Final Presentation

Description

Presentations of the projects are given a few days after the Final Demo to an audience of fellow student reviewers, the lab instructors, and occasionally faculty or even students from outside the class who are following up a project of personal interest to them. The style is formal and professional, and students should dress accordingly.

Requirements and Grading

Each project team has 25 minutes for a Powerpoint presentation and questions. Every group member must present their own work contributing to the project and be ready to answer questions. Individual grades are given, and everyone in the audience participates in evaluating the presentation. Talks are judged on the basis of presentation technique and of technical organization and content.

Points of technique include dress, use of display materials and their design for readability, clarity of speech, absence of annoying mannerisms, proper eye contact with audience and smooth transitions between speakers. Content is judged on use of a proper introduction, orderly and connected development of ideas, absence of unnecessary details, proper pacing to stay within the allotted time, and an adequate summary at the close of the talk. Quantitative results are expected whenever applicable. Here is a general outline to follow:

  1. Introduction
  2. Objective
  3. Review of original design, requirements, and verifications
  4. Description of project build and functional tests
  5. Discussion of successes and challenges, as well as explanations of any failed verifications demonstrating and understanding of the engineering reason behind the failure
  6. Details of other tests including tests not explicitly required for verification procedures
  7. Recommendations for further work

Any significant relevant ethical issues should be briefly addressed, preferably in a single slide.

Presentations will be graded using the presentation grading rubric. Two sample Presentation documents - with notes at the top - are available at: Sample PRES 1, Sample PRES 2

Here are some recent presentations you can refer to: FA20_Team13, FA20_Team3

Submission and Deadlines

Slides for your final presentation must be uploaded to your project page on PACE prior to your presentation time. Deadlines for signing up may be found on the Calendar. Sign-up for the final presentation is done through PACE. Remember to sign up for a peer review of another group.

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.