Project

# Title Team Members TA Documents Sponsor
32 A Wearable Device That Can Detect Mood
 Junjie Ren
Kejun Wu
Peidong Yang
Xinzhuo Li
 design_document1.pdf
design_document2.pdf
proposal1.pdf
proposal2.pdf
 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 Silmee™ 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.

Electronic Automatic Transmission for Bicycle

Featured Project

Tianqi Liu(tliu51)

Ruijie Qi(rqi2)

Xingkai Zhou(xzhou40)

Sometimes bikers might not which gear is the optimal one to select. Bicycle changes gears by pulling or releasing a steel cable mechanically. We could potentially automate gear changing by hooking up a servo motor to the gear cable. We could calculate the optimal gear under current condition by using several sensors: two hall effect sensors, one sensing cadence from the paddle and the other one sensing the overall speed from the wheel, we could also use pressure sensors on the paddle to determine how hard the biker is paddling. With these sensors, it would be sufficient enough for use detect different terrains since the biker tend to go slower and pedal slower for uphill or go faster and pedal faster for downhill. With all these information from the sensors, we could definitely find out the optimal gear electronically. We plan to take care of the shifting of rear derailleur, if we have more time we may consider modifying the front as well.

Besides shifting automatically, we plan to add a manual mode to our project as well. With manual mode activated, the rider could override the automatic system and select the gear on its own.

We found out another group did electronic bicycle shifting in Spring 2016, but they didn't have a automatic function and didn't have the sensor set-up like ours. Commercially, both SRAM and SHIMANO have electronic shifting products, but these products integrate the servo motor inside the derailleurs, and they have a price tag over $1000. Only professionals or rich enthusiasts can have a hand on them. As our system could potentially serve as an add-on device to all bicycles with gears, it would be much cheaper.