# Title Team Members TA Documents Sponsor
32 A Wearable Device That Can Detect Mood
Junjie Ren
Kejun Wu
Peidong Yang
Xinzhuo Li
Said Mikki
# A Wearable Device That Can Detect Mood

**Team Members:**
- Junjie Ren [junjier2]
- Peidong Yang [peidong5]
- Xinzhuo Li [xinzhuo4]
- Kejun Wu [kejunwu2]

## Problem
Our project targets the pervasive impact of workplace stress, anxiety, and depression, recognizing these as critical challenges compromising individual well-being and overall productivity. Motivated by the need for proactive solutions, we aim to provide a wearable device equipped with advanced sensors and a unique mood recognition framework. By integrating psychological knowledge and wearable technology, our solution objectively monitors and manages mood-related challenges, offering timely feedback. The goal is to contribute to a healthier work environment, and our project represents a significant step at the intersection of technology and mental health in modern workplaces.

## Solution Overview

### Objective
The project aims to recognize and monitor the mood of employees in a workplace environment, leveraging wearable sensors and smartphone technology.

### Problem-Solving Approach
1. **Mood Recognition**: Using wearable sensors to collect physiological data that correlates with various mood states.
2. **Data Analysis**: Applying machine learning algorithms to interpret the physiological data and predict mood states.
3. **Feedback Mechanism**: Providing individual feedback to users and aggregated data to employers for well-being initiatives.

## Solution Components
1. **Wearable Sensor Subsystem**:
- **Components**: Practical sensors, like Toshiba Silmeeā„¢ Bar Type or W20/W21 wristbands.
- **Function**: Collects physiological data such as heart rate, skin temperature, and activity levels.
- **Role in Solution**: Provides the raw data necessary for mood prediction.

2. **Data Processing and Analysis Subsystem**:
- **Components**: Machine learning models (both personalized and generalized), feature extraction techniques.
- **Function**: Analyzes sensor data, extracts meaningful features, and applies machine learning techniques to predict mood.
- **Role in Solution**: Core of the mood recognition framework, turning data into actionable insights.

3. **Feedback and Reporting Subsystem**:
- **Components**: User interface for feedback, anonymized data aggregation for employers.
- **Function**: Provides mood predictions and wellness statistics to users and employers.
- **Role in Solution**: Closes the loop by informing users about their mood trends and assisting employers in enhancing workplace wellbeing.

## Criterion for Success

### Hardware Achievements
1. Successful deployment of advanced sensors, which are capable of collecting various physiological data such as heart rate, pulse rate, and skin temperature.
2. Integration of sensors into a wearable format that can be comfortably used in the working environment.
3. Clear and vivid display on the screen, indicating the detected mood.

### Software Achievements
1. Creation of a sophisticated mood recognition framework capable of identifying eight different types of moods at five intensity levels, with regular time updates.
2. Application of machine learning techniques for both personalized and generalized mood prediction models based on physiological data.
3. Achievement of a high average classification accuracy in mood prediction, showcasing the efficacy of the software algorithms.

## Distribution of Work
- Junjie Ren is responsible for System Design and Architecture.
- Peidong Yang is responsible for Data Collection and Analysis.
- Xinzhuo Li takes charge of Psychological Model Integration.
- Kejun Wu is responsible for User Study Coordination.

### Electrical Complexity
The project's electrical complexity encompasses integrating advanced sensors into the wearable device, demanding intricate signal processing algorithms, and robust coding for accurate mood interpretation. Ensuring seamless communication with the smartphone app adds complexity, along with implementing an efficient power management system for sustained monitoring.

### Mechanical Complexity
The mechanical intricacy involves designing a comfortable and durable wearable, accommodating integrated sensors while considering user ergonomics. The challenge lies in achieving a balance between functionality and aesthetics, ensuring the device is robust enough for daily wear and capable of withstanding various environmental factors. This complexity is justified by the need for a reliable, user-friendly solution contributing to mental health monitoring in professional settings.

Cloud-controlled quadcopter

Featured Project


To build a GPS-assisted, cloud-controlled quadcopter, for consumer-friendly aerial photography.


We will be building a quad from the frame up. The four motors will each have electronic speed controllers,to balance and handle control inputs received from an 8-bit microcontroller(AP),required for its flight. The firmware will be tweaked slightly to allow flight modes that our project specifically requires. A companion computer such as the Erle Brain will be connected to the AP and to the cloud(EC2). We will build a codebase for the flight controller to navigate the quad. This would involve sending messages as per the MAVLink spec for sUAS between the companion computer and the AP to poll sensor data , voltage information , etc. The companion computer will also talk to the cloud via a UDP port to receive requests and process them via our code. Users make requests for media capture via a phone app that talks to the cloud via an internet connection.

Why is it worth doing:

There is currently no consumer-friendly solution that provides or lets anyone capture aerial photographs of them/their family/a nearby event via a simple tap on a phone. In fact, present day off-the-shelf alternatives offer relatively expensive solutions that require owning and carrying bulky equipment such as the quads/remotes. Our idea allows for safe and responsible use of drones as our proposed solution is autonomous, has several safety features, is context aware(terrain information , no fly zones , NOTAMs , etc.) and integrates with the federal airspace seamlessly.

End Product:

Quads that are ready for the connected world and are capable to fly autonomously, from the user standpoint, and can perform maneuvers safely with a very simplistic UI for the common user. Specifically, quads which are deployed on user's demand, without the hassle of ownership.

Similar products and comparison:

Current solutions include RTF (ready to fly) quads such as the DJI Phantom and the Kickstarter project, Lily,that are heavily user-dependent or user-centric.The Phantom requires you to carry a bulky remote with multiple antennas. Moreover,the flight radius could be reduced by interference from nearby conditions.Lily requires the user to carry a tracking device on them. You can not have Lily shoot a subject that is not you. Lily can have a maximum altitude of 15 m above you and that is below the tree line,prone to crashes.

Our solution differs in several ways.Our solution intends to be location and/or event-centric. We propose that the users need not own quads and user can capture a moment with a phone.As long as any of the users are in the service area and the weather conditions are permissible, safety and knowledge of controlling the quad are all abstracted. The only question left to the user is what should be in the picture at a given time.