. The edge computing platform we choose should have high performance when running the dynamic gesture recognition model.

- Our machine could recognize different sign language immediately and react with corresponding gestures without obvious delay.

# DISTRIBUTION OF WORK

- Qianzhong Chen(ME): Mechanical design and manufacture the bionic hand; tune the linking between motors and mechanical parts; work with Haina to program on STM32 to generate PWM signals and drive motors.

- Hanwen Liu(CompE): Record gesture clips to collect enough data; test camera modules; draft reports; make schedules.

- Haina Lou(EE): Implement the embedded controlling System; program the microcontroller, AI embedded edge computing module and implement serial communication.

- Yike Zhou(EE): Accomplish object detection subsystem; Build and train the machine learning models.