# Title Team Members TA Documents Sponsor
40 Automatic Intelligent Fishing Pod
Baiming Li
Xinyi Song
Yitong Gu
Ziyi Shen
Said Mikki
Our project, the SmartFish Pod, introduces a seamless fishing experience by integrating automation and AI technology. This device is an innovation in the recreational fishing industry, enhancing the traditional practice with modern technology.

General Description:
SmartFish Pod is a compact, intelligent fishing assistant that automates baiting, bite detection, and rod lifting. It employs cameras and sensors, coupled with machine vision, to not only detect activity but also identify fish species and analyze the environment.

The project's uniqueness lies in its autonomous operation, offering a hands-free fishing solution. Unlike inventions that create new methods, this innovation refines and elevates an existing practice. It's particularly distinctive due to its species identification capabilities, which none of the current fishing aids offer.

The current market offers basic electronic bite alarms and rod holders, which reduce but do not eliminate manual involvement. SmartFish Pod's full automation and environmental assessment features are novel, positioning it ahead of competitors in terms of technology integration and user experience.

Technical Overview:
The pod's mechanics are designed for ease of use, featuring an automatic baiting system and a responsive rod lifting mechanism. Its digital brain utilizes a robust AI algorithm trained on a multitude of data to recognize species and predict bites. This system is connected to a user-friendly interface that informs the angler of real-time conditions and statistics, making fishing accessible and educational for enthusiasts at all levels.

An Intelligent Assistant Using Sign Language

Qianzhong Chen, Howie Liu, Haina Lou, Yike Zhou

Featured Project


Qianzhong Chen (qc19)

Hanwen Liu (hanwenl4)

Haina Lou (hainal2)

Yike Zhou (yikez3)


An Intelligent Assistant Using Sign Language


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.


## 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.


- 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.


- 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.